├── .github └── workflows │ └── python-app.yml ├── .gitmodules ├── Dockerfile ├── LICENSE ├── README.md ├── dissemination ├── ABPHM_2023.pdf ├── ECCMID_2022_poster_VMixao.pdf └── README ├── examples ├── Listeria │ ├── input │ │ ├── Listeria_alleles.tsv │ │ ├── Listeria_metadata.tsv │ │ └── Listeria_nomenclature.tsv │ └── output │ │ ├── Lm.log │ │ ├── Lm.nwk │ │ ├── Lm_SAMPLES_OF_INTEREST_partitions_summary.tsv │ │ ├── Lm_clusterComposition.tsv │ │ ├── Lm_dist.tsv │ │ ├── Lm_dist_grapetree.tsv │ │ ├── Lm_dist_hamming.tsv │ │ ├── Lm_flt_samples_matrix.tsv │ │ ├── Lm_loci_report.tsv │ │ ├── Lm_metadata_w_partitions.tsv │ │ ├── Lm_nomenclature_changes.tsv │ │ ├── Lm_nomenclature_code_2023-03-28_summary.tsv │ │ ├── Lm_partitions.tsv │ │ ├── Lm_partitions_summary.tsv │ │ └── Lm_redundantSamples.txt ├── Neisseria │ ├── input │ │ ├── Allelic_profile_matrix_MScgMLST_822_loci_3791_isolates_NEW.tab │ │ ├── NG_Metadata_NEW.tsv │ │ └── NG_nomenclature.tsv │ └── output │ │ ├── NG_822.log │ │ ├── NG_822.nwk │ │ ├── NG_822_clusterComposition.tsv │ │ ├── NG_822_country_summary.tsv │ │ ├── NG_822_dist.tsv │ │ ├── NG_822_metadata_w_partitions.tsv │ │ ├── NG_822_metrics.tsv │ │ ├── NG_822_nomenclature_changes.tsv │ │ ├── NG_822_partitions_summary.tsv │ │ ├── NG_822_redundantSamples.txt │ │ └── NG_822_stableRegions.tsv ├── SARS-CoV-2 │ ├── input │ │ ├── nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk │ │ └── nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.tsv │ └── output │ │ ├── example1 │ │ ├── ReporTree_ncov_PT_lineage_clade_overview.log │ │ ├── ReporTree_ncov_PT_lineage_clade_overview_Health_region_summary.tsv │ │ ├── ReporTree_ncov_PT_lineage_clade_overview_clade_membership_iso_week_count_matrix.tsv │ │ ├── ReporTree_ncov_PT_lineage_clade_overview_clade_membership_iso_week_freq_matrix.tsv │ │ ├── ReporTree_ncov_PT_lineage_clade_overview_clade_membership_summary.tsv │ │ ├── ReporTree_ncov_PT_lineage_clade_overview_lineage_iso_week_count_matrix.tsv │ │ ├── ReporTree_ncov_PT_lineage_clade_overview_lineage_iso_week_freq_matrix.tsv │ │ ├── ReporTree_ncov_PT_lineage_clade_overview_lineage_summary.tsv │ │ └── ReporTree_ncov_PT_lineage_clade_overview_metadata_w_partitions.tsv │ │ ├── example2 │ │ ├── ReporTree_ncov_PT_lineage_clade_reg_freq.log │ │ ├── ReporTree_ncov_PT_lineage_clade_reg_freq_Health_region_iso_week_count_matrix.tsv │ │ ├── ReporTree_ncov_PT_lineage_clade_reg_freq_Health_region_summary.tsv │ │ ├── ReporTree_ncov_PT_lineage_clade_reg_freq_clade_membership_Health_region_iso_week_count_matrix.tsv │ │ ├── ReporTree_ncov_PT_lineage_clade_reg_freq_clade_membership_Health_region_iso_week_freq_matrix.tsv │ │ ├── ReporTree_ncov_PT_lineage_clade_reg_freq_iso_week_summary.tsv │ │ ├── ReporTree_ncov_PT_lineage_clade_reg_freq_lineage_Health_region_iso_week_count_matrix.tsv │ │ ├── ReporTree_ncov_PT_lineage_clade_reg_freq_lineage_Health_region_iso_week_freq_matrix.tsv │ │ └── ReporTree_ncov_PT_lineage_clade_reg_freq_metadata_w_partitions.tsv │ │ └── example3 │ │ ├── ReporTree_ncov_PT_get_clusters.log │ │ ├── ReporTree_ncov_PT_get_clusters_S1_mutations_summary.tsv │ │ ├── ReporTree_ncov_PT_get_clusters_clusterComposition.tsv │ │ ├── ReporTree_ncov_PT_get_clusters_metadata_w_partitions.tsv │ │ ├── ReporTree_ncov_PT_get_clusters_partitions.tsv │ │ └── ReporTree_ncov_PT_get_clusters_partitions_summary.tsv ├── SARS-CoV-2_lineage │ ├── input │ │ ├── README.md │ │ └── metadata.txt │ └── output │ │ ├── SARS-CoV-2_XBB.1.16.1.log │ │ ├── SARS-CoV-2_XBB.1.16.1_clusterComposition.tsv │ │ ├── SARS-CoV-2_XBB.1.16.1_country_summary.tsv │ │ ├── SARS-CoV-2_XBB.1.16.1_iso_week_summary.tsv │ │ ├── SARS-CoV-2_XBB.1.16.1_metadata_w_partitions.tsv │ │ ├── SARS-CoV-2_XBB.1.16.1_pango_lineage_usher_country_iso_week_count_matrix.tsv │ │ ├── SARS-CoV-2_XBB.1.16.1_pango_lineage_usher_summary.tsv │ │ └── SARS-CoV-2_XBB.1.16.1_partitions_summary.tsv ├── SARS-CoV-2_recent_samples │ ├── input │ │ ├── README.md │ │ └── metadata.txt │ └── output │ │ ├── SARS-CoV-2_last2months.log │ │ ├── SARS-CoV-2_last2months_clusterComposition.tsv │ │ ├── SARS-CoV-2_last2months_country_summary.tsv │ │ ├── SARS-CoV-2_last2months_iso_week_summary.tsv │ │ ├── SARS-CoV-2_last2months_metadata_w_partitions.tsv │ │ ├── SARS-CoV-2_last2months_pango_lineage_usher_country_iso_week_count_matrix.tsv │ │ ├── SARS-CoV-2_last2months_pango_lineage_usher_summary.tsv │ │ └── SARS-CoV-2_last2months_partitions_summary.tsv └── readme_examples ├── reportree.py ├── reportree_env.yml ├── reportree_env_osx.yml ├── reportree_logo.png ├── scripts ├── alignment_processing.py ├── metadata_report.py ├── newick4reportree.py ├── partitioning_HC.py ├── partitioning_grapetree.py └── partitioning_treecluster.py ├── tests ├── HC_test.py ├── alignment.fas ├── alignment_test.py ├── allele_matrix.tsv ├── grapetree_test.py ├── metadata.tsv ├── nomenclature.tsv ├── pos_list.tsv ├── tree.nwk └── treecluster_test.py └── useful_loci_lists ├── README.md ├── cgMLST_Campylobacter_jejuni_chewie-NS.txt ├── cgMLST_Escherichia_coli_chewie-NS.txt └── cgMLST_Salmonella_enterica_chewie-NS.txt /.github/workflows/python-app.yml: -------------------------------------------------------------------------------- 1 | # This workflow will install Python dependencies, run tests and lint with a single version of Python 2 | # For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python 3 | 4 | name: Test Reportree 5 | 6 | on: 7 | push: 8 | branches: [ "main" ] 9 | paths-ignore: 10 | - '**/README.md' 11 | pull_request: 12 | branches: [ "main" ] 13 | paths-ignore: 14 | - '**/README.md' 15 | 16 | permissions: 17 | contents: read 18 | 19 | jobs: 20 | build: 21 | 22 | runs-on: ubuntu-latest 23 | 24 | defaults: 25 | run: 26 | shell: bash -l {0} 27 | 28 | steps: 29 | - name: Checkout main module 30 | uses: actions/checkout@v3 31 | with: 32 | submodules: 'true' 33 | - name: Set up Conda 34 | uses: mamba-org/setup-micromamba@v1 35 | with: 36 | micromamba-version: '1.5.6-0' 37 | environment-file: reportree_env.yml 38 | init-shell: >- 39 | bash 40 | cache-environment: true 41 | - name: Test with pytest 42 | run: | 43 | pytest 44 | 45 | -------------------------------------------------------------------------------- /.gitmodules: -------------------------------------------------------------------------------- 1 | [submodule "scripts/GrapeTree"] 2 | path = scripts/GrapeTree 3 | url = https://github.com/insapathogenomics/GrapeTree 4 | [submodule "scripts/ComparingPartitions"] 5 | path = scripts/ComparingPartitions 6 | url = https://github.com/insapathogenomics/ComparingPartitions 7 | [submodule "scripts/vcf2mst"] 8 | path = scripts/vcf2mst 9 | url = https://github.com/genpat-it/vcf2mst.git 10 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | FROM conda/miniconda3 2 | 3 | WORKDIR /app 4 | 5 | RUN conda install -c etetoolkit -c anaconda -c bioconda -c conda-forge -c defaults biopython=1.77 cgmlst-dists=0.4.0 ete3 grapetree=2.1 numba=0.55.1 numpy=1.19.2 numpy-base=1.19.2 pandas=1.1.3 python=3.8 scikit-learn snp-sites=2.5.1 treecluster=1.0.3 zip pytest git --yes 6 | 7 | RUN git clone -b v2.5.4 https://github.com/insapathogenomics/ReporTree && chmod 755 ReporTree/reportree.py && chmod 755 ReporTree/scripts/partitioning_grapetree.py && chmod 755 ReporTree/scripts/partitioning_HC.py && chmod 755 ReporTree/scripts/partitioning_treecluster.py && chmod 755 ReporTree/scripts/alignment_processing.py && chmod 755 ReporTree/scripts/metadata_report.py && cd ReporTree/scripts/ && git clone https://github.com/insapathogenomics/ComparingPartitions.git && git clone https://github.com/insapathogenomics/GrapeTree.git && git clone https://github.com/insapathogenomics/vcf2mst.git && cd ComparingPartitions && chmod 755 comparing_partitions_v2.py 8 | 9 | ENV PATH="/app/ReporTree:/app/ReporTree/scripts:/app/ReporTree/scripts/ComparingPartitions:${PATH}" 10 | -------------------------------------------------------------------------------- /dissemination/ABPHM_2023.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/insapathogenomics/ReporTree/d0e1d7526905a332b49de68ea3b7b7021159f91d/dissemination/ABPHM_2023.pdf -------------------------------------------------------------------------------- /dissemination/ECCMID_2022_poster_VMixao.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/insapathogenomics/ReporTree/d0e1d7526905a332b49de68ea3b7b7021159f91d/dissemination/ECCMID_2022_poster_VMixao.pdf -------------------------------------------------------------------------------- /dissemination/README: -------------------------------------------------------------------------------- 1 | This directory contains the dissemination material associated to ReporTree 2 | -------------------------------------------------------------------------------- /examples/Listeria/output/Lm.log: -------------------------------------------------------------------------------- 1 | 2 | ******************** running reportree.py ******************** 3 | 4 | version 2.0.0 last updated on 2023-03-28 5 | 6 | /Users/veronicamixao/Desktop/reportree/ReporTree/reportree.py -m input/Listeria_metadata.tsv -a input/Listeria_alleles.tsv --loci-called 0.95 --columns_summary_report country,n_country,source -out output/Lm --analysis grapetree --partitions2report 4,7,15 --nomenclature-code 150,7,4,country --nomenclature-file input/Listeria_nomenclature.tsv --sample_of_interest sample_0269,sample_0675,sample_0010 7 | start: 2023-03-28 15:47:51.526501 8 | 9 | Profiles file provided -> will run partitioning_grapetree.py: 10 | 11 | 12 | -------------------- partitioning_grapetree.py -------------------- 13 | 14 | version 1.2.0 last updated on 2023-03-28 15 | 16 | /Users/veronicamixao/Desktop/reportree/ReporTree/scripts/partitioning_grapetree.py -a input/Listeria_alleles.tsv -o output/Lm --method MSTreeV2 --missing 0 --n_proc 5 -thr max -d 1.0 --site-inclusion 0.0 -pct_thr none --loci-called 0.95 --missing-code 0 17 | 18 | start: 2023-03-28 15:47:52.120045 19 | Cleaning the profile matrix using a threshold of >0.95 alleles/positions called... 20 | From the 865 samples, 861 were kept in the profile matrix. 21 | Getting the pairwise distance matrix with cgmlst-dists (if your profile matrix is too big, this will be done in chunks of 2000 alleles/positions)... 22 | Running GrapeTree... 23 | /Users/veronicamixao/miniconda3/envs/reportree2/bin/python /Users/veronicamixao/Desktop/reportree/ReporTree/scripts/GrapeTree/grapetree.py -p output/Lm_flt_samples_matrix.tsv -m MSTreeV2 -o output/Lm --missing 0 --n_proc 5 24 | 25 | Processing clustering threshold... 26 | Getting redundant sample information... 27 | Defining clusters... 28 | Calculating clustering in range 0 1729 with a distance of 1.0 29 | Creating cluster composition file... 30 | Creating sample partitions file... 31 | 32 | partitioning_grapetree.py is done! 33 | 34 | End: 2023-03-28 15:48:31.604519 35 | Time elapsed: 0:00:39.484474 36 | 37 | Nomenclature file provided. Will work on cluster names... 38 | 39 | Generating nomenclature code... 40 | The nomenclature code has 3 partition levels and 1 metadata variable. 41 | 42 | -------------------- metadata_report.py -------------------- 43 | 44 | version 1.0.2 last updated on 2023-03-28 45 | 46 | /Users/veronicamixao/Desktop/reportree/ReporTree/scripts/metadata_report.py -m output/Lm_metadata_w_partitions.tsv -p output/Lm_partitions.tsv -o output/Lm --columns_summary_report country,n_country,source --partitions2report MST-4x1.0,MST-7x1.0,MST-15x1.0 --metadata2report nomenclature_code_2023-03-28 -f --frequency-matrix no --count-matrix no 47 | 48 | Getting information from the partitions table: output/Lm_partitions.tsv 49 | Getting metadata information... 50 | Samples present in partitions table but missing in metadata table: 51 | Samples not present in partitions table but present in metadata table: sample_0329,sample_0183,sample_0317,sample_0184 52 | Getting summary stats for the variables specified at '--partitions2report'... 53 | Getting summary stats for the variables specified at '--metadata2report'... 54 | metadata_report.py is done! 55 | 56 | 57 | 58 | 59 | ****************************** PROCESSING SAMPLES OF INTEREST ****************************** 60 | 61 | 62 | Filtering partitions_summary.tsv according to samples of interest... 63 | 64 | ------------------------------------------------------------ 65 | 66 | ReporTree is done! If you found any issue please contact us!! 67 | 68 | 69 | End: 2023-03-28 15:48:39.420937 70 | Time elapsed: 0:00:47.894436 71 | -------------------------------------------------------------------------------- /examples/Listeria/output/Lm.nwk: -------------------------------------------------------------------------------- 1 | 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2 | -------------------------------------------------------------------------------- /examples/Listeria/output/Lm_SAMPLES_OF_INTEREST_partitions_summary.tsv: -------------------------------------------------------------------------------- 1 | SAMPLE_OF_INTEREST partition cluster nomenclature_change n_increase cluster_length samples country n_country source samples_increase 2 | sample_0269 MST-4x1.0 cluster_1 kept (increase) 1 7 sample_0168,sample_0205,sample_0212,sample_0241,sample_0253,sample_0269,sample_0613 B (42.9%), A (42.9%), C (14.3%) (n = 7) 3 clinical (100.0%) (n = 7) sample_0269 3 | sample_0675 MST-4x1.0 cluster_17 kept (increase) 1 2 sample_0675,sample_0687 B (50.0%), C (50.0%) (n = 2) 2 food (100.0%) (n = 2) sample_0675 4 | sample_0675 MST-7x1.0 cluster_13 kept (increase) 1 4 sample_0281,sample_0283,sample_0675,sample_0687 B (50.0%), A (25.0%), C (25.0%) (n = 4) 3 clinical (50.0%), food (50.0%) (n = 4) sample_0675 5 | sample_0010 MST-7x1.0 cluster_81 kept (increase) 1 14 sample_0010,sample_0081,sample_0312,sample_0386,sample_0444,sample_0445,sample_0460,sample_0462,sample_0468,sample_0476,sample_0500,sample_0516,sample_0556,sample_0634 C (50.0%), B (28.6%), A (21.4%) (n = 14) 3 clinical (100.0%) (n = 14) sample_0010 6 | sample_0269 MST-7x1.0 cluster_93 kept (increase) 1 24 sample_0061,sample_0093,sample_0098,sample_0105,sample_0107,sample_0108,sample_0109,sample_0121,sample_0137,sample_0155,sample_0168,sample_0182,sample_0187,sample_0188,sample_0189,sample_0190,sample_0195,sample_0205,sample_0212,sample_0241,sample_0253,sample_0269,sample_0336,sample_0613 C (33.3%), B (33.3%), A (33.3%) (n = 24) 3 clinical (100.0%) (n = 24) sample_0269 7 | sample_0675 MST-15x1.0 cluster_8 4 sample_0281,sample_0283,sample_0675,sample_0687 B (50.0%), A (25.0%), C (25.0%) (n = 4) 3 clinical (50.0%), food (50.0%) (n = 4) 8 | sample_0010 MST-15x1.0 cluster_84 37 sample_0010,sample_0032,sample_0081,sample_0124,sample_0126,sample_0127,sample_0144,sample_0252,sample_0293,sample_0308,sample_0312,sample_0386,sample_0444,sample_0445,sample_0453,sample_0459,sample_0460,sample_0462,sample_0468,sample_0476,sample_0499,sample_0500,sample_0516,sample_0556,sample_0583,sample_0584,sample_0586,sample_0587,sample_0589,sample_0590,sample_0592,sample_0596,sample_0605,sample_0624,sample_0634,sample_0642,sample_0655 C (37.8%), A (35.1%), B (27.0%) (n = 37) 3 clinical (70.3%), food (29.7%) (n = 37) 9 | sample_0269 MST-15x1.0 cluster_64 24 sample_0061,sample_0093,sample_0098,sample_0105,sample_0107,sample_0108,sample_0109,sample_0121,sample_0137,sample_0155,sample_0168,sample_0182,sample_0187,sample_0188,sample_0189,sample_0190,sample_0195,sample_0205,sample_0212,sample_0241,sample_0253,sample_0269,sample_0336,sample_0613 C (33.3%), B (33.3%), A (33.3%) (n = 24) 3 clinical (100.0%) (n = 24) 10 | -------------------------------------------------------------------------------- /examples/Neisseria/output/NG_822_nomenclature_changes.tsv: -------------------------------------------------------------------------------- 1 | partition old_cluster old_cluster_length nomenclature_change cluster_2023-03-28 cluster_2023-03-28_length n_increase samples_increase 2 | MST-78x1.0 G001 817 kept (none) G001 817 0 3 | MST-78x1.0 G002 574 kept (none) G002 574 0 4 | MST-78x1.0 G003 202 kept (none) G003 202 0 5 | MST-78x1.0 G004 211 kept (none) G004 211 0 6 | MST-78x1.0 G005 274 kept (none) G005 274 0 7 | MST-78x1.0 G006 150 kept (none) G006 150 0 8 | MST-78x1.0 G007 143 kept (none) G007 143 0 9 | MST-78x1.0 G008 90 new (split) G008.1 85 0 10 | MST-78x1.0 G008 90 new (split) G008.2 5 0 11 | MST-78x1.0 G009 119 kept (none) G009 119 0 12 | MST-78x1.0 G010 134 kept (none) G010 134 0 13 | MST-78x1.0 G011 80 kept (none) G011 80 0 14 | MST-78x1.0 G012 129 kept (none) G012 129 0 15 | MST-78x1.0 G013 54 kept (none) G013 54 0 16 | MST-78x1.0 G014 61 kept (none) G014 61 0 17 | MST-78x1.0 G015 34 kept (none) G015 34 0 18 | MST-78x1.0 G016 32 kept (none) G016 32 0 19 | MST-78x1.0 G017 29 kept (none) G017 29 0 20 | MST-78x1.0 G018 42 kept (none) G018 42 0 21 | MST-78x1.0 G019 37 kept (none) G019 37 0 22 | MST-78x1.0 G020 21 kept (none) G020 21 0 23 | MST-78x1.0 G021 19 kept (none) G021 19 0 24 | MST-78x1.0 G022 19 kept (none) G022 19 0 25 | MST-78x1.0 G023 14 kept (none) G023 14 0 26 | MST-78x1.0 G024 13 kept (none) G024 13 0 27 | MST-78x1.0 G025 56 kept (none) G025 56 0 28 | MST-78x1.0 G026 12 kept (none) G026 12 0 29 | MST-78x1.0 G027 15 kept (none) G027 15 0 30 | MST-78x1.0 G028 10 kept (none) G028 10 0 31 | MST-78x1.0 G029 14 kept (none) G029 14 0 32 | MST-78x1.0 G030 11 kept (none) G030 11 0 33 | MST-78x1.0 G031 12 kept (none) G031 12 0 34 | MST-78x1.0 G032 10 kept (none) G032 10 0 35 | MST-78x1.0 G033 9 kept (none) G033 9 0 36 | MST-78x1.0 G034 8 kept (none) G034 8 0 37 | MST-78x1.0 G035 7 kept (none) G035 7 0 38 | MST-78x1.0 G036 10 kept (none) G036 10 0 39 | MST-78x1.0 G037 7 kept (none) G037 7 0 40 | MST-78x1.0 G038 8 kept (none) G038 8 0 41 | MST-78x1.0 G039 15 kept (none) G039 15 0 42 | MST-78x1.0 G040 8 kept (none) G040 8 0 43 | MST-78x1.0 G041 11 kept (none) G041 11 0 44 | MST-78x1.0 G042 7 kept (none) G042 7 0 45 | MST-78x1.0 G043 7 kept (none) G043 7 0 46 | MST-78x1.0 G044 7 kept (none) G044 7 0 47 | MST-78x1.0 G045 5 kept (none) G045 5 0 48 | MST-78x1.0 G046 5 kept (none) G046 5 0 49 | MST-78x1.0 G047 5 kept (none) G047 5 0 50 | MST-78x1.0 G048 4 kept (none) G048 4 0 51 | MST-78x1.0 G049 4 kept (none) G049 4 0 52 | MST-78x1.0 G050 7 kept (none) G050 7 0 53 | MST-78x1.0 G051 4 kept (none) G051 4 0 54 | MST-78x1.0 G052 4 kept (none) G052 4 0 55 | MST-78x1.0 G053 5 kept (none) G053 5 0 56 | MST-78x1.0 G054 7 kept (none) G054 7 0 57 | MST-78x1.0 G055 3 kept (none) G055 3 0 58 | MST-78x1.0 G056 3 kept (none) G056 3 0 59 | MST-78x1.0 G057 3 kept (none) G057 3 0 60 | MST-78x1.0 G058 3 kept (none) G058 3 0 61 | MST-78x1.0 G059 4 kept (none) G059 4 0 62 | MST-78x1.0 G060 4 kept (none) G060 4 0 63 | MST-78x1.0 G061 3 kept (none) G061 3 0 64 | MST-78x1.0 G062 3 kept (none) G062 3 0 65 | MST-78x1.0 G063 3 kept (none) G063 3 0 66 | MST-78x1.0 G064 3 kept (none) G064 3 0 67 | MST-78x1.0 G065 3 kept (none) G065 3 0 68 | MST-78x1.0 G066 2 kept (none) G066 2 0 69 | MST-78x1.0 G067 3 kept (none) G067 3 0 70 | MST-78x1.0 G068 2 kept (none) G068 2 0 71 | MST-78x1.0 G069 2 kept (none) G069 2 0 72 | MST-78x1.0 G070 2 kept (none) G070 2 0 73 | MST-78x1.0 G071 2 kept (none) G071 2 0 74 | MST-78x1.0 G072 2 kept (none) G072 2 0 75 | MST-78x1.0 G073 2 kept (none) G073 2 0 76 | MST-78x1.0 G074 2 kept (none) G074 2 0 77 | MST-78x1.0 G075 2 kept (none) G075 2 0 78 | MST-78x1.0 G076 2 kept (none) G076 2 0 79 | MST-78x1.0 G077 2 kept (none) G077 2 0 80 | MST-78x1.0 G078 3 kept (none) G078 3 0 81 | MST-78x1.0 G079 5 kept (none) G079 5 0 82 | MST-78x1.0 G080 3 kept (none) G080 3 0 83 | MST-78x1.0 G081 2 kept (none) G081 2 0 84 | MST-78x1.0 G082 3 kept (none) G082 3 0 85 | MST-78x1.0 G083 2 kept (none) G083 2 0 86 | MST-78x1.0 G084 2 kept (none) G084 2 0 87 | MST-78x1.0 G085 3 kept (none) G085 3 0 88 | MST-78x1.0 G086 2 kept (none) G086 2 0 89 | MST-78x1.0 G087 2 kept (none) G087 2 0 90 | MST-78x1.0 G088 2 kept (none) G088 2 0 91 | MST-78x1.0 G089 4 kept (none) G089 4 0 92 | MST-78x1.0 G090 1 kept (none) G090 1 0 93 | MST-78x1.0 G091 1 kept (none) G091 1 0 94 | MST-78x1.0 G092 1 kept (none) G092 1 0 95 | MST-78x1.0 G093 1 kept (none) G093 1 0 96 | MST-78x1.0 G094 2 kept (none) G094 2 0 97 | MST-78x1.0 G095 1 kept (none) G095 1 0 98 | MST-78x1.0 G096 1 kept (none) G096 1 0 99 | MST-78x1.0 G097 1 kept (none) G097 1 0 100 | MST-78x1.0 G098 1 kept (none) G098 1 0 101 | MST-78x1.0 G099 1 kept (none) G099 1 0 102 | MST-78x1.0 G100 1 kept (none) G100 1 0 103 | MST-78x1.0 G101 1 kept (none) G101 1 0 104 | MST-78x1.0 G102 1 kept (none) G102 1 0 105 | MST-78x1.0 G103 1 kept (none) G103 1 0 106 | MST-78x1.0 G104 1 kept (none) G104 1 0 107 | MST-78x1.0 G105 1 kept (none) G105 1 0 108 | MST-78x1.0 G106 1 kept (none) G106 1 0 109 | MST-78x1.0 G107 2 kept (none) G107 2 0 110 | MST-78x1.0 G108 1 kept (none) G108 1 0 111 | MST-78x1.0 G109 1 kept (none) G109 1 0 112 | MST-78x1.0 G110 1 kept (none) G110 1 0 113 | MST-78x1.0 G111 1 kept (none) G111 1 0 114 | MST-78x1.0 G112 1 kept (none) G112 1 0 115 | MST-78x1.0 G113 1 kept (none) G113 1 0 116 | MST-78x1.0 G114 1 kept (none) G114 1 0 117 | MST-78x1.0 G115 1 kept (none) G115 1 0 118 | MST-78x1.0 G116 1 kept (none) G116 1 0 119 | MST-78x1.0 G117 1 kept (none) G117 1 0 120 | MST-78x1.0 G118 1 kept (none) G118 1 0 121 | MST-78x1.0 G119 1 kept (none) G119 1 0 122 | MST-78x1.0 G120 1 kept (none) G120 1 0 123 | MST-78x1.0 G121 1 kept (none) G121 1 0 124 | MST-78x1.0 G122 1 kept (none) G122 1 0 125 | MST-78x1.0 G123 1 kept (none) G123 1 0 126 | MST-78x1.0 G124 1 kept (none) G124 1 0 127 | MST-78x1.0 G125 1 kept (none) G125 1 0 128 | MST-78x1.0 G126 1 kept (none) G126 1 0 129 | MST-78x1.0 G127 1 kept (none) G127 1 0 130 | MST-78x1.0 G128 2 kept (none) G128 2 0 131 | MST-78x1.0 G129 1 kept (none) G129 1 0 132 | MST-78x1.0 G130 3 kept (none) G130 3 0 133 | MST-78x1.0 G131 1 kept (none) G131 1 0 134 | MST-78x1.0 G132 1 kept (none) G132 1 0 135 | MST-78x1.0 G133 1 kept (none) G133 1 0 136 | MST-78x1.0 G134 1 kept (none) G134 1 0 137 | MST-78x1.0 G135 1 kept (none) G135 1 0 138 | MST-78x1.0 G136 1 kept (none) G136 1 0 139 | MST-78x1.0 G137 1 kept (none) G137 1 0 140 | MST-78x1.0 G138 1 kept (none) G138 1 0 141 | MST-78x1.0 G139 1 kept (none) G139 1 0 142 | MST-78x1.0 G140 1 kept (none) G140 1 0 143 | MST-78x1.0 G141 1 kept (none) G141 1 0 144 | MST-78x1.0 G142 1 kept (none) G142 1 0 145 | MST-78x1.0 G143 1 kept (none) G143 1 0 146 | MST-78x1.0 G144 1 kept (none) G144 1 0 147 | MST-78x1.0 G145 2 kept (none) G145 2 0 148 | MST-78x1.0 G146 1 kept (none) G146 1 0 149 | MST-78x1.0 G147 1 kept (none) G147 1 0 150 | MST-78x1.0 G148 1 kept (none) G148 1 0 151 | MST-78x1.0 G149 1 kept (none) G149 1 0 152 | MST-78x1.0 G150 1 kept (none) G150 1 0 153 | MST-78x1.0 G151 1 kept (none) G151 1 0 154 | MST-78x1.0 G152 1 kept (none) G152 1 0 155 | MST-78x1.0 G153 1 kept (none) G153 1 0 156 | MST-78x1.0 G154 1 kept (none) G154 1 0 157 | MST-78x1.0 G155 2 kept (none) G155 2 0 158 | MST-78x1.0 G156 1 kept (none) G156 1 0 159 | MST-78x1.0 G157 2 kept (none) G157 2 0 160 | MST-78x1.0 G158 1 kept (none) G158 1 0 161 | MST-78x1.0 G159 1 kept (none) G159 1 0 162 | MST-78x1.0 G160 1 kept (none) G160 1 0 163 | MST-78x1.0 G161 1 kept (none) G161 1 0 164 | MST-78x1.0 G162 1 kept (none) G162 1 0 165 | MST-78x1.0 G163 1 kept (none) G163 1 0 166 | MST-78x1.0 G164 1 kept (none) G164 1 0 167 | MST-78x1.0 G165 2 kept (none) G165 2 0 168 | MST-78x1.0 G166 1 kept (none) G166 1 0 169 | MST-78x1.0 G167 1 kept (none) G167 1 0 170 | MST-78x1.0 G168 1 kept (none) G168 1 0 171 | MST-78x1.0 G169 1 kept (none) G169 1 0 172 | MST-78x1.0 G170 5 kept (none) G170 5 0 173 | MST-78x1.0 G171 1 kept (none) G171 1 0 174 | MST-78x1.0 G172 2 kept (none) G172 2 0 175 | MST-78x1.0 G173 2 kept (none) G173 2 0 176 | MST-78x1.0 G174 1 kept (none) G174 1 0 177 | MST-78x1.0 G175 1 kept (none) G175 1 0 178 | MST-78x1.0 G176 1 kept (none) G176 1 0 179 | MST-78x1.0 G177 1 kept (none) G177 1 0 180 | MST-78x1.0 G178 1 kept (none) G178 1 0 181 | MST-78x1.0 G179 1 kept (none) G179 1 0 182 | MST-78x1.0 G180 1 kept (none) G180 1 0 183 | -------------------------------------------------------------------------------- /examples/Neisseria/output/NG_822_stableRegions.tsv: -------------------------------------------------------------------------------- 1 | #Stability regions of the Adjusted Wallace coefficient for output/NG_822_metrics.tsv 2 | #using a threshold of 0.99 and minimum number of required observations of 9 3 | #block_id first_partition last_partition len_block 4 | block_8 MST-40x1.0->MST-39x1.0 MST-54x1.0->MST-53x1.0 15 5 | block_14 MST-79x1.0->MST-78x1.0 MST-193x1.0->MST-192x1.0 115 6 | block_17 MST-207x1.0->MST-206x1.0 MST-221x1.0->MST-220x1.0 15 7 | block_19 MST-232x1.0->MST-231x1.0 MST-243x1.0->MST-242x1.0 12 8 | block_20 MST-245x1.0->MST-244x1.0 MST-254x1.0->MST-253x1.0 10 9 | block_21 MST-256x1.0->MST-255x1.0 MST-279x1.0->MST-278x1.0 24 10 | block_23 MST-286x1.0->MST-285x1.0 MST-294x1.0->MST-293x1.0 9 11 | block_26 MST-300x1.0->MST-299x1.0 MST-317x1.0->MST-316x1.0 18 12 | block_29 MST-333x1.0->MST-332x1.0 MST-355x1.0->MST-354x1.0 23 13 | block_31 MST-363x1.0->MST-362x1.0 MST-371x1.0->MST-370x1.0 9 14 | block_34 MST-383x1.0->MST-382x1.0 MST-399x1.0->MST-398x1.0 17 15 | block_35 MST-401x1.0->MST-400x1.0 MST-413x1.0->MST-412x1.0 13 16 | block_36 MST-415x1.0->MST-414x1.0 MST-425x1.0->MST-424x1.0 11 17 | block_41 MST-453x1.0->MST-452x1.0 MST-471x1.0->MST-470x1.0 19 18 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2/output/example1/ReporTree_ncov_PT_lineage_clade_overview.log: -------------------------------------------------------------------------------- 1 | 2 | ******************** running reportree.py ******************** 3 | 4 | ../../ReporTree/reportree.py -m nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.tsv --columns_summary_report n_strain,first_seq_date,last_seq_date,timespan_days,n_Health_region,Health_region,division,n_division,n_location,lineage,clade_membership --metadata2report lineage,clade_membership,Health_region -f country == Portugal;Representative_sampling == Weekly --frequency-matrix lineage,iso_week;clade_membership,iso_week --count-matrix lineage,iso_week;clade_membership,iso_week -out ReporTree_ncov_PT_lineage_clade_overview 5 | start: 2022-02-23 14:29:54.656126 6 | 7 | Only metadata file provided -> only metadata_report.py will be run: 8 | 9 | 10 | -------------------- metadata_report.py -------------------- 11 | 12 | /mnt/extra_disk_02/TEMP_SARS_CoV_2/ReporTree/ReporTree/scripts/metadata_report.py -m nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.tsv -o ReporTree_ncov_PT_lineage_clade_overview --columns_summary_report n_strain,first_seq_date,last_seq_date,timespan_days,n_Health_region,Health_region,division,n_division,n_location,lineage,clade_membership --partitions2report all --metadata2report lineage,clade_membership,Health_region -f country == Portugal;Representative_sampling == Weekly --frequency-matrix lineage,iso_week;clade_membership,iso_week --count-matrix lineage,iso_week;clade_membership,iso_week 13 | Getting metadata information... 14 | Filtering metadata for the following parameters: country == Portugal & Representative_sampling == Weekly 15 | Getting summary stats for the variables specified at '--metadata2report'... 16 | Getting frequency matrix for the variables specified at '--frequency-matrix'... 17 | Creating matrix with the frequency of lineage per iso_week... 18 | Creating matrix with the frequency of clade_membership per iso_week... 19 | Getting count matrix for the variables specified at '--count-matrix'... 20 | Creating matrix with the count of lineage per iso_week... 21 | Creating matrix with the count of clade_membership per iso_week... 22 | metadata_report.py is done! 23 | 24 | ------------------------------------------------------------ 25 | 26 | ReporTree is done! If you found any issue please contact us!! 27 | 28 | 29 | End: 2022-02-23 14:29:58.464260 30 | Time elapsed: 0:00:03.808134 31 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2/output/example1/ReporTree_ncov_PT_lineage_clade_overview_Health_region_summary.tsv: -------------------------------------------------------------------------------- 1 | Health_region n_strain first_seq_date last_seq_date timespan_days division n_division n_location lineage clade_membership 2 | NORTE 1691 2021-11-01 2022-02-08 99 Porto (41.0%), Braga (25.5%), Aveiro (12.1%), Braganca (8.7%), Vila Real (8.5%), Viseu (2.9%), Viana do Castelo (1.4%) (n = 1691) 7 80 BA.1 (34.9%), BA.1.1 (10.9%), AY.124 (10.2%), AY.5 (6.0%), AY.43 (5.3%), AY.122 (4.0%), AY.42 (3.8%), AY.4 (3.5%), AY.43.5 (3.3%), AY.124.1 (2.0%), BA.2 (1.4%), AY.103 (1.4%), AY.98.1 (1.2%), AY.125 (1.2%), AY.4.2 (1.1%), B.1.617.2 (1.0%), AY.94 (0.9%), AY.5.4 (0.9%), AY.4.9 (0.8%), AY.126 (0.8%), AY.121 (0.5%), AY.120 (0.4%), AY.46.6 (0.4%), AY.36 (0.4%), AY.102 (0.3%), AY.22 (0.2%), AY.34 (0.2%), AY.26 (0.2%), AY.4.2.1 (0.2%), AY.91 (0.2%), AY.98 (0.2%), AY.133 (0.2%), AY.129 (0.1%), AY.43.4 (0.1%), AY.109 (0.1%), AY.127 (0.1%), AY.34.1 (0.1%), AY.39 (0.1%), AY.46 (0.1%), AY.122.2 (0.1%), AY.99.2 (0.1%), AY.6 (0.1%), AY.106 (0.1%), AY.70 (0.1%), AY.112 (0.1%), AY.25 (0.1%), AY.117 (0.1%), AY.111 (0.1%), AY.119.1 (0.1%), AY.121.1 (0.1%), AY.9.2 (0.1%), AY.4.2.3 (0.1%), AY.122.4 (0.1%), AY.85 (0.1%), AY.44 (0.1%) (n = 1691) 21J (Delta) (52.2%), 21K (Omicron) (45.8%), 21L (Omicron) (1.4%), 21I (Delta) (0.5%) (n = 1691) 3 | ALENTEJO 1231 2021-11-01 2022-02-08 99 Beja (47.1%), Evora (23.0%), Portalegre (16.6%), Setubal (13.3%) (n = 1231) 4 54 BA.1 (42.0%), AY.4 (7.8%), BA.1.1 (7.3%), AY.124 (7.0%), AY.122 (5.2%), AY.5 (5.1%), AY.43 (4.0%), AY.102 (2.9%), AY.103 (2.8%), AY.26 (2.5%), BA.2 (2.1%), B.1.617.2 (2.0%), AY.42 (1.2%), AY.6 (1.1%), AY.4.2 (1.0%), AY.127 (0.9%), AY.129 (0.6%), AY.43.5 (0.6%), AY.121 (0.6%), AY.22 (0.5%), AY.34.1 (0.4%), AY.33 (0.3%), AY.46.6 (0.2%), AY.66 (0.2%), AY.120 (0.2%), AY.45 (0.2%), AY.118 (0.2%), AY.98.1 (0.2%), AY.4.2.1 (0.2%), AY.46 (0.2%), AY.85 (0.2%), AY.94 (0.1%), AY.36 (0.1%), AY.4.10 (0.1%), AY.125 (0.1%), AY.78 (0.1%) (n = 1231) 21K (Omicron) (49.3%), 21J (Delta) (45.8%), 21I (Delta) (2.8%), 21L (Omicron) (2.1%) (n = 1231) 4 | LX V TEJO 1225 2021-11-01 2022-02-08 99 Lisboa (65.0%), Santarem (17.6%), Setubal (14.0%), Leiria (3.5%) (n = 1225) 4 48 BA.1 (43.1%), BA.1.1 (6.1%), AY.4 (5.3%), AY.124 (4.9%), AY.43 (4.2%), AY.103 (3.7%), B.1.617.2 (3.7%), BA.2 (3.3%), AY.5 (2.8%), AY.122 (2.8%), AY.102 (2.7%), AY.4.2 (2.2%), AY.125 (1.9%), AY.26 (1.8%), AY.22 (1.4%), AY.34.1 (1.2%), AY.43.5 (1.1%), AY.42 (0.8%), AY.121 (0.7%), AY.126 (0.7%), AY.9.2 (0.7%), AY.124.1 (0.7%), AY.46 (0.6%), AY.127 (0.4%), AY.33 (0.4%), AY.98.1 (0.3%), AY.39 (0.2%), AY.99.2 (0.2%), AY.6 (0.2%), AY.129 (0.2%), AY.43.1 (0.2%), AY.123 (0.2%), AY.44 (0.2%), AY.34 (0.1%), AY.94 (0.1%), AY.123.1 (0.1%), AY.15 (0.1%), AY.118 (0.1%), AY.39.2 (0.1%), AY.4.2.1 (0.1%), AY.100 (0.1%), AY.91 (0.1%), AY.120 (0.1%), AY.4.9 (0.1%), AY.4.2.3 (0.1%), B.1.36.17 (0.1%), AY.23 (0.1%), AY.4.6 (0.1%), AY.112 (0.1%) (n = 1225) 21K (Omicron) (49.2%), 21J (Delta) (45.0%), 21L (Omicron) (3.3%), 21I (Delta) (2.4%), 20A (0.1%), 21A (Delta) (0.1%) (n = 1225) 5 | CENTRO 1222 2021-11-01 2022-02-08 99 Viseu (22.6%), Coimbra (19.6%), Castelo Branco (18.7%), Aveiro (17.9%), Guarda (13.8%), Leiria (7.4%) (n = 1222) 6 85 BA.1 (32.0%), AY.124 (10.4%), BA.1.1 (8.5%), AY.43 (8.3%), AY.43.5 (6.6%), AY.5 (4.5%), AY.4 (4.0%), AY.26 (4.0%), AY.121 (2.5%), BA.2 (2.5%), AY.42 (2.4%), AY.103 (1.7%), AY.124.1 (1.6%), AY.117 (1.5%), AY.122 (1.5%), AY.98.1 (0.9%), B.1.617.2 (0.7%), AY.46 (0.7%), AY.46.6 (0.6%), AY.4.2 (0.5%), AY.125 (0.4%), AY.4.5 (0.4%), AY.22 (0.4%), AY.91 (0.3%), AY.94 (0.2%), AY.36 (0.2%), AY.118 (0.2%), AY.44 (0.2%), AY.126 (0.2%), AY.109 (0.2%), AY.127 (0.2%), AY.5.4 (0.2%), AY.3 (0.1%), AY.4.10 (0.1%), AY.34.1 (0.1%), AY.121.1 (0.1%), AY.4.7 (0.1%), AY.120 (0.1%), AY.4.2.3 (0.1%), AY.102 (0.1%), AY.23 (0.1%), AY.99.2 (0.1%), AY.123 (0.1%), AY.32 (0.1%), AY.112 (0.1%), AY.33 (0.1%), AY.88 (0.1%), AY.92 (0.1%) (n = 1222) 21J (Delta) (52.5%), 21K (Omicron) (40.5%), 21I (Delta) (4.4%), 21L (Omicron) (2.5%), 21A (Delta) (0.1%) (n = 1222) 6 | ALGARVE 777 2021-11-01 2022-02-08 99 Faro (100.0%) (n = 777) 1 16 BA.1 (34.9%), AY.5 (15.3%), AY.4.2 (8.6%), BA.1.1 (7.2%), AY.43 (6.9%), AY.4 (6.0%), BA.2 (3.2%), AY.46 (2.2%), AY.122 (2.1%), B.1.617.2 (2.1%), AY.98.1 (1.8%), AY.121 (1.4%), AY.125 (1.2%), AY.124 (0.9%), AY.9.2 (0.8%), AY.4.2.2 (0.8%), AY.103 (0.6%), AY.34 (0.5%), AY.117 (0.4%), AY.4.2.1 (0.4%), AY.42 (0.4%), AY.85 (0.4%), AY.6 (0.3%), AY.126 (0.1%), AY.4.4 (0.1%), AY.3 (0.1%), AY.127 (0.1%), AY.26 (0.1%), AY.43.5 (0.1%), AY.120 (0.1%), AY.4.9 (0.1%), AY.99.2 (0.1%), AY.106 (0.1%), AY.124.1 (0.1%), AY.4.5 (0.1%), AY.25.3 (0.1%) (n = 777) 21J (Delta) (53.8%), 21K (Omicron) (42.1%), 21L (Omicron) (3.2%), 21I (Delta) (0.9%) (n = 777) 7 | ACORES-RA 749 2021-11-01 2022-02-08 99 Acores (100.0%) (n = 749) 1 20 BA.1 (33.1%), AY.124.1 (19.6%), AY.4 (10.3%), BA.1.1 (10.1%), AY.124 (4.3%), AY.122 (3.5%), AY.43 (3.3%), AY.36 (2.1%), AY.42 (1.7%), B.1.617.2 (1.2%), AY.25.1 (0.9%), AY.4.2 (0.9%), AY.25 (0.8%), AY.43.5 (0.8%), AY.102 (0.8%), AY.22 (0.8%), AY.5 (0.5%), AY.33 (0.5%), AY.127 (0.4%), AY.91 (0.4%), AY.9.2 (0.4%), AY.116 (0.4%), AY.103 (0.4%), BA.2 (0.4%), AY.126 (0.3%), AY.46.6 (0.3%), AY.98.1 (0.3%), AY.4.2.1 (0.3%), AY.119 (0.3%), AY.99.2 (0.3%), AY.39 (0.1%), AY.120 (0.1%), AY.125 (0.1%), AY.128 (0.1%) (n = 749) 21J (Delta) (55.9%), 21K (Omicron) (43.3%), 21L (Omicron) (0.4%), 21I (Delta) (0.4%) (n = 749) 8 | MADEIRA-RA 483 2021-11-01 2022-02-07 98 Madeira (100.0%) (n = 483) 1 11 AY.4 (38.5%), BA.1 (35.8%), BA.1.1 (7.7%), AY.43 (3.9%), AY.122 (2.5%), AY.127 (2.1%), BA.2 (1.7%), AY.98.1 (1.2%), AY.4.2 (1.2%), AY.121 (1.0%), AY.124.1 (0.6%), B.1.617.2 (0.6%), AY.46.6 (0.4%), AY.4.10 (0.4%), AY.26 (0.4%), AY.126 (0.2%), AY.5 (0.2%), AY.129 (0.2%), AY.43.5 (0.2%), AY.34.1 (0.2%), AY.91 (0.2%), AY.124 (0.2%), AY.102 (0.2%), AY.42 (0.2%) (n = 483) 21J (Delta) (54.5%), 21K (Omicron) (43.5%), 21L (Omicron) (1.7%), 21I (Delta) (0.4%) (n = 483) 9 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2/output/example1/ReporTree_ncov_PT_lineage_clade_overview_clade_membership_iso_week_count_matrix.tsv: -------------------------------------------------------------------------------- 1 | iso_week 21L (Omicron) 20A 21K (Omicron) 21J (Delta) 21A (Delta) 21I (Delta) 2 | 2021W44 0 1 0 448 0 10 3 | 2021W45 0 0 0 470 1 24 4 | 2021W46 0 0 0 577 0 30 5 | 2021W47 0 0 1 628 0 29 6 | 2021W48 0 0 8 476 0 21 7 | 2021W49 0 0 16 352 0 9 8 | 2021W50 0 0 94 399 1 10 9 | 2021W51 0 0 195 186 0 3 10 | 2021W52 1 0 367 85 0 1 11 | 2022W01 4 0 484 66 0 1 12 | 2022W02 2 0 494 21 0 0 13 | 2022W03 11 0 468 14 0 0 14 | 2022W04 29 0 475 11 0 0 15 | 2022W05 49 0 447 5 0 0 16 | 2022W06 61 0 292 1 0 0 17 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2/output/example1/ReporTree_ncov_PT_lineage_clade_overview_clade_membership_iso_week_freq_matrix.tsv: -------------------------------------------------------------------------------- 1 | iso_week 21L (Omicron) 20A 21K (Omicron) 21J (Delta) 21A (Delta) 21I (Delta) 2 | 2021W44 0.0 0.002178649237472767 0.0 0.9760348583877996 0.0 0.02178649237472767 3 | 2021W45 0.0 0.0 0.0 0.9494949494949495 0.00202020202020202 0.048484848484848485 4 | 2021W46 0.0 0.0 0.0 0.9505766062602965 0.0 0.04942339373970346 5 | 2021W47 0.0 0.0 0.001519756838905775 0.9544072948328267 0.0 0.044072948328267476 6 | 2021W48 0.0 0.0 0.015841584158415842 0.9425742574257425 0.0 0.041584158415841586 7 | 2021W49 0.0 0.0 0.042440318302387266 0.9336870026525199 0.0 0.023872679045092837 8 | 2021W50 0.0 0.0 0.1865079365079365 0.7916666666666666 0.001984126984126984 0.01984126984126984 9 | 2021W51 0.0 0.0 0.5078125 0.484375 0.0 0.0078125 10 | 2021W52 0.0022026431718061676 0.0 0.8083700440528634 0.18722466960352424 0.0 0.0022026431718061676 11 | 2022W01 0.007207207207207207 0.0 0.872072072072072 0.11891891891891893 0.0 0.0018018018018018018 12 | 2022W02 0.0038684719535783366 0.0 0.9555125725338491 0.04061895551257253 0.0 0.0 13 | 2022W03 0.02231237322515213 0.0 0.949290060851927 0.028397565922920892 0.0 0.0 14 | 2022W04 0.05631067961165048 0.0 0.9223300970873787 0.021359223300970873 0.0 0.0 15 | 2022W05 0.09780439121756487 0.0 0.8922155688622755 0.00998003992015968 0.0 0.0 16 | 2022W06 0.17231638418079095 0.0 0.8248587570621468 0.002824858757062147 0.0 0.0 17 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2/output/example1/ReporTree_ncov_PT_lineage_clade_overview_clade_membership_summary.tsv: -------------------------------------------------------------------------------- 1 | clade_membership n_strain first_seq_date last_seq_date timespan_days n_Health_region Health_region division n_division n_location lineage 2 | 21J (Delta) 3739 2021-11-01 2022-02-07 98 7 NORTE (23.6%), CENTRO (17.1%), ALENTEJO (15.1%), LX V TEJO (14.7%), ACORES-RA (11.2%), ALGARVE (11.2%), MADEIRA-RA (7.0%) (n = 3739) Acores (11.2%), Faro (11.2%), Lisboa (9.5%), Porto (9.3%), Beja (7.9%), Madeira (7.0%), Aveiro (6.5%), Braga (6.0%), Viseu (5.6%), Evora (3.3%), Coimbra (3.1%), Setubal (3.0%), Castelo Branco (2.8%), Santarem (2.7%), Portalegre (2.6%), Guarda (2.2%), Leiria (2.0%), Braganca (2.0%), Vila Real (1.7%), Viana do Castelo (0.3%) (n = 3739) 20 268 AY.4 (15.5%), AY.124 (13.0%), AY.43 (10.5%), AY.5 (10.1%), AY.122 (6.3%), AY.124.1 (5.7%), AY.43.5 (4.4%), AY.4.2 (3.9%), AY.42 (3.6%), AY.103 (3.5%), B.1.617.2 (3.2%), AY.102 (2.2%), AY.121 (1.9%), AY.98.1 (1.6%), AY.125 (1.6%), AY.22 (1.0%), AY.46 (1.0%), AY.127 (0.9%), AY.126 (0.7%), AY.36 (0.7%), AY.34.1 (0.6%), AY.117 (0.6%), AY.94 (0.6%), AY.46.6 (0.5%), AY.6 (0.5%), AY.5.4 (0.5%), AY.4.9 (0.4%), AY.120 (0.4%), AY.33 (0.4%), AY.129 (0.3%), AY.91 (0.3%), AY.4.2.1 (0.3%), AY.99.2 (0.2%), AY.34 (0.2%), AY.25 (0.2%), AY.25.1 (0.2%), AY.118 (0.2%), AY.39 (0.2%), AY.4.5 (0.2%), AY.85 (0.2%), AY.4.2.2 (0.2%), AY.44 (0.2%), AY.109 (0.1%), AY.4.10 (0.1%), AY.112 (0.1%), AY.4.2.3 (0.1%), AY.98 (0.1%), AY.106 (0.1%), AY.116 (0.1%), AY.123 (0.1%), AY.45 (0.1%), AY.43.4 (0.1%), AY.3 (0.1%), AY.43.1 (0.1%), AY.121.1 (0.1%), AY.23 (0.1%), AY.119 (0.1%), AY.122.2 (0.1%), AY.4.4 (0.0%), AY.123.1 (0.0%), AY.15 (0.0%), AY.111 (0.0%), AY.39.2 (0.0%), AY.119.1 (0.0%), AY.100 (0.0%), AY.4.7 (0.0%), AY.78 (0.0%), AY.128 (0.0%), AY.122.4 (0.0%), AY.4.6 (0.0%), AY.32 (0.0%), AY.25.3 (0.0%), AY.88 (0.0%), AY.92 (0.0%) (n = 3739) 3 | 21K (Omicron) 3341 2021-11-27 2022-02-08 73 7 NORTE (23.2%), ALENTEJO (18.2%), LX V TEJO (18.0%), CENTRO (14.8%), ALGARVE (9.8%), ACORES-RA (9.7%), MADEIRA-RA (6.3%) (n = 3341) Lisboa (11.7%), Porto (9.9%), Faro (9.8%), Acores (9.7%), Beja (7.7%), Madeira (6.3%), Setubal (6.2%), Braga (6.0%), Aveiro (5.3%), Evora (4.1%), Coimbra (3.3%), Viseu (3.3%), Portalegre (3.1%), Santarem (3.1%), Castelo Branco (2.3%), Guarda (2.2%), Braganca (2.2%), Vila Real (2.0%), Leiria (1.5%), Viana do Castelo (0.4%) (n = 3341) 20 271 BA.1 (81.4%), BA.1.1 (18.6%) (n = 3341) 4 | 21L (Omicron) 157 2022-01-02 2022-02-08 37 7 LX V TEJO (25.5%), CENTRO (19.7%), ALENTEJO (16.6%), ALGARVE (15.9%), NORTE (15.3%), MADEIRA-RA (5.1%), ACORES-RA (1.9%) (n = 157) Faro (15.9%), Lisboa (15.3%), Castelo Branco (12.1%), Setubal (8.3%), Porto (7.6%), Beja (7.0%), Vila Real (5.1%), Madeira (5.1%), Evora (4.5%), Santarem (4.5%), Coimbra (2.5%), Portalegre (1.9%), Guarda (1.9%), Acores (1.9%), Aveiro (1.9%), Braga (1.3%), Leiria (1.3%), Viseu (1.3%), Braganca (0.6%) (n = 157) 19 72 BA.2 (100.0%) (n = 157) 5 | 21I (Delta) 138 2021-11-01 2022-01-09 69 7 CENTRO (39.1%), ALENTEJO (24.6%), LX V TEJO (21.0%), NORTE (6.5%), ALGARVE (5.1%), ACORES-RA (2.2%), MADEIRA-RA (1.4%) (n = 138) Castelo Branco (18.8%), Lisboa (15.9%), Beja (11.6%), Evora (10.1%), Guarda (8.7%), Coimbra (5.8%), Faro (5.1%), Leiria (4.3%), Santarem (2.9%), Setubal (2.2%), Vila Real (2.2%), Porto (2.2%), Acores (2.2%), Viseu (2.2%), Portalegre (1.4%), Braga (1.4%), Madeira (1.4%), Braganca (0.7%), Aveiro (0.7%) (n = 138) 19 62 AY.26 (77.5%), AY.9.2 (13.0%), B.1.617.2 (3.6%), AY.66 (2.2%), AY.133 (2.2%), AY.70 (1.4%) (n = 138) 6 | 21A (Delta) 2 2021-11-09 2021-12-14 35 2 CENTRO (50.0%), LX V TEJO (50.0%) (n = 2) Coimbra (50.0%), Lisboa (50.0%) (n = 2) 2 2 AY.26 (50.0%), B.1.617.2 (50.0%) (n = 2) 7 | 20A 1 2021-11-06 2021-11-06 0 1 LX V TEJO (100.0%) (n = 1) Lisboa (100.0%) (n = 1) 1 1 B.1.36.17 (100.0%) (n = 1) 8 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2/output/example1/ReporTree_ncov_PT_lineage_clade_overview_lineage_iso_week_count_matrix.tsv: -------------------------------------------------------------------------------- 1 | iso_week AY.45 AY.94 AY.3 AY.15 AY.4.2.1 AY.121.1 AY.91 AY.120 AY.6 AY.106 AY.124.1 AY.133 AY.4.2.2 AY.4 AY.129 AY.4.4 AY.98.1 AY.39.2 AY.4.9 AY.4.2.3 AY.78 B.1.36.17 AY.102 AY.25.1 AY.122.2 AY.4.2 AY.99.2 AY.22 AY.121 AY.70 AY.32 AY.112 AY.33 AY.92 AY.34 AY.46.6 AY.117 AY.109 AY.127 AY.4.10 AY.43.1 AY.118 AY.122 AY.43.5 AY.34.1 AY.39 AY.100 AY.125 AY.124 AY.46 AY.23 AY.119 AY.98 AY.123 AY.42 BA.2 AY.25.3 AY.44 AY.126 AY.5 AY.25 AY.66 AY.43.4 AY.43 AY.36 BA.1.1 AY.123.1 AY.26 AY.111 AY.5.4 AY.119.1 BA.1 AY.4.7 AY.9.2 AY.128 AY.116 AY.122.4 AY.103 AY.4.5 AY.85 AY.88 AY.4.6 B.1.617.2 2 | 2021W44 0 4 0 0 0 1 4 6 0 0 25 0 0 82 0 0 3 1 1 0 0 1 3 0 0 12 1 7 5 0 0 0 4 0 2 4 11 0 1 0 1 0 20 32 6 0 0 13 54 0 0 0 0 1 10 0 0 1 2 47 0 1 0 57 0 0 0 9 0 3 0 0 0 0 0 0 0 19 0 2 1 0 2 3 | 2021W45 0 3 0 0 1 0 1 2 3 0 33 1 0 91 1 0 2 0 0 0 0 0 10 0 0 8 2 7 7 1 1 0 0 0 1 6 5 0 2 0 1 0 23 23 2 1 0 11 80 1 0 1 1 2 16 0 1 0 2 61 0 2 0 38 1 0 0 16 0 2 0 0 1 5 0 0 0 7 0 2 0 1 6 4 | 2021W46 0 4 0 0 2 1 1 3 8 0 44 2 0 49 0 1 13 0 0 1 0 0 29 0 0 16 3 7 13 1 0 2 5 0 1 1 1 1 7 0 0 0 38 24 4 3 0 8 85 2 1 1 0 0 12 0 0 0 1 77 0 0 0 55 15 0 0 26 0 3 0 0 0 1 0 0 1 17 1 0 0 0 16 5 | 2021W47 0 4 0 0 0 0 5 1 4 1 30 0 1 86 3 0 16 0 2 1 0 0 27 0 2 27 3 4 16 0 0 0 1 0 0 3 3 0 5 0 0 1 43 31 2 2 0 7 95 4 0 0 1 0 14 0 0 1 2 58 0 0 0 65 2 0 0 26 1 7 0 1 0 3 1 0 0 32 1 2 0 0 11 6 | 2021W48 0 1 1 1 2 0 1 1 3 1 34 0 1 45 2 0 8 0 2 0 1 0 10 1 0 19 0 2 8 0 0 0 1 0 3 5 0 2 3 0 0 1 31 19 1 0 0 8 64 10 0 0 0 0 24 0 0 4 6 57 0 0 0 58 1 0 0 19 0 1 0 8 0 2 0 0 0 18 0 0 0 0 15 7 | 2021W49 0 0 0 0 1 0 0 0 0 0 17 0 0 58 3 0 8 0 3 0 0 0 3 0 0 17 0 4 12 0 0 0 0 1 0 0 1 1 1 0 0 0 24 18 3 0 0 6 36 10 0 0 0 0 21 0 0 0 5 26 2 0 0 35 1 0 0 6 0 1 0 16 0 2 0 0 0 14 2 0 0 0 19 8 | 2021W50 2 2 1 0 2 0 0 1 2 1 10 0 3 75 1 0 7 0 4 0 0 0 0 4 0 15 0 6 8 0 0 1 3 0 0 0 1 0 4 3 0 1 24 13 4 0 1 2 44 3 1 0 0 0 22 0 0 0 6 33 2 0 0 44 2 7 0 4 0 0 1 87 0 3 0 3 0 16 0 0 0 0 25 9 | 2021W51 0 2 0 0 3 0 0 0 0 0 8 0 1 33 2 0 2 0 2 0 0 0 0 1 0 17 0 1 0 0 0 1 0 0 0 0 0 0 4 0 0 1 20 1 2 0 0 2 15 5 0 0 0 0 9 0 0 0 2 11 1 0 0 18 2 16 0 1 0 0 0 179 0 1 0 0 0 5 2 0 0 0 14 10 | 2021W52 0 1 0 0 0 0 0 0 0 0 5 0 0 15 0 0 0 0 2 1 0 0 0 0 0 8 0 0 2 0 0 0 0 0 0 1 0 0 4 0 0 1 6 2 0 0 0 1 6 1 0 0 1 0 5 1 0 0 1 3 0 0 0 10 0 41 1 0 0 0 0 326 0 1 0 0 0 3 0 0 0 0 5 11 | 2022W01 0 0 0 0 0 0 0 0 0 0 3 0 0 17 0 0 0 0 0 0 0 0 0 1 0 4 0 0 0 0 0 0 0 0 1 0 0 0 3 1 0 1 8 1 0 0 0 1 5 0 0 0 0 0 1 4 0 0 0 3 2 0 0 7 2 55 0 1 0 0 0 429 0 0 0 0 0 0 0 0 0 0 5 12 | 2022W02 0 0 0 0 0 0 0 0 0 0 1 0 0 8 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 2 3 0 72 0 0 0 0 0 422 0 0 0 0 0 1 0 0 0 0 3 13 | 2022W03 0 0 0 0 0 0 0 0 0 0 2 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 11 0 0 0 2 0 0 0 0 0 74 0 0 0 0 0 394 0 0 0 0 0 0 0 0 0 0 0 14 | 2022W04 0 0 0 0 0 0 0 0 0 0 1 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 29 0 0 0 0 0 0 0 1 0 118 0 0 0 0 0 357 0 0 0 0 0 0 0 0 0 0 3 15 | 2022W05 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 49 0 0 0 0 0 0 0 0 0 141 0 0 0 0 0 306 0 0 0 0 0 0 0 0 0 0 0 16 | 2022W06 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 61 0 0 0 0 0 0 0 0 0 98 0 0 0 0 0 194 0 0 0 0 0 0 0 0 0 0 0 17 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2/output/example1/ReporTree_ncov_PT_lineage_clade_overview_lineage_iso_week_freq_matrix.tsv: -------------------------------------------------------------------------------- 1 | iso_week AY.45 AY.94 AY.3 AY.15 AY.4.2.1 AY.121.1 AY.91 AY.120 AY.6 AY.106 AY.124.1 AY.133 AY.4.2.2 AY.4 AY.129 AY.4.4 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0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.002028397565922921 0.0 0.0 0.0 0.0 0.0 0.0 0.02231237322515213 0.0 0.0 0.0 0.004056795131845842 0.0 0.0 0.0 0.0 0.0 0.15010141987829614 0.0 0.0 0.0 0.0 0.0 0.7991886409736308 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14 | 2022W04 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.001941747572815534 0.0 0.0 0.009708737864077669 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.001941747572815534 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.05631067961165048 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.001941747572815534 0.0 0.229126213592233 0.0 0.0 0.0 0.0 0.0 0.6932038834951456 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.005825242718446602 15 | 2022W05 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00998003992015968 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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clade_membership 2 | BA.1 2719 2021-11-27 2022-02-08 73 7 NORTE (21.7%), LX V TEJO (19.4%), ALENTEJO (19.0%), CENTRO (14.4%), ALGARVE (10.0%), ACORES-RA (9.1%), MADEIRA-RA (6.4%) (n = 2719) Lisboa (12.4%), Faro (10.0%), Porto (9.6%), Acores (9.1%), Beja (8.0%), Setubal (6.7%), Madeira (6.4%), Braga (5.6%), Aveiro (4.8%), Evora (4.3%), Santarem (3.5%), Portalegre (3.2%), Coimbra (3.2%), Viseu (2.9%), Castelo Branco (2.6%), Guarda (2.2%), Braganca (2.1%), Vila Real (1.6%), Leiria (1.5%), Viana do Castelo (0.3%) (n = 2719) 20 263 21K (Omicron) (100.0%) (n = 2719) 3 | BA.1.1 622 2021-12-14 2022-02-08 56 7 NORTE (29.6%), CENTRO (16.7%), ALENTEJO (14.5%), ACORES-RA (12.2%), LX V TEJO (12.1%), ALGARVE (9.0%), MADEIRA-RA (5.9%) (n = 622) Acores (12.2%), Porto (11.1%), Faro (9.0%), Lisboa (8.7%), Braga (8.2%), Aveiro (7.2%), Beja (6.6%), Madeira (5.9%), Viseu (4.8%), Setubal (4.0%), Coimbra (3.9%), Vila Real (3.7%), Evora (3.4%), Portalegre (2.4%), Braganca (2.3%), Guarda (2.1%), Leiria (1.4%), Santarem (1.3%), Castelo Branco (1.1%), Viana do Castelo (0.6%) (n = 622) 20 158 21K (Omicron) (100.0%) (n = 622) 4 | AY.4 579 2021-11-01 2022-02-07 98 7 MADEIRA-RA (32.1%), ALENTEJO (16.6%), ACORES-RA (13.3%), LX V TEJO (11.2%), NORTE (10.2%), CENTRO (8.5%), ALGARVE (8.1%) (n = 579) Madeira (32.1%), Acores (13.3%), Beja (9.8%), Faro (8.1%), Lisboa (7.4%), Braga (4.8%), Portalegre (4.0%), Castelo Branco (3.5%), Porto (2.8%), Evora (2.6%), Setubal (2.4%), Viseu (2.2%), Aveiro (2.2%), Santarem (1.6%), Guarda (1.2%), Viana do Castelo (0.7%), Vila Real (0.5%), Leiria (0.3%), Braganca (0.3%) (n = 579) 19 106 21J (Delta) (100.0%) (n = 579) 5 | AY.124 485 2021-11-01 2022-01-17 77 7 NORTE (35.5%), CENTRO (26.2%), ALENTEJO (17.7%), LX V TEJO (12.4%), ACORES-RA (6.6%), ALGARVE (1.4%), MADEIRA-RA (0.2%) (n = 485) Aveiro (15.1%), Beja (11.8%), Porto (11.5%), Viseu (10.9%), Lisboa (8.5%), Acores (6.6%), Braga (5.8%), Braganca (5.6%), Guarda (4.5%), Setubal (3.3%), Leiria (3.3%), Castelo Branco (2.9%), Coimbra (2.3%), Santarem (2.1%), Evora (1.9%), Portalegre (1.6%), Faro (1.4%), Vila Real (0.8%), Madeira (0.2%) (n = 485) 19 133 21J (Delta) (100.0%) (n = 485) 6 | AY.43 391 2021-11-01 2022-01-24 84 7 CENTRO (26.1%), NORTE (23.0%), ALGARVE (13.8%), LX V TEJO (13.3%), ALENTEJO (12.5%), ACORES-RA (6.4%), MADEIRA-RA (4.9%) (n = 391) Faro (13.8%), Porto (13.0%), Lisboa (8.7%), Viseu (8.4%), Acores (6.4%), Guarda (5.9%), Beja (5.9%), Aveiro (5.4%), Madeira (4.9%), Coimbra (4.6%), Braga (4.3%), Castelo Branco (3.6%), Portalegre (3.3%), Setubal (3.3%), Leiria (2.3%), Santarem (2.0%), Braganca (1.8%), Evora (1.0%), Vila Real (1.0%), Viana do Castelo (0.3%) (n = 391) 20 128 21J (Delta) (100.0%) (n = 391) 7 | AY.5 378 2021-11-01 2022-01-23 83 7 ALGARVE (31.5%), NORTE (27.0%), ALENTEJO (16.7%), CENTRO (14.6%), LX V TEJO (9.0%), ACORES-RA (1.1%), MADEIRA-RA (0.3%) (n = 378) Faro (31.5%), Braga (14.0%), Beja (9.3%), Aveiro (6.9%), Evora (6.6%), Lisboa (6.3%), Porto (6.1%), Castelo Branco (4.2%), Viseu (3.4%), Guarda (2.9%), Leiria (2.1%), Coimbra (1.9%), Santarem (1.9%), Acores (1.1%), Portalegre (0.8%), Braganca (0.5%), Madeira (0.3%), Vila Real (0.3%) (n = 378) 18 96 21J (Delta) (100.0%) (n = 378) 8 | AY.122 237 2021-11-01 2022-01-09 69 7 NORTE (28.3%), ALENTEJO (27.0%), LX V TEJO (14.3%), ACORES-RA (11.0%), CENTRO (7.6%), ALGARVE (6.8%), MADEIRA-RA (5.1%) (n = 237) Beja (21.5%), Porto (12.7%), Acores (11.0%), Lisboa (9.7%), Faro (6.8%), Braganca (6.8%), Madeira (5.1%), Vila Real (5.1%), Portalegre (3.8%), Braga (3.8%), Aveiro (3.0%), Guarda (3.0%), Santarem (2.5%), Leiria (1.7%), Evora (1.7%), Setubal (1.3%), Coimbra (0.8%) (n = 237) 17 77 21J (Delta) (100.0%) (n = 237) 9 | AY.124.1 213 2021-11-01 2022-01-30 90 6 ACORES-RA (69.0%), NORTE (16.0%), CENTRO (9.4%), LX V TEJO (3.8%), MADEIRA-RA (1.4%), ALGARVE (0.5%) (n = 213) Acores (69.0%), Aveiro (14.6%), Porto (5.2%), Viseu (2.3%), Lisboa (1.9%), Madeira (1.4%), Braga (1.4%), Santarem (1.4%), Castelo Branco (0.9%), Coimbra (0.5%), Faro (0.5%), Leiria (0.5%), Braganca (0.5%) (n = 213) 13 47 21J (Delta) (100.0%) (n = 213) 10 | AY.43.5 165 2021-11-01 2022-01-24 84 7 CENTRO (49.1%), NORTE (33.9%), LX V TEJO (7.9%), ALENTEJO (4.2%), ACORES-RA (3.6%), ALGARVE (0.6%), MADEIRA-RA (0.6%) (n = 165) Coimbra (18.8%), Viseu (15.8%), Porto (13.9%), Aveiro (10.3%), Braga (7.9%), Lisboa (6.1%), Leiria (5.5%), Vila Real (4.8%), Portalegre (4.2%), Acores (3.6%), Braganca (3.0%), Castelo Branco (2.4%), Guarda (1.8%), Madeira (0.6%), Setubal (0.6%), Faro (0.6%) (n = 165) 16 72 21J (Delta) (100.0%) (n = 165) 11 | BA.2 157 2022-01-02 2022-02-08 37 7 LX V TEJO (25.5%), CENTRO (19.7%), ALENTEJO (16.6%), ALGARVE (15.9%), NORTE (15.3%), MADEIRA-RA (5.1%), ACORES-RA (1.9%) (n = 157) Faro (15.9%), Lisboa (15.3%), Castelo Branco (12.1%), Setubal (8.3%), Porto (7.6%), Beja (7.0%), Vila Real (5.1%), Madeira (5.1%), Evora (4.5%), Santarem (4.5%), Coimbra (2.5%), Portalegre (1.9%), Guarda (1.9%), Acores (1.9%), Aveiro (1.9%), Braga (1.3%), Leiria (1.3%), Viseu (1.3%), Braganca (0.6%) (n = 157) 19 72 21L (Omicron) (100.0%) (n = 157) 12 | AY.4.2 144 2021-11-01 2022-01-10 70 7 ALGARVE (46.5%), LX V TEJO (18.8%), NORTE (13.2%), ALENTEJO (8.3%), ACORES-RA (4.9%), MADEIRA-RA (4.2%), CENTRO (4.2%) (n = 144) Faro (46.5%), Lisboa (13.2%), Porto (5.6%), Acores (4.9%), Setubal (4.9%), Madeira (4.2%), Beja (3.5%), Evora (2.8%), Braga (2.8%), Viseu (2.1%), Braganca (2.1%), Portalegre (1.4%), Coimbra (1.4%), Aveiro (1.4%), Vila Real (1.4%), Santarem (1.4%), Viana do Castelo (0.7%) (n = 144) 17 54 21J (Delta) (100.0%) (n = 144) 13 | AY.42 136 2021-11-01 2022-01-10 70 7 NORTE (47.8%), CENTRO (21.3%), ALENTEJO (11.0%), ACORES-RA (9.6%), LX V TEJO (7.4%), ALGARVE (2.2%), MADEIRA-RA (0.7%) (n = 136) Porto (26.5%), Viseu (18.4%), Braga (10.3%), Acores (9.6%), Aveiro (6.6%), Setubal (5.9%), Beja (5.1%), Vila Real (3.7%), Lisboa (3.7%), Santarem (3.7%), Faro (2.2%), Braganca (1.5%), Guarda (0.7%), Madeira (0.7%), Coimbra (0.7%), Castelo Branco (0.7%) (n = 136) 16 56 21J (Delta) (100.0%) (n = 136) 14 | AY.103 132 2021-11-01 2022-01-10 70 6 LX V TEJO (34.1%), ALENTEJO (26.5%), NORTE (17.4%), CENTRO (15.9%), ALGARVE (3.8%), ACORES-RA (2.3%) (n = 132) Lisboa (26.5%), Setubal (14.4%), Braga (9.8%), Viseu (7.6%), Beja (7.6%), Porto (4.5%), Leiria (4.5%), Portalegre (3.8%), Faro (3.8%), Evora (3.8%), Aveiro (3.8%), Santarem (3.8%), Acores (2.3%), Coimbra (1.5%), Castelo Branco (0.8%), Vila Real (0.8%), Guarda (0.8%) (n = 132) 17 54 21J (Delta) (100.0%) (n = 132) 15 | B.1.617.2 124 2021-11-01 2022-01-29 89 7 LX V TEJO (36.3%), ALENTEJO (20.2%), NORTE (13.7%), ALGARVE (12.9%), CENTRO (7.3%), ACORES-RA (7.3%), MADEIRA-RA (2.4%) (n = 124) Lisboa (25.8%), Faro (12.9%), Setubal (9.7%), Evora (8.9%), Acores (7.3%), Portalegre (6.5%), Porto (5.6%), Castelo Branco (4.8%), Braga (4.0%), Beja (3.2%), Madeira (2.4%), Vila Real (2.4%), Aveiro (1.6%), Leiria (1.6%), Viseu (0.8%), Braganca (0.8%), Coimbra (0.8%), Santarem (0.8%) (n = 124) 18 60 21J (Delta) (95.2%), 21I (Delta) (4.0%), 21A (Delta) (0.8%) (n = 124) 16 | AY.26 108 2021-11-01 2022-01-09 69 6 CENTRO (45.4%), ALENTEJO (28.7%), LX V TEJO (20.4%), NORTE (2.8%), MADEIRA-RA (1.9%), ALGARVE (0.9%) (n = 108) Castelo Branco (19.4%), Lisboa (15.7%), Evora (13.0%), Beja (12.0%), Guarda (11.1%), Coimbra (7.4%), Leiria (5.6%), Viseu (2.8%), Setubal (2.8%), Porto (1.9%), Madeira (1.9%), Portalegre (1.9%), Santarem (1.9%), Faro (0.9%), Braganca (0.9%), Aveiro (0.9%) (n = 108) 16 47 21I (Delta) (99.1%), 21A (Delta) (0.9%) (n = 108) 17 | AY.102 82 2021-11-01 2021-12-06 35 6 ALENTEJO (43.9%), LX V TEJO (40.2%), ACORES-RA (7.3%), NORTE (6.1%), CENTRO (1.2%), MADEIRA-RA (1.2%) (n = 82) Evora (26.8%), Lisboa (20.7%), Portalegre (13.4%), Santarem (11.0%), Setubal (8.5%), Acores (7.3%), Braga (3.7%), Beja (3.7%), Porto (2.4%), Madeira (1.2%), Coimbra (1.2%) (n = 82) 11 34 21J (Delta) (100.0%) (n = 82) 18 | AY.121 71 2021-11-02 2021-12-28 56 6 CENTRO (43.7%), ALGARVE (15.5%), LX V TEJO (12.7%), NORTE (11.3%), ALENTEJO (9.9%), MADEIRA-RA (7.0%) (n = 71) Coimbra (25.4%), Faro (15.5%), Lisboa (8.5%), Beja (8.5%), Madeira (7.0%), Viseu (7.0%), Leiria (5.6%), Vila Real (4.2%), Aveiro (4.2%), Castelo Branco (4.2%), Braganca (2.8%), Porto (1.4%), Setubal (1.4%), Braga (1.4%), Evora (1.4%), Santarem (1.4%) (n = 71) 16 39 21J (Delta) (100.0%) (n = 71) 19 | AY.98.1 59 2021-11-01 2021-12-26 55 7 NORTE (33.9%), ALGARVE (23.7%), CENTRO (18.6%), MADEIRA-RA (10.2%), LX V TEJO (6.8%), ALENTEJO (3.4%), ACORES-RA (3.4%) (n = 59) Faro (23.7%), Porto (13.6%), Viseu (13.6%), Madeira (10.2%), Aveiro (10.2%), Vila Real (8.5%), Lisboa (6.8%), Braga (5.1%), Acores (3.4%), Leiria (1.7%), Evora (1.7%), Beja (1.7%) (n = 59) 12 37 21J (Delta) (100.0%) (n = 59) 20 | AY.125 59 2021-11-01 2022-01-03 63 6 LX V TEJO (39.0%), NORTE (33.9%), ALGARVE (15.3%), CENTRO (8.5%), ALENTEJO (1.7%), ACORES-RA (1.7%) (n = 59) Lisboa (27.1%), Porto (23.7%), Faro (15.3%), Santarem (8.5%), Braga (5.1%), Viseu (3.4%), Setubal (3.4%), Vila Real (3.4%), Coimbra (1.7%), Acores (1.7%), Aveiro (1.7%), Leiria (1.7%), Braganca (1.7%), Beja (1.7%) (n = 59) 14 32 21J (Delta) (100.0%) (n = 59) 21 | AY.22 38 2021-11-01 2021-12-21 50 5 LX V TEJO (44.7%), ALENTEJO (15.8%), ACORES-RA (15.8%), CENTRO (13.2%), NORTE (10.5%) (n = 38) Santarem (34.2%), Acores (15.8%), Beja (13.2%), Aveiro (10.5%), Leiria (5.3%), Lisboa (5.3%), Porto (2.6%), Guarda (2.6%), Vila Real (2.6%), Viana do Castelo (2.6%), Evora (2.6%), Braga (2.6%) (n = 38) 12 22 21J (Delta) (100.0%) (n = 38) 22 | AY.46 36 2021-11-08 2022-01-02 55 5 ALGARVE (47.2%), CENTRO (22.2%), LX V TEJO (19.4%), ALENTEJO (5.6%), NORTE (5.6%) (n = 36) Faro (47.2%), Coimbra (13.9%), Leiria (8.3%), Santarem (8.3%), Porto (5.6%), Lisboa (5.6%), Beja (5.6%), Setubal (2.8%), Castelo Branco (2.8%) (n = 36) 9 20 21J (Delta) (100.0%) (n = 36) 23 | AY.127 34 2021-11-01 2022-01-03 63 7 ALENTEJO (32.4%), MADEIRA-RA (29.4%), LX V TEJO (14.7%), ACORES-RA (8.8%), CENTRO (5.9%), NORTE (5.9%), ALGARVE (2.9%) (n = 34) Evora (32.4%), Madeira (29.4%), Lisboa (11.8%), Acores (8.8%), Faro (2.9%), Setubal (2.9%), Braga (2.9%), Aveiro (2.9%), Coimbra (2.9%), Castelo Branco (2.9%) (n = 34) 10 15 21J (Delta) (100.0%) (n = 34) 24 | AY.126 27 2021-11-01 2021-12-27 56 6 NORTE (48.1%), LX V TEJO (29.6%), CENTRO (7.4%), ACORES-RA (7.4%), ALGARVE (3.7%), MADEIRA-RA (3.7%) (n = 27) Lisboa (25.9%), Porto (22.2%), Aveiro (11.1%), Acores (7.4%), Viseu (7.4%), Guarda (7.4%), Madeira (3.7%), Faro (3.7%), Setubal (3.7%), Vila Real (3.7%), Braga (3.7%) (n = 27) 11 17 21J (Delta) (100.0%) (n = 27) 25 | AY.36 26 2021-11-13 2022-01-09 57 4 ACORES-RA (61.5%), NORTE (23.1%), CENTRO (11.5%), ALENTEJO (3.8%) (n = 26) Acores (61.5%), Aveiro (23.1%), Castelo Branco (7.7%), Porto (3.8%), Evora (3.8%) (n = 26) 5 7 21J (Delta) (100.0%) (n = 26) 26 | AY.34.1 24 2021-11-01 2021-12-21 50 5 LX V TEJO (62.5%), ALENTEJO (20.8%), NORTE (8.3%), CENTRO (4.2%), MADEIRA-RA (4.2%) (n = 24) Lisboa (33.3%), Santarem (29.2%), Portalegre (20.8%), Braga (8.3%), Madeira (4.2%), Viseu (4.2%) (n = 24) 6 9 21J (Delta) (100.0%) (n = 24) 27 | AY.117 22 2021-11-01 2021-12-13 42 3 CENTRO (81.8%), ALGARVE (13.6%), NORTE (4.5%) (n = 22) Castelo Branco (77.3%), Faro (13.6%), Porto (4.5%), Leiria (4.5%) (n = 22) 4 9 21J (Delta) (100.0%) (n = 22) 28 | AY.94 21 2021-11-02 2021-12-27 55 4 NORTE (76.2%), CENTRO (14.3%), ALENTEJO (4.8%), LX V TEJO (4.8%) (n = 21) Porto (52.4%), Aveiro (19.0%), Braga (14.3%), Portalegre (4.8%), Viseu (4.8%), Setubal (4.8%) (n = 21) 6 15 21J (Delta) (100.0%) (n = 21) 29 | AY.6 20 2021-11-08 2021-12-14 36 4 ALENTEJO (65.0%), LX V TEJO (15.0%), NORTE (10.0%), ALGARVE (10.0%) (n = 20) Beja (60.0%), Lisboa (15.0%), Faro (10.0%), Porto (5.0%), Evora (5.0%), Aveiro (5.0%) (n = 20) 6 9 21J (Delta) (100.0%) (n = 20) 30 | AY.46.6 20 2021-11-01 2022-01-02 62 5 CENTRO (35.0%), NORTE (30.0%), ALENTEJO (15.0%), MADEIRA-RA (10.0%), ACORES-RA (10.0%) (n = 20) Leiria (20.0%), Porto (15.0%), Evora (15.0%), Madeira (10.0%), Acores (10.0%), Braganca (10.0%), Coimbra (5.0%), Vila Real (5.0%), Guarda (5.0%), Castelo Branco (5.0%) (n = 20) 10 15 21J (Delta) (100.0%) (n = 20) 31 | AY.9.2 18 2021-11-08 2021-12-27 49 4 LX V TEJO (44.4%), ALGARVE (33.3%), ACORES-RA (16.7%), NORTE (5.6%) (n = 18) Faro (33.3%), Lisboa (33.3%), Acores (16.7%), Santarem (11.1%), Porto (5.6%) (n = 18) 5 13 21I (Delta) (100.0%) (n = 18) 32 | AY.5.4 17 2021-11-01 2021-12-12 41 2 NORTE (88.2%), CENTRO (11.8%) (n = 17) Porto (41.2%), Vila Real (35.3%), Coimbra (5.9%), Aveiro (5.9%), Braga (5.9%), Castelo Branco (5.9%) (n = 17) 6 11 21J (Delta) (100.0%) (n = 17) 33 | AY.4.9 16 2021-11-07 2021-12-27 50 3 NORTE (87.5%), ALGARVE (6.2%), LX V TEJO (6.2%) (n = 16) Braga (50.0%), Porto (18.8%), Aveiro (18.8%), Faro (6.2%), Lisboa (6.2%) (n = 16) 5 12 21J (Delta) (100.0%) (n = 16) 34 | AY.120 14 2021-11-01 2021-12-14 43 6 NORTE (50.0%), ALENTEJO (21.4%), ALGARVE (7.1%), CENTRO (7.1%), LX V TEJO (7.1%), ACORES-RA (7.1%) (n = 14) Porto (28.6%), Beja (21.4%), Braganca (14.3%), Acores (7.1%), Faro (7.1%), Coimbra (7.1%), Lisboa (7.1%), Braga (7.1%) (n = 14) 8 10 21J (Delta) (100.0%) (n = 14) 35 | AY.33 14 2021-11-02 2021-12-14 42 4 LX V TEJO (35.7%), ACORES-RA (28.6%), ALENTEJO (28.6%), CENTRO (7.1%) (n = 14) Santarem (35.7%), Acores (28.6%), Evora (28.6%), Coimbra (7.1%) (n = 14) 4 7 21J (Delta) (100.0%) (n = 14) 36 | AY.129 12 2021-11-09 2021-12-26 47 4 ALENTEJO (58.3%), NORTE (16.7%), LX V TEJO (16.7%), MADEIRA-RA (8.3%) (n = 12) Beja (58.3%), Madeira (8.3%), Vila Real (8.3%), Leiria (8.3%), Lisboa (8.3%), Braga (8.3%) (n = 12) 6 7 21J (Delta) (100.0%) (n = 12) 37 | AY.91 12 2021-11-02 2021-12-05 33 5 CENTRO (33.3%), ACORES-RA (25.0%), NORTE (25.0%), MADEIRA-RA (8.3%), LX V TEJO (8.3%) (n = 12) Acores (25.0%), Viseu (25.0%), Porto (16.7%), Madeira (8.3%), Lisboa (8.3%), Aveiro (8.3%), Guarda (8.3%) (n = 12) 7 9 21J (Delta) (100.0%) (n = 12) 38 | AY.4.2.1 11 2021-11-08 2021-12-26 48 5 ALGARVE (27.3%), NORTE (27.3%), ALENTEJO (18.2%), ACORES-RA (18.2%), LX V TEJO (9.1%) (n = 11) Faro (27.3%), Braga (27.3%), Acores (18.2%), Beja (18.2%), Lisboa (9.1%) (n = 11) 5 8 21J (Delta) (100.0%) (n = 11) 39 | AY.99.2 9 2021-11-01 2021-11-28 27 5 LX V TEJO (33.3%), ACORES-RA (22.2%), NORTE (22.2%), ALGARVE (11.1%), CENTRO (11.1%) (n = 9) Setubal (33.3%), Acores (22.2%), Porto (11.1%), Faro (11.1%), Braga (11.1%), Castelo Branco (11.1%) (n = 9) 6 7 21J (Delta) (100.0%) (n = 9) 40 | AY.34 8 2021-11-07 2022-01-03 57 3 ALGARVE (50.0%), NORTE (37.5%), LX V TEJO (12.5%) (n = 8) Faro (50.0%), Porto (25.0%), Vila Real (12.5%), Lisboa (12.5%) (n = 8) 4 5 21J (Delta) (100.0%) (n = 8) 41 | AY.25.1 7 2021-11-29 2022-01-04 36 1 ACORES-RA (100.0%) (n = 7) Acores (100.0%) (n = 7) 1 2 21J (Delta) (100.0%) (n = 7) 42 | AY.25 7 2021-12-06 2022-01-09 34 2 ACORES-RA (85.7%), NORTE (14.3%) (n = 7) Acores (85.7%), Braga (14.3%) (n = 7) 2 5 21J (Delta) (100.0%) (n = 7) 43 | AY.39 6 2021-11-08 2021-11-28 20 3 LX V TEJO (50.0%), NORTE (33.3%), ACORES-RA (16.7%) (n = 6) Lisboa (50.0%), Porto (16.7%), Acores (16.7%), Braga (16.7%) (n = 6) 4 5 21J (Delta) (100.0%) (n = 6) 44 | AY.4.5 6 2021-11-20 2021-12-20 30 2 CENTRO (83.3%), ALGARVE (16.7%) (n = 6) Viseu (50.0%), Castelo Branco (33.3%), Faro (16.7%) (n = 6) 3 4 21J (Delta) (100.0%) (n = 6) 45 | AY.85 6 2021-11-01 2021-11-28 27 3 ALGARVE (50.0%), ALENTEJO (33.3%), NORTE (16.7%) (n = 6) Faro (50.0%), Evora (33.3%), Porto (16.7%) (n = 6) 3 5 21J (Delta) (100.0%) (n = 6) 46 | AY.44 6 2021-11-01 2021-12-05 34 3 CENTRO (50.0%), LX V TEJO (33.3%), NORTE (16.7%) (n = 6) Castelo Branco (50.0%), Setubal (33.3%), Porto (16.7%) (n = 6) 3 6 21J (Delta) (100.0%) (n = 6) 47 | AY.118 6 2021-11-23 2022-01-09 47 3 CENTRO (50.0%), ALENTEJO (33.3%), LX V TEJO (16.7%) (n = 6) Coimbra (33.3%), Viseu (16.7%), Lisboa (16.7%), Evora (16.7%), Beja (16.7%) (n = 6) 5 6 21J (Delta) (100.0%) (n = 6) 48 | AY.4.2.2 6 2021-11-23 2021-12-20 27 1 ALGARVE (100.0%) (n = 6) Faro (100.0%) (n = 6) 1 4 21J (Delta) (100.0%) (n = 6) 49 | AY.4.10 4 2021-12-14 2022-01-04 21 3 MADEIRA-RA (50.0%), CENTRO (25.0%), ALENTEJO (25.0%) (n = 4) Madeira (50.0%), Beja (25.0%), Aveiro (25.0%) (n = 4) 3 3 21J (Delta) (100.0%) (n = 4) 50 | AY.109 4 2021-11-21 2021-12-06 15 2 CENTRO (50.0%), NORTE (50.0%) (n = 4) Coimbra (25.0%), Vila Real (25.0%), Guarda (25.0%), Braga (25.0%) (n = 4) 4 4 21J (Delta) (100.0%) (n = 4) 51 | AY.112 4 2021-11-15 2021-12-26 41 3 NORTE (50.0%), CENTRO (25.0%), LX V TEJO (25.0%) (n = 4) Coimbra (25.0%), Santarem (25.0%), Viseu (25.0%), Aveiro (25.0%) (n = 4) 4 4 21J (Delta) (100.0%) (n = 4) 52 | AY.4.2.3 3 2021-11-19 2022-01-02 44 3 CENTRO (33.3%), NORTE (33.3%), LX V TEJO (33.3%) (n = 3) Viana do Castelo (33.3%), Viseu (33.3%), Setubal (33.3%) (n = 3) 3 3 21J (Delta) (100.0%) (n = 3) 53 | AY.66 3 2021-11-02 2021-11-09 7 1 ALENTEJO (100.0%) (n = 3) Beja (100.0%) (n = 3) 1 1 21I (Delta) (100.0%) (n = 3) 54 | AY.98 3 2021-11-08 2021-12-27 49 1 NORTE (100.0%) (n = 3) Porto (66.7%), Viana do Castelo (33.3%) (n = 3) 2 3 21J (Delta) (100.0%) (n = 3) 55 | AY.123 3 2021-11-02 2021-11-08 6 2 LX V TEJO (66.7%), CENTRO (33.3%) (n = 3) Lisboa (66.7%), Coimbra (33.3%) (n = 3) 2 2 21J (Delta) (100.0%) (n = 3) 56 | AY.116 3 2021-12-13 2021-12-14 1 1 ACORES-RA (100.0%) (n = 3) Acores (100.0%) (n = 3) 1 1 21J (Delta) (100.0%) (n = 3) 57 | AY.106 3 2021-11-28 2021-12-19 21 2 NORTE (66.7%), ALGARVE (33.3%) (n = 3) Porto (66.7%), Faro (33.3%) (n = 3) 2 3 21J (Delta) (100.0%) (n = 3) 58 | AY.133 3 2021-11-14 2021-11-16 2 1 NORTE (100.0%) (n = 3) Vila Real (100.0%) (n = 3) 1 2 21I (Delta) (100.0%) (n = 3) 59 | AY.3 2 2021-11-29 2021-12-13 14 2 CENTRO (50.0%), ALGARVE (50.0%) (n = 2) Coimbra (50.0%), Faro (50.0%) (n = 2) 2 2 21J (Delta) (100.0%) (n = 2) 60 | AY.121.1 2 2021-11-05 2021-11-16 11 2 CENTRO (50.0%), NORTE (50.0%) (n = 2) Porto (50.0%), Guarda (50.0%) (n = 2) 2 2 21J (Delta) (100.0%) (n = 2) 61 | AY.43.1 2 2021-11-06 2021-11-14 8 1 LX V TEJO (100.0%) (n = 2) Lisboa (100.0%) (n = 2) 1 2 21J (Delta) (100.0%) (n = 2) 62 | AY.43.4 2 2022-01-11 2022-01-11 0 1 NORTE (100.0%) (n = 2) Braga (100.0%) (n = 2) 1 1 21J (Delta) (100.0%) (n = 2) 63 | AY.45 2 2021-12-13 2021-12-13 0 1 ALENTEJO (100.0%) (n = 2) Portalegre (50.0%), Beja (50.0%) (n = 2) 2 2 21J (Delta) (100.0%) (n = 2) 64 | AY.122.2 2 2021-11-25 2021-11-25 0 1 NORTE (100.0%) (n = 2) Porto (100.0%) (n = 2) 1 2 21J (Delta) (100.0%) (n = 2) 65 | AY.23 2 2021-11-15 2021-12-14 29 2 CENTRO (50.0%), LX V TEJO (50.0%) (n = 2) Lisboa (50.0%), Aveiro (50.0%) (n = 2) 2 2 21J (Delta) (100.0%) (n = 2) 66 | AY.70 2 2021-11-13 2021-11-16 3 1 NORTE (100.0%) (n = 2) Braga (100.0%) (n = 2) 1 1 21I (Delta) (100.0%) (n = 2) 67 | AY.119 2 2021-11-08 2021-11-15 7 1 ACORES-RA (100.0%) (n = 2) Acores (100.0%) (n = 2) 1 1 21J (Delta) (100.0%) (n = 2) 68 | B.1.36.17 1 2021-11-06 2021-11-06 0 1 LX V TEJO (100.0%) (n = 1) Lisboa (100.0%) (n = 1) 1 1 20A (100.0%) (n = 1) 69 | AY.128 1 2021-11-24 2021-11-24 0 1 ACORES-RA (100.0%) (n = 1) Acores (100.0%) (n = 1) 1 1 21J (Delta) (100.0%) (n = 1) 70 | AY.4.6 1 2021-11-09 2021-11-09 0 1 LX V TEJO (100.0%) (n = 1) Lisboa (100.0%) (n = 1) 1 1 21J (Delta) (100.0%) (n = 1) 71 | AY.88 1 2021-11-01 2021-11-01 0 1 CENTRO (100.0%) (n = 1) Coimbra (100.0%) (n = 1) 1 1 21J (Delta) (100.0%) (n = 1) 72 | AY.15 1 2021-12-04 2021-12-04 0 1 LX V TEJO (100.0%) (n = 1) Santarem (100.0%) (n = 1) 1 1 21J (Delta) (100.0%) (n = 1) 73 | AY.122.4 1 2021-11-18 2021-11-18 0 1 NORTE (100.0%) (n = 1) Viana do Castelo (100.0%) (n = 1) 1 1 21J (Delta) (100.0%) (n = 1) 74 | AY.92 1 2021-12-11 2021-12-11 0 1 CENTRO (100.0%) (n = 1) Coimbra (100.0%) (n = 1) 1 1 21J (Delta) (100.0%) (n = 1) 75 | AY.4.7 1 2021-11-13 2021-11-13 0 1 CENTRO (100.0%) (n = 1) Coimbra (100.0%) (n = 1) 1 1 21J (Delta) (100.0%) (n = 1) 76 | AY.25.3 1 2021-11-14 2021-11-14 0 1 ALGARVE (100.0%) (n = 1) Faro (100.0%) (n = 1) 1 1 21J (Delta) (100.0%) (n = 1) 77 | AY.78 1 2021-12-04 2021-12-04 0 1 ALENTEJO (100.0%) (n = 1) Beja (100.0%) (n = 1) 1 1 21J (Delta) (100.0%) (n = 1) 78 | AY.32 1 2021-11-09 2021-11-09 0 1 CENTRO (100.0%) (n = 1) Coimbra (100.0%) (n = 1) 1 1 21J (Delta) (100.0%) (n = 1) 79 | AY.119.1 1 2021-12-13 2021-12-13 0 1 NORTE (100.0%) (n = 1) Porto (100.0%) (n = 1) 1 1 21J (Delta) (100.0%) (n = 1) 80 | AY.111 1 2021-11-27 2021-11-27 0 1 NORTE (100.0%) (n = 1) Aveiro (100.0%) (n = 1) 1 1 21J (Delta) (100.0%) (n = 1) 81 | AY.4.4 1 2021-11-15 2021-11-15 0 1 ALGARVE (100.0%) (n = 1) Faro (100.0%) (n = 1) 1 1 21J (Delta) (100.0%) (n = 1) 82 | AY.123.1 1 2021-12-27 2021-12-27 0 1 LX V TEJO (100.0%) (n = 1) Leiria (100.0%) (n = 1) 1 1 21J (Delta) (100.0%) (n = 1) 83 | AY.39.2 1 2021-11-07 2021-11-07 0 1 LX V TEJO (100.0%) (n = 1) Lisboa (100.0%) (n = 1) 1 1 21J (Delta) (100.0%) (n = 1) 84 | AY.100 1 2021-12-19 2021-12-19 0 1 LX V TEJO (100.0%) (n = 1) Lisboa (100.0%) (n = 1) 1 1 21J (Delta) (100.0%) (n = 1) 85 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2/output/example2/ReporTree_ncov_PT_lineage_clade_reg_freq.log: -------------------------------------------------------------------------------- 1 | 2 | ******************** running reportree.py ******************** 3 | 4 | ../../ReporTree/reportree.py -m nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.tsv --columns_summary_report n_strain,n_originating_lab --metadata2report Health_region,iso_week -f country == Portugal;Representative_sampling == Weekly;iso_week == 2022W02,2022W03,2022W04,2022W05,2022W06 --frequency-matrix lineage,Health_region:iso_week;clade_membership,Health_region:iso_week --count-matrix lineage,Health_region:iso_week;clade_membership,Health_region:iso_week;Health_region,iso_week -out ReporTree_ncov_PT_lineage_clade_reg_freq 5 | start: 2022-02-23 14:30:15.105965 6 | 7 | Only metadata file provided -> only metadata_report.py will be run: 8 | 9 | 10 | -------------------- metadata_report.py -------------------- 11 | 12 | /mnt/extra_disk_02/TEMP_SARS_CoV_2/ReporTree/ReporTree/scripts/metadata_report.py -m nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.tsv -o ReporTree_ncov_PT_lineage_clade_reg_freq --columns_summary_report n_strain,n_originating_lab --partitions2report all --metadata2report Health_region,iso_week -f country == Portugal;Representative_sampling == Weekly;iso_week == 2022W02,2022W03,2022W04,2022W05,2022W06 --frequency-matrix lineage,Health_region:iso_week;clade_membership,Health_region:iso_week --count-matrix lineage,Health_region:iso_week;clade_membership,Health_region:iso_week;Health_region,iso_week 13 | Getting metadata information... 14 | Filtering metadata for the following parameters: country == Portugal & Representative_sampling == Weekly & iso_week == 2022W02,2022W03,2022W04,2022W05,2022W06 15 | Getting summary stats for the variables specified at '--metadata2report'... 16 | Getting frequency matrix for the variables specified at '--frequency-matrix'... 17 | Creating matrix with the frequency of lineage per Health_region and iso_week... 18 | Creating matrix with the frequency of clade_membership per Health_region and iso_week... 19 | Getting count matrix for the variables specified at '--count-matrix'... 20 | Creating matrix with the count of lineage per Health_region and iso_week... 21 | Creating matrix with the count of clade_membership per Health_region and iso_week... 22 | Creating matrix with the count of Health_region per iso_week... 23 | metadata_report.py is done! 24 | 25 | ------------------------------------------------------------ 26 | 27 | ReporTree is done! If you found any issue please contact us!! 28 | 29 | 30 | End: 2022-02-23 14:30:16.615416 31 | Time elapsed: 0:00:01.509451 32 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2/output/example2/ReporTree_ncov_PT_lineage_clade_reg_freq_Health_region_iso_week_count_matrix.tsv: -------------------------------------------------------------------------------- 1 | iso_week NORTE ALENTEJO ACORES-RA ALGARVE CENTRO MADEIRA-RA LX V TEJO 2 | 2022W02 114 99 55 51 77 36 85 3 | 2022W03 120 73 70 43 73 34 80 4 | 2022W04 100 99 52 65 89 36 74 5 | 2022W05 114 109 56 44 96 3 79 6 | 2022W06 74 67 26 42 56 30 59 7 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2/output/example2/ReporTree_ncov_PT_lineage_clade_reg_freq_Health_region_summary.tsv: -------------------------------------------------------------------------------- 1 | Health_region n_strain n_originating_lab 2 | NORTE 522 17 3 | ALENTEJO 447 13 4 | CENTRO 391 17 5 | LX V TEJO 377 17 6 | ACORES-RA 259 5 7 | ALGARVE 245 5 8 | MADEIRA-RA 139 2 9 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2/output/example2/ReporTree_ncov_PT_lineage_clade_reg_freq_clade_membership_Health_region_iso_week_count_matrix.tsv: -------------------------------------------------------------------------------- 1 | Health_region iso_week 21J (Delta) 21K (Omicron) 21L (Omicron) 2 | ACORES-RA 2022W02 7 48 0 3 | ACORES-RA 2022W03 10 60 0 4 | ACORES-RA 2022W04 5 47 0 5 | ACORES-RA 2022W05 5 49 2 6 | ACORES-RA 2022W06 1 24 1 7 | ALENTEJO 2022W02 2 97 0 8 | ALENTEJO 2022W03 1 72 0 9 | ALENTEJO 2022W04 4 86 9 10 | ALENTEJO 2022W05 0 103 6 11 | ALENTEJO 2022W06 0 56 11 12 | ALGARVE 2022W02 2 48 1 13 | ALGARVE 2022W03 1 42 0 14 | ALGARVE 2022W04 0 58 7 15 | ALGARVE 2022W05 0 38 6 16 | ALGARVE 2022W06 0 32 10 17 | CENTRO 2022W02 2 74 1 18 | CENTRO 2022W03 0 71 2 19 | CENTRO 2022W04 1 83 5 20 | CENTRO 2022W05 0 86 10 21 | CENTRO 2022W06 0 44 12 22 | LX V TEJO 2022W02 1 84 0 23 | LX V TEJO 2022W03 0 75 5 24 | LX V TEJO 2022W04 1 69 4 25 | LX V TEJO 2022W05 0 64 15 26 | LX V TEJO 2022W06 0 46 13 27 | MADEIRA-RA 2022W02 1 35 0 28 | MADEIRA-RA 2022W03 0 34 0 29 | MADEIRA-RA 2022W04 0 36 0 30 | MADEIRA-RA 2022W05 0 1 2 31 | MADEIRA-RA 2022W06 0 24 6 32 | NORTE 2022W02 6 108 0 33 | NORTE 2022W03 2 114 4 34 | NORTE 2022W04 0 96 4 35 | NORTE 2022W05 0 106 8 36 | NORTE 2022W06 0 66 8 37 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2/output/example2/ReporTree_ncov_PT_lineage_clade_reg_freq_clade_membership_Health_region_iso_week_freq_matrix.tsv: -------------------------------------------------------------------------------- 1 | Health_region iso_week 21J (Delta) 21K (Omicron) 21L (Omicron) 2 | ACORES-RA 2022W02 0.12727272727272726 0.8727272727272727 0.0 3 | ACORES-RA 2022W03 0.14285714285714285 0.8571428571428571 0.0 4 | ACORES-RA 2022W04 0.09615384615384616 0.9038461538461539 0.0 5 | ACORES-RA 2022W05 0.08928571428571429 0.875 0.03571428571428571 6 | ACORES-RA 2022W06 0.038461538461538464 0.9230769230769231 0.038461538461538464 7 | ALENTEJO 2022W02 0.020202020202020204 0.9797979797979798 0.0 8 | ALENTEJO 2022W03 0.0136986301369863 0.9863013698630136 0.0 9 | ALENTEJO 2022W04 0.04040404040404041 0.8686868686868687 0.09090909090909091 10 | ALENTEJO 2022W05 0.0 0.944954128440367 0.05504587155963303 11 | ALENTEJO 2022W06 0.0 0.835820895522388 0.16417910447761194 12 | ALGARVE 2022W02 0.0392156862745098 0.9411764705882353 0.0196078431372549 13 | ALGARVE 2022W03 0.023255813953488372 0.9767441860465116 0.0 14 | ALGARVE 2022W04 0.0 0.8923076923076924 0.1076923076923077 15 | ALGARVE 2022W05 0.0 0.8636363636363636 0.13636363636363635 16 | ALGARVE 2022W06 0.0 0.7619047619047619 0.23809523809523808 17 | CENTRO 2022W02 0.025974025974025976 0.961038961038961 0.012987012987012988 18 | CENTRO 2022W03 0.0 0.9726027397260274 0.0273972602739726 19 | CENTRO 2022W04 0.011235955056179775 0.9325842696629213 0.056179775280898875 20 | CENTRO 2022W05 0.0 0.8958333333333334 0.10416666666666667 21 | CENTRO 2022W06 0.0 0.7857142857142857 0.21428571428571427 22 | LX V TEJO 2022W02 0.011764705882352941 0.9882352941176471 0.0 23 | LX V TEJO 2022W03 0.0 0.9375 0.0625 24 | LX V TEJO 2022W04 0.013513513513513514 0.9324324324324325 0.05405405405405406 25 | LX V TEJO 2022W05 0.0 0.810126582278481 0.189873417721519 26 | LX V TEJO 2022W06 0.0 0.7796610169491526 0.22033898305084745 27 | MADEIRA-RA 2022W02 0.027777777777777776 0.9722222222222222 0.0 28 | MADEIRA-RA 2022W03 0.0 1.0 0.0 29 | MADEIRA-RA 2022W04 0.0 1.0 0.0 30 | MADEIRA-RA 2022W05 0.0 0.3333333333333333 0.6666666666666666 31 | MADEIRA-RA 2022W06 0.0 0.8 0.2 32 | NORTE 2022W02 0.05263157894736842 0.9473684210526315 0.0 33 | NORTE 2022W03 0.016666666666666666 0.95 0.03333333333333333 34 | NORTE 2022W04 0.0 0.96 0.04 35 | NORTE 2022W05 0.0 0.9298245614035088 0.07017543859649122 36 | NORTE 2022W06 0.0 0.8918918918918919 0.10810810810810811 37 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2/output/example2/ReporTree_ncov_PT_lineage_clade_reg_freq_iso_week_summary.tsv: -------------------------------------------------------------------------------- 1 | iso_week n_strain n_originating_lab 2 | 2022W02 517 33 3 | 2022W04 515 29 4 | 2022W05 501 30 5 | 2022W03 493 33 6 | 2022W06 354 27 7 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2/output/example2/ReporTree_ncov_PT_lineage_clade_reg_freq_lineage_Health_region_iso_week_count_matrix.tsv: -------------------------------------------------------------------------------- 1 | Health_region iso_week AY.103 AY.124 AY.124.1 AY.4 AY.4.2 AY.42 AY.43 AY.43.4 AY.43.5 AY.5 B.1.617.2 BA.1 BA.1.1 BA.2 2 | ACORES-RA 2022W02 0 0 1 6 0 0 0 0 0 0 0 36 12 0 3 | ACORES-RA 2022W03 0 0 2 8 0 0 0 0 0 0 0 49 11 0 4 | ACORES-RA 2022W04 0 0 1 4 0 0 0 0 0 0 0 39 8 0 5 | ACORES-RA 2022W05 0 0 0 5 0 0 0 0 0 0 0 26 23 2 6 | ACORES-RA 2022W06 0 0 0 1 0 0 0 0 0 0 0 11 13 1 7 | ALENTEJO 2022W02 0 0 0 0 0 0 1 0 0 0 1 80 17 0 8 | ALENTEJO 2022W03 0 0 0 1 0 0 0 0 0 0 0 67 5 0 9 | ALENTEJO 2022W04 0 0 0 1 0 0 1 0 0 0 2 67 19 9 10 | ALENTEJO 2022W05 0 0 0 0 0 0 0 0 0 0 0 76 27 6 11 | ALENTEJO 2022W06 0 0 0 0 0 0 0 0 0 0 0 43 13 11 12 | ALGARVE 2022W02 0 0 0 0 0 0 1 0 0 0 1 44 4 1 13 | ALGARVE 2022W03 0 0 0 0 0 0 0 0 0 1 0 37 5 0 14 | ALGARVE 2022W04 0 0 0 0 0 0 0 0 0 0 0 42 16 7 15 | ALGARVE 2022W05 0 0 0 0 0 0 0 0 0 0 0 26 12 6 16 | ALGARVE 2022W06 0 0 0 0 0 0 0 0 0 0 0 22 10 10 17 | CENTRO 2022W02 1 0 0 1 0 0 0 0 0 0 0 61 13 1 18 | CENTRO 2022W03 0 0 0 0 0 0 0 0 0 0 0 61 10 2 19 | CENTRO 2022W04 0 0 0 0 0 0 0 0 1 0 0 59 24 5 20 | CENTRO 2022W05 0 0 0 0 0 0 0 0 0 0 0 53 33 10 21 | CENTRO 2022W06 0 0 0 0 0 0 0 0 0 0 0 33 11 12 22 | LX V TEJO 2022W02 0 0 0 0 0 0 0 0 0 0 1 79 5 0 23 | LX V TEJO 2022W03 0 0 0 0 0 0 0 0 0 0 0 65 10 5 24 | LX V TEJO 2022W04 0 0 0 0 0 0 0 0 0 0 1 59 10 4 25 | LX V TEJO 2022W05 0 0 0 0 0 0 0 0 0 0 0 50 14 15 26 | LX V TEJO 2022W06 0 0 0 0 0 0 0 0 0 0 0 31 15 13 27 | MADEIRA-RA 2022W02 0 0 0 1 0 0 0 0 0 0 0 33 2 0 28 | MADEIRA-RA 2022W03 0 0 0 0 0 0 0 0 0 0 0 28 6 0 29 | MADEIRA-RA 2022W04 0 0 0 0 0 0 0 0 0 0 0 28 8 0 30 | MADEIRA-RA 2022W05 0 0 0 0 0 0 0 0 0 0 0 0 1 2 31 | MADEIRA-RA 2022W06 0 0 0 0 0 0 0 0 0 0 0 14 10 6 32 | NORTE 2022W02 0 0 0 0 1 2 1 2 0 0 0 89 19 0 33 | NORTE 2022W03 0 1 0 0 0 0 0 0 0 1 0 87 27 4 34 | NORTE 2022W04 0 0 0 0 0 0 0 0 0 0 0 63 33 4 35 | NORTE 2022W05 0 0 0 0 0 0 0 0 0 0 0 75 31 8 36 | NORTE 2022W06 0 0 0 0 0 0 0 0 0 0 0 40 26 8 37 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2/output/example2/ReporTree_ncov_PT_lineage_clade_reg_freq_lineage_Health_region_iso_week_freq_matrix.tsv: -------------------------------------------------------------------------------- 1 | Health_region iso_week AY.103 AY.124 AY.124.1 AY.4 AY.4.2 AY.42 AY.43 AY.43.4 AY.43.5 AY.5 B.1.617.2 BA.1 BA.1.1 BA.2 2 | ACORES-RA 2022W02 0.0 0.0 0.01818181818181818 0.10909090909090909 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6545454545454545 0.21818181818181817 0.0 3 | ACORES-RA 2022W03 0.0 0.0 0.02857142857142857 0.11428571428571428 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.7 0.15714285714285714 0.0 4 | ACORES-RA 2022W04 0.0 0.0 0.019230769230769232 0.07692307692307693 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.75 0.15384615384615385 0.0 5 | ACORES-RA 2022W05 0.0 0.0 0.0 0.08928571428571429 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4642857142857143 0.4107142857142857 0.03571428571428571 6 | ACORES-RA 2022W06 0.0 0.0 0.0 0.038461538461538464 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4230769230769231 0.5 0.038461538461538464 7 | ALENTEJO 2022W02 0.0 0.0 0.0 0.0 0.0 0.0 0.010101010101010102 0.0 0.0 0.0 0.010101010101010102 0.8080808080808081 0.1717171717171717 0.0 8 | ALENTEJO 2022W03 0.0 0.0 0.0 0.0136986301369863 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9178082191780822 0.0684931506849315 0.0 9 | ALENTEJO 2022W04 0.0 0.0 0.0 0.010101010101010102 0.0 0.0 0.010101010101010102 0.0 0.0 0.0 0.020202020202020204 0.6767676767676768 0.1919191919191919 0.09090909090909091 10 | ALENTEJO 2022W05 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6972477064220184 0.24770642201834864 0.05504587155963303 11 | ALENTEJO 2022W06 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6417910447761194 0.19402985074626866 0.16417910447761194 12 | ALGARVE 2022W02 0.0 0.0 0.0 0.0 0.0 0.0 0.0196078431372549 0.0 0.0 0.0 0.0196078431372549 0.8627450980392157 0.0784313725490196 0.0196078431372549 13 | ALGARVE 2022W03 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.023255813953488372 0.0 0.8604651162790697 0.11627906976744186 0.0 14 | ALGARVE 2022W04 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6461538461538462 0.24615384615384617 0.1076923076923077 15 | ALGARVE 2022W05 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5909090909090909 0.2727272727272727 0.13636363636363635 16 | ALGARVE 2022W06 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5238095238095238 0.23809523809523808 0.23809523809523808 17 | CENTRO 2022W02 0.012987012987012988 0.0 0.0 0.012987012987012988 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.7922077922077922 0.16883116883116883 0.012987012987012988 18 | CENTRO 2022W03 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8356164383561644 0.136986301369863 0.0273972602739726 19 | CENTRO 2022W04 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.011235955056179775 0.0 0.0 0.6629213483146067 0.2696629213483146 0.056179775280898875 20 | CENTRO 2022W05 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5520833333333334 0.34375 0.10416666666666667 21 | CENTRO 2022W06 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5892857142857143 0.19642857142857142 0.21428571428571427 22 | LX V TEJO 2022W02 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.011764705882352941 0.9294117647058824 0.058823529411764705 0.0 23 | LX V TEJO 2022W03 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8125 0.125 0.0625 24 | LX V TEJO 2022W04 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.013513513513513514 0.7972972972972973 0.13513513513513514 0.05405405405405406 25 | LX V TEJO 2022W05 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6329113924050633 0.17721518987341772 0.189873417721519 26 | LX V TEJO 2022W06 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5254237288135594 0.2542372881355932 0.22033898305084745 27 | MADEIRA-RA 2022W02 0.0 0.0 0.0 0.027777777777777776 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9166666666666666 0.05555555555555555 0.0 28 | MADEIRA-RA 2022W03 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8235294117647058 0.17647058823529413 0.0 29 | MADEIRA-RA 2022W04 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.7777777777777778 0.2222222222222222 0.0 30 | MADEIRA-RA 2022W05 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3333333333333333 0.6666666666666666 31 | MADEIRA-RA 2022W06 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4666666666666667 0.3333333333333333 0.2 32 | NORTE 2022W02 0.0 0.0 0.0 0.0 0.008771929824561403 0.017543859649122806 0.008771929824561403 0.017543859649122806 0.0 0.0 0.0 0.7807017543859649 0.16666666666666666 0.0 33 | NORTE 2022W03 0.0 0.008333333333333333 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.008333333333333333 0.0 0.725 0.225 0.03333333333333333 34 | NORTE 2022W04 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.63 0.33 0.04 35 | NORTE 2022W05 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6578947368421053 0.2719298245614035 0.07017543859649122 36 | NORTE 2022W06 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5405405405405406 0.35135135135135137 0.10810810810810811 37 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2/output/example3/ReporTree_ncov_PT_get_clusters.log: -------------------------------------------------------------------------------- 1 | 2 | ******************** running reportree.py ******************** 3 | 4 | ../../ReporTree/reportree.py -m nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.tsv -t nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk --columns_summary_report n_strain,lineage,clade_membership,first_seq_date,last_seq_date,timespan_days,n_Health_region,Health_region,S1_mutations --metadata2report S1_mutations --method-threshold root_dist-25-69,avg_clade-1,max_clade-2 -out ReporTree_ncov_PT_get_clusters 5 | start: 2022-02-23 14:22:22.904103 6 | 7 | Tree file provided -> will run partitioning_treecluster.py and metadata_report.py: 8 | 9 | 10 | -------------------- partitioning_treecluster.py -------------------- 11 | 12 | /mnt/extra_disk_02/TEMP_SARS_CoV_2/ReporTree/ReporTree/scripts/partitioning_treecluster.py -t nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters -d 1.0 --method-threshold root_dist-25-69,avg_clade-1,max_clade-2 13 | Assessing tree specificities... 14 | Minimum possible threshold is 1.0 15 | Maximum possible threshold is 69.0 16 | Getting partitions with the following command line(s): 17 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 25.0 -s -inf -m root_dist 18 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 26.0 -s -inf -m root_dist 19 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 27.0 -s -inf -m root_dist 20 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 28.0 -s -inf -m root_dist 21 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 29.0 -s -inf -m root_dist 22 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 30.0 -s -inf -m root_dist 23 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 31.0 -s -inf -m root_dist 24 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 32.0 -s -inf -m root_dist 25 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 33.0 -s -inf -m root_dist 26 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 34.0 -s -inf -m root_dist 27 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 35.0 -s -inf -m root_dist 28 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 36.0 -s -inf -m root_dist 29 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 37.0 -s -inf -m root_dist 30 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 38.0 -s -inf -m root_dist 31 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 39.0 -s -inf -m root_dist 32 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 40.0 -s -inf -m root_dist 33 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 41.0 -s -inf -m root_dist 34 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 42.0 -s -inf -m root_dist 35 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 43.0 -s -inf -m root_dist 36 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 44.0 -s -inf -m root_dist 37 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 45.0 -s -inf -m root_dist 38 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 46.0 -s -inf -m root_dist 39 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 47.0 -s -inf -m root_dist 40 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 48.0 -s -inf -m root_dist 41 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 49.0 -s -inf -m root_dist 42 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 50.0 -s -inf -m root_dist 43 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 51.0 -s -inf -m root_dist 44 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 52.0 -s -inf -m root_dist 45 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 53.0 -s -inf -m root_dist 46 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 54.0 -s -inf -m root_dist 47 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 55.0 -s -inf -m root_dist 48 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 56.0 -s -inf -m root_dist 49 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 57.0 -s -inf -m root_dist 50 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 58.0 -s -inf -m root_dist 51 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 59.0 -s -inf -m root_dist 52 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 60.0 -s -inf -m root_dist 53 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 61.0 -s -inf -m root_dist 54 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 62.0 -s -inf -m root_dist 55 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 63.0 -s -inf -m root_dist 56 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 64.0 -s -inf -m root_dist 57 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 65.0 -s -inf -m root_dist 58 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 66.0 -s -inf -m root_dist 59 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 67.0 -s -inf -m root_dist 60 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 68.0 -s -inf -m root_dist 61 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 69.0 -s -inf -m root_dist 62 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 1.0 -s -inf -m avg_clade 63 | TreeCluster.py -i nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.nwk -o ReporTree_ncov_PT_get_clusters.tsv -t 2.0 -s -inf -m max_clade 64 | 65 | partitioning_treecluster.py is done! 66 | 67 | -------------------- metadata_report.py -------------------- 68 | 69 | /mnt/extra_disk_02/TEMP_SARS_CoV_2/ReporTree/ReporTree/scripts/metadata_report.py -m nextstrain_ncov_PT_INSA_since_Nov2021_release_2022-02-22.tsv -p ReporTree_ncov_PT_get_clusters_partitions.tsv -o ReporTree_ncov_PT_get_clusters --columns_summary_report n_strain,lineage,clade_membership,first_seq_date,last_seq_date,timespan_days,n_Health_region,Health_region,S1_mutations --partitions2report all --metadata2report S1_mutations -f --frequency-matrix no --count-matrix no 70 | Getting information from the partitions table: ReporTree_ncov_PT_get_clusters_partitions.tsv 71 | Getting metadata information... 72 | Samples present in partitions table but missing in metadata table: 73 | Samples not present in partitions table but present in metadata table: 74 | Getting summary stats for the variables specified at '--partitions2report'... 75 | Getting summary stats for the variables specified at '--metadata2report'... 76 | metadata_report.py is done! 77 | 78 | ------------------------------------------------------------ 79 | 80 | ReporTree is done! If you found any issue please contact us!! 81 | 82 | 83 | End: 2022-02-23 14:23:12.347965 84 | Time elapsed: 0:00:49.443862 85 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2/output/example3/ReporTree_ncov_PT_get_clusters_S1_mutations_summary.tsv: -------------------------------------------------------------------------------- 1 | S1_mutations n_strain lineage clade_membership first_seq_date last_seq_date timespan_days n_Health_region Health_region 2 | 27.00 2600 BA.1 (99.8%), BA.1.1 (0.2%) (n = 2600) 21K (Omicron) (100.0%) (n = 2600) 2021-11-19 2022-02-08 81 7 NORTE (21.5%), LX V TEJO (21.3%), ALENTEJO (18.5%), CENTRO (14.5%), ALGARVE (9.0%), ACORES-RA (8.9%), MADEIRA-RA (6.2%) (n = 2600) 3 | 8.00 1847 AY.4 (24.6%), AY.124 (9.7%), AY.43.5 (8.7%), AY.124.1 (7.7%), AY.43 (5.8%), B.1.617.2 (4.9%), AY.26 (4.5%), AY.122 (4.4%), AY.5 (3.8%), AY.42 (3.6%), AY.102 (3.5%), AY.121 (2.9%), AY.98.1 (2.6%), AY.125 (2.5%), AY.127 (1.0%), AY.117 (1.0%), AY.4.9 (0.9%), AY.126 (0.8%), AY.103 (0.7%), AY.9.2 (0.7%), AY.129 (0.6%), AY.120 (0.6%), AY.25.1 (0.4%), AY.118 (0.3%), AY.91 (0.3%), AY.4.5 (0.3%), AY.46.6 (0.3%), AY.44 (0.3%), AY.94 (0.2%), AY.46 (0.2%), AY.112 (0.2%), AY.4.10 (0.2%), AY.99.2 (0.2%), AY.39 (0.2%), AY.66 (0.2%), AY.116 (0.2%), AY.6 (0.1%), AY.119 (0.1%), AY.85 (0.1%), AY.122.2 (0.1%), AY.4.2.3 (0.1%), AY.5.4 (0.1%), AY.106 (0.1%), AY.39.2 (0.1%), AY.100 (0.1%), AY.15 (0.1%), AY.123 (0.1%), AY.128 (0.1%), AY.3 (0.1%), AY.4.7 (0.1%), AY.78 (0.1%) (n = 1847) 21J (Delta) (94.5%), 21I (Delta) (5.4%), 21A (Delta) (0.1%) (n = 1847) 2021-11-01 2022-02-07 98 7 CENTRO (21.2%), NORTE (18.5%), ALENTEJO (15.6%), LX V TEJO (15.0%), ACORES-RA (13.5%), MADEIRA-RA (9.8%), ALGARVE (6.4%) (n = 1847) 4 | 7.00 882 AY.5 (35.3%), AY.43 (31.5%), AY.122 (15.3%), AY.46 (3.5%), AY.6 (2.0%), AY.103 (1.9%), AY.94 (1.9%), AY.5.4 (1.8%), B.1.617.2 (1.6%), AY.46.6 (1.6%), AY.99.2 (0.8%), AY.91 (0.7%), AY.98 (0.3%), AY.43.4 (0.2%), AY.124 (0.2%), AY.43.1 (0.2%), AY.23 (0.2%), AY.129 (0.1%), AY.25.3 (0.1%), AY.121 (0.1%), AY.44 (0.1%), AY.122.4 (0.1%), AY.3 (0.1%), B.1.36.17 (0.1%) (n = 882) 21J (Delta) (99.9%), 20A (0.1%) (n = 882) 2021-11-01 2022-01-24 84 7 NORTE (29.1%), ALGARVE (22.3%), ALENTEJO (16.1%), CENTRO (13.6%), LX V TEJO (12.1%), ACORES-RA (4.0%), MADEIRA-RA (2.7%) (n = 882) 5 | 9.00 879 AY.124 (29.0%), AY.4 (13.1%), AY.103 (11.1%), AY.42 (7.7%), AY.124.1 (6.8%), AY.22 (4.1%), AY.36 (2.8%), AY.34.1 (2.5%), AY.122 (2.4%), AY.26 (2.4%), B.1.617.2 (2.0%), AY.127 (1.9%), AY.121 (1.8%), AY.126 (1.5%), AY.98.1 (1.3%), AY.5 (1.0%), AY.125 (0.9%), AY.34 (0.9%), AY.43 (0.8%), AY.25 (0.8%), AY.102 (0.7%), AY.43.5 (0.6%), AY.117 (0.5%), AY.109 (0.3%), AY.45 (0.2%), AY.106 (0.2%), AY.123 (0.2%), AY.39 (0.2%), AY.70 (0.2%), AY.120 (0.2%), AY.121.1 (0.2%), AY.118 (0.1%), AY.33 (0.1%), AY.4.10 (0.1%), AY.111 (0.1%), AY.4.6 (0.1%), AY.92 (0.1%), AY.123.1 (0.1%), AY.119.1 (0.1%), AY.9.2 (0.1%), AY.32 (0.1%), AY.88 (0.1%), AY.4.4 (0.1%) (n = 879) 21J (Delta) (96.7%), 21I (Delta) (3.3%) (n = 879) 2021-11-01 2022-02-06 97 7 NORTE (28.9%), CENTRO (17.5%), LX V TEJO (15.9%), ALENTEJO (15.8%), ACORES-RA (12.2%), MADEIRA-RA (5.2%), ALGARVE (4.4%) (n = 879) 6 | 28.00 793 BA.1.1 (75.2%), BA.1 (24.8%) (n = 793) 21K (Omicron) (100.0%) (n = 793) 2021-12-02 2022-02-08 68 7 NORTE (28.1%), ALENTEJO (15.5%), CENTRO (15.1%), LX V TEJO (13.5%), ACORES-RA (11.2%), ALGARVE (10.6%), MADEIRA-RA (5.9%) (n = 793) 7 | 10.00 261 AY.4.2 (52.9%), AY.124 (14.9%), AY.33 (4.2%), AY.124.1 (4.2%), AY.102 (3.8%), AY.4 (3.4%), AY.26 (2.3%), AY.125 (1.9%), AY.103 (1.5%), AY.4.2.2 (1.5%), AY.9.2 (1.5%), AY.85 (1.1%), B.1.617.2 (1.1%), AY.34.1 (0.8%), AY.42 (0.8%), AY.22 (0.8%), AY.121 (0.4%), AY.39 (0.4%), AY.34 (0.4%), AY.46 (0.4%), AY.36 (0.4%), AY.4.2.3 (0.4%), AY.120 (0.4%), AY.121.1 (0.4%) (n = 261) 21J (Delta) (96.2%), 21I (Delta) (3.8%) (n = 261) 2021-11-01 2022-01-10 70 7 ALGARVE (29.5%), LX V TEJO (22.2%), NORTE (11.1%), ALENTEJO (10.7%), ACORES-RA (10.7%), CENTRO (10.3%), MADEIRA-RA (5.4%) (n = 261) 8 | 24.00 161 BA.2 (100.0%) (n = 161) 21L (Omicron) (100.0%) (n = 161) 2022-01-02 2022-02-08 37 7 LX V TEJO (24.8%), CENTRO (19.9%), NORTE (16.8%), ALENTEJO (16.1%), ALGARVE (15.5%), MADEIRA-RA (5.0%), ACORES-RA (1.9%) (n = 161) 9 | 11.00 37 AY.4.2.1 (24.3%), AY.4.2 (24.3%), AY.124 (21.6%), AY.133 (8.1%), AY.33 (5.4%), AY.4.2.2 (5.4%), AY.102 (2.7%), AY.85 (2.7%), AY.109 (2.7%), AY.4 (2.7%) (n = 37) 21J (Delta) (91.9%), 21I (Delta) (8.1%) (n = 37) 2021-11-01 2022-01-09 69 6 NORTE (40.5%), LX V TEJO (21.6%), ALGARVE (18.9%), ACORES-RA (8.1%), CENTRO (5.4%), ALENTEJO (5.4%) (n = 37) 10 | 29.00 31 BA.1.1 (77.4%), BA.1 (22.6%) (n = 31) 21K (Omicron) (100.0%) (n = 31) 2021-12-27 2022-02-08 43 7 NORTE (25.8%), CENTRO (22.6%), ALGARVE (22.6%), ALENTEJO (9.7%), ACORES-RA (9.7%), LX V TEJO (6.5%), MADEIRA-RA (3.2%) (n = 31) 11 | 26.00 9 BA.1 (100.0%) (n = 9) 21K (Omicron) (100.0%) (n = 9) 2021-12-20 2022-02-06 48 4 ALENTEJO (33.3%), NORTE (33.3%), LX V TEJO (22.2%), ALGARVE (11.1%) (n = 9) 12 | 12.00 4 AY.4.2.1 (50.0%), AY.33 (25.0%), AY.124 (25.0%) (n = 4) 21J (Delta) (100.0%) (n = 4) 2021-11-21 2021-12-14 23 4 CENTRO (25.0%), ALENTEJO (25.0%), LX V TEJO (25.0%), NORTE (25.0%) (n = 4) 13 | 23.00 4 BA.2 (100.0%) (n = 4) 21L (Omicron) (100.0%) (n = 4) 2022-01-14 2022-01-14 0 4 ALENTEJO (25.0%), CENTRO (25.0%), LX V TEJO (25.0%), NORTE (25.0%) (n = 4) 14 | 6.00 4 AY.4 (50.0%), AY.5 (25.0%), AY.124 (25.0%) (n = 4) 21J (Delta) (100.0%) (n = 4) 2021-11-01 2021-11-09 8 3 LX V TEJO (50.0%), CENTRO (25.0%), ALENTEJO (25.0%) (n = 4) 15 | 25.00 2 BA.1 (100.0%) (n = 2) 21K (Omicron) (100.0%) (n = 2) 2021-12-11 2021-12-21 10 1 LX V TEJO (100.0%) (n = 2) 16 | EMPTY 1 B (100.0%) (n = 1) 19A (100.0%) (n = 1) 2019-12-26 2019-12-26 0 1 EMPTY (100.0%) (n = 1) 17 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2_lineage/input/README.md: -------------------------------------------------------------------------------- 1 | The SARS-CoV-2 tree used in this example, and that corresponded to this metadata file, was downloaded from Taxonium [https://cov2tree.org/] through: 2 | http://hgdownload.soe.ucsc.edu/goldenPath/wuhCor1/UShER_SARS-CoV-2// 3 | on April 24th, 2023 4 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2_lineage/output/SARS-CoV-2_XBB.1.16.1.log: -------------------------------------------------------------------------------- 1 | 2 | ******************** running reportree.py ******************** 3 | 4 | version 2.0.1 last updated on 2023-05-03 5 | 6 | reportree.py -m input/metadata.nwk -t input/tree.nwk --columns_summary_report pango_lineage_usher,country,n_country,first_seq_date,last_seq_date,timespan_days --metadata2report pango_lineage_usher,country,iso_week --method-threshold avg_clade-2 --subset -f pango_lineage_usher == XBB.1.16.1 --count-matrix pango_lineage_usher,country:iso_week -out output/SARS-CoV-2_XBB.1.16.1 7 | start: 2023-05-08 17:10:47.713166 8 | 9 | Tree file provided -> will run partitioning_treecluster.py: 10 | 11 | 12 | -------------------- partitioning_treecluster.py -------------------- 13 | 14 | version 1.2.0 last updated on 2023-05-03 15 | 16 | scripts/partitioning_treecluster.py -t input/tree.nwk -o output/SARS-CoV-2_XBB.1.16.1 -d 1.0 --method-threshold avg_clade-2 --root no -m input/metadata.nwk -f pango_lineage_usher == XBB.1.16.1 17 | 18 | Assessing tree specificities... 19 | Threshold magnitude is 1.0 20 | Maximum possible threshold is 148.0 21 | Getting partitions with the following command line(s): 22 | TreeCluster.py -i input/tree.nwk -o output/SARS-CoV-2_XBB.1.16.1.tsv -t 2.0 -s -inf -m avg_clade 23 | Filtering metadata for the following parameters: pango_lineage_usher == XBB.1.16.1 24 | 25 | partitioning_treecluster.py is done! 26 | 27 | -------------------- metadata_report.py -------------------- 28 | 29 | version 1.0.2 last updated on 2023-03-22 30 | 31 | scripts/metadata_report.py -m input/metadata.nwk -p output/SARS-CoV-2_XBB.1.16.1_partitions.tsv -o output/SARS-CoV-2_XBB.1.16.1 --columns_summary_report pango_lineage_usher,country,n_country,first_seq_date,last_seq_date,timespan_days --partitions2report all --metadata2report pango_lineage_usher,country,iso_week -f pango_lineage_usher == XBB.1.16.1 --frequency-matrix no --count-matrix pango_lineage_usher,country:iso_week 32 | 33 | Getting information from the partitions table: output/SARS-CoV-2_XBB.1.16.1_partitions.tsv 34 | Getting metadata information... 35 | Filtering metadata for the following parameters: pango_lineage_usher == XBB.1.16.1 36 | Samples present in partitions table but missing in metadata table: 37 | Samples not present in partitions table but present in metadata table: Wales/PHWC-PKYEM6/2022|2022-07-07,RNA|OX280165.1|2022-08-01,USA/NY-CDC-LC0880231/2022|OP483531.1|2022-09-10,England/QEUH-3EFF7BD/2022|OX262547.1|2022-07-19,USA/WA-UW-22081116722/2022|OP363400.1|2022-08-11,USA/CA-CDC-VSX-A073760/2023|OQ520548.1|2023-02-13,USA/IL-CDC-LC0868712/2022|OP424260.1|2022-08-29,England/PHEP-YYF6FJ8/2022|2022-10-16,England/DHSC-CYGHP9J/2022|2022-07-08,USA/FL-CDC-STM-XN89XXE83/2022|OP504271.1|2022-09-08,USA/CO-CDPHE-2103515566/2022|OP274885.1|2022-07-14,USA/CA-CDPH-FS25390315/2022|OP421100.1|2022-08-05,England/PHEC-YYGM8PD/2022|2022-07-20,England/PHEP-YYF6EBW/2022|2022-10-13,England/PHEP-YYFMGMJ/2022|2022-07-08,England/PHEC-YYG1RYT/2022|2022-08-02,USA/2207050099/2022|OP821237.1|2022-07-05,USA/UT-UPHL-220702498994/2022|ON942608.2|2022-06-13,Denmark/DCGC-572753/2022|OX331927.1|2022-08-27,Wales/PHWC-PKD7AE/2022|2022-08-30,USA/CA-CDC-STM-67U4G95S2/2022|OP848854.1|2022-11-01,England/PHEC-YYGSD5U/2022|2022-08-07,USA/CA-CDC-LC0853044/2022|OP351168.1|2022-08-22,England/PHEP-YYF4BU6/2022|2022-10-07,England/QEUH-3EF0EB7/2022|OX248843.1|2022-07-15,England/PHEC-YYGESCH/2022|2022-07-15,USA/WA-UW-22072893481/2022|OP348635.1|2022-07-28,England/PHEC-4G0BBZ4D/2021|2021-12-27,England/PHEC-4G0BBZF2/2021|2021-12-27,England/LSPA-3EC2346/2022|OX254160.1|2022-07-04,England/PHEC-4G0BAZ6E/2021|2021-12-27,England/LSPA-3ED5CEB/2022|OX255909.1|2022-07-08,England/DHSC-CYR3H3Q/2022|2022-07-19,Scotland/LSPA-3F22079/2022|OX261900.1|2022-07-26,USA/CA-CDC-LC0873878/2022|OP439403.1|2022-09-04,England/QEUH-3EFCCB1/2022|OX263588.1|2022-07-19,England/LSPA-3F42DE5/2022|OX272326.1|2022-08-02,England/PHEC-YYGFTEX/2022|2022-07-16,England/LSPA-3ED353D/2022|OX256501.1|2022-07-07,England/PHEC-YYGO7QY/2022|2022-07-28,USA/CA-CDC-STM-6T9EWTCFR/2022|OP848911.1|2022-11-02,Switzerland/BL-ETHZ-33576878/2021|OU574723.2|2021-07-25,USA/MA-CDCBI-CRSP_F5DSEHRYSW2L7PPR/2022|OP122708.1|2022-07-19,USA/FL-CDC-LC0794988/2022|OP118419.1|2022-07-18,Denmark/DCGC-570894/2022|OX331015.1|2022-08-22,USA/CO-CDPHE-2103491186/2022|OP355012.1|2022-07-08,England/QEUH-325A2916/2022|OX363537.1|2022-10-06,England/PHEP-YYFI475/2022|2022-09-07,England/PHEP-YYFWP4O/2022|2022-07-27,USA/CA-OC-3484/2022|OP174602.1|2022-07-21,USA/CA-CDPH-7000016858/2022|OQ335728.1|2022-08-02,England/PLYM-3EE3A77/2022|OX244359.1|2022-07-12,USA/OKPHL0026332/2023|OQ630218.1|2023-02-27,England/PLYM-3EE7800/2022|OX248663.1|2022-07-13,England/PHEP-YYFXEAR/2022|2022-07-09,England/PHEC-YYGZXQY/2022|2022-08-22,Denmark/DCGC-565475/2022|OX323420.1|2022-08-14,USA/CO-CDPHE-2103508377/2022|OP275030.1|2022-07-12,England/PHEC-YYGEGTB/2022|2022-07-14,Switzerland/SH-ETHZ-33447906/2021|OU573421.2|2021-07-09,England/PHEC-YYGE47I/2022|2022-07-14,Denmark/DCGC-556743/2022|OX280900.1|2022-07-29,RNA|OX258921.1|2022-07-14,England/PHEC-YYG8K9D/2022|2022-07-18,USA/IL-CDC-LC0865222/2022|OP402430.1|2022-08-28,Wales/PLYM-3F85BA0/2022|OX340062.1|2022-08-29,England/LSPA-3F63F8E/2022|OX319962.1|2022-08-15,USA/NY-CDC-LC0790717/2022|OP119542.1|2022-07-19,England/QEUH-32581124/2022|OX354351.1|2022-09-22,England/PHEC-YY8YRC7/2022|2022-09-29,England/QEUH-325B553A/2022|OX364015.1|2022-10-11,England/PLYM-325D6BB9/2022|OX372260.1|2022-10-28,England/PHEC-YYG1MRN/2022|2022-08-01,England/LSPA-3EC929D/2022|OX257400.1|2022-07-04,USA/MN-MDH-27762/2022|OP030635.1|2022-07-13,England/PLYM-3EEB6D5/2022|OX248484.1|2022-07-15,England/PHEP-YYF5QJR/2022|2022-10-12,England/LSPA-3F445BE/2022|OX319998.1|2022-07-28,England/PHEC-YYGTQ51/2022|2022-07-30,USA/TX-CDC-VSX-A073891/2023|OQ582164.1|2023-02-13,Scotland/LSPA-3257E4A9/2022|OX353398.1|2022-08-27,USA/NJ-CDC-LC0790427/2022|OP118801.1|2022-07-18,EGY/CCHE57357_Wave_3_A_023/2021|MZ364992.1|2021-05-08,England/LSPA-3EC1B12/2022|OX253511.1|2022-07-04,USA/CO-CDPHE-2103496831/2022|OP275105.1|2022-07-13,England/PHEC-3Z07AZ26/2021|2021-12-27,USA/ND-CDC-2-6354247/2022|OP431740.1|2022-08-08,USA/CA-CDC-STM-ZFTJF9A86/2022|OQ114635.1|2022-12-03,England/PHEC-YYG1D9U/2022|2022-08-02,England/PHEP-YYF1ICJ/2022|2022-08-09,England/LSPA-3F68C64/2022|OX319810.1|2022-08-17,Wales/PHWC-PJ97JX/2022|2022-07-02,England/PHEC-3Z079ZB0/2021|2021-12-16,Denmark/DCGC-544914/2022|OX222530.1|2022-07-02,England/PHEP-YYFPZ4I/2022|2022-07-10,England/PHEC-3U048UD4/2021|2021-11-22,England/PHEC-YYGPMHH/2022|2022-07-28,England/PHEC-YYG1KDF/2022|2022-08-03,Denmark/DCGC-555392/2022|OX284255.1|2022-07-24,England/LSPA-3F3916D/2022|OX272005.1|2022-07-29,England/PHEC-YYGSMAT/2022|2022-08-16,England/PHEC-YYGTQWY/2022|2022-07-30,Denmark/DCGC-564698/2022|OX325488.1|2022-08-08,Denmark/DCGC-572500/2022|OX330581.1|2022-08-26,England/PHEC-YYG8ETB/2022|2022-07-17,England/DHSC-CYFKAXF/2022|2022-07-11,Switzerland/VD-ETHZ-33454535/2021|OU573692.2|2021-07-09,USA/UT-UPHL-220812467645/2022|OP294511.2|2022-07-20,Scotland/LSPA-3F1788E/2022|OX259801.1|2022-07-23,Northern_Ireland/NIRE-01dee4/2022|2022-07-01,USA/CA-CDPH-500098396/2022|OQ641277.1|2022-12-26,USA/IN-CDC-STM-FT6S9GDUQ/2022|OP794952.1|2022-09-28,Denmark/DCGC-556774/2022|OX279321.1|2022-07-29,USA/UT-UPHL-230118688287/2023|OQ336429.2|2023-01-07,England/PHEC-YYGOMQ1/2022|2022-07-28,England/LSPA-3EC1769/2022|OX254428.1|2022-07-04,England/PHEP-YYF3EMK/2022|2022-09-29,England/PHEC-3Z07AZ08/2021|2021-12-27,USA/NC-CDC-LC0819518/2022|OP240328.1|2022-08-02,England/PHEC-YYGSPAX/2022|2022-08-16,England/PLYM-3EF0646/2022|OX248097.1|2022-07-16,England/PHEP-YYF9FQ5/2022|2022-11-02,England/QEUH-3EE388C/2022|OX343122.1|2022-07-06,England/PHEC-YYG1DH3/2022|2022-08-02,Scotland/SCOT-15129/2022|OX425511.1|2022-06-26,England/LSPA-3F4A688/2022|OX281701.1|2022-08-03,USA/CO-CDPHE-2103476136/2022|OP280540.1|2022-06-28,England/PHEC-YYG8GBR/2022|2022-07-14,England/LSPA-3F05E90/2022|OX264902.1|2022-07-22,England/PHEC-YYGWD8Y/2022|2022-08-03,England/LSPA-3EC1620/2022|OX254142.1|2022-07-04,England/LSPA-3F58CFE/2022|OX298511.1|2022-08-10,USA/CA-CDC-VSX-A063413/2023|OQ691934.1|2023-03-17,England/QEUH-3ED1C49/2022|OX243245.1|2022-07-06,Wales/PHWC-PKDAST/2022|2022-08-29,USA/IL-CDC-STM-8FR8ZPYN4/2022|OP732470.1|2022-10-03,England/PHEP-YYFOWES/2022|2022-07-19,England/QEUH-3EFC273/2022|OX261988.1|2022-07-19,England/LSPA-325ABE69/2022|OX363762.1|2022-10-10,England/PHEP-YYFSOT5/2022|2022-09-18,England/PHEC-YYG8K4P/2022|2022-07-17,England/PHEP-YYFPQ46/2022|2022-07-11,England/QEUH-3EFC176/2022|OX263308.1|2022-07-19,USA/CO-CDPHE-2103479745/2022|OP278496.1|2022-06-28,England/LSPA-3EC1787/2022|OX251553.1|2022-07-04,USA/CT_1139/2021|OL467310.1|2021-11-01,USA/LA-EVTL2694/2021|OP398720.1|2021-04-22,RNA|OX259219.1|2022-07-04,England/PHEP-YYFWGKA/2022|2022-08-11,USA/CO-CDPHE-2103388652/2022|OP049366.1|2022-06-20,Denmark/DCGC-570201/2022|OX333187.1|2022-08-22,USA/UT-UPHL-230106408183/2022|OQ268412.2|2022-12-23,England/LSPA-3EC1918/2022|OX272614.1|2022-07-04,England/LSPA-3F2B78A/2022|OX271580.1|2022-07-27,England/PHEP-YYFOJQI/2022|2022-07-23,USA/CA-CDC-807969704/2023|OQ381321.1|2023-01-14,RNA|OX346154.1|2022-09-21,England/LSPA-3259AE6D/2022|OX356285.1|2022-10-04,USA/VA-CDC-LC0775270/2022|OP065676.1|2022-07-14,Denmark/DCGC-572343/2022|OX329472.1|2022-08-27,England/PLYM-15208D2D/2022|OX341171.1|2022-09-12,Denmark/DCGC-564569/2022|OX323642.1|2022-08-08,England/PHEC-YY8FCF3/2022|2022-09-12,Denmark/DCGC-561220/2022|OX295249.1|2022-08-02,Denmark/DCGC-569209/2022|OX325509.1|2022-08-19,USA/IL-CDC-LC0875140/2022|OP463192.1|2022-09-01,England/LSPA-3F68F89/2022|OX319403.1|2022-08-17 38 | Getting summary stats for the variables specified at '--partitions2report'... 39 | Getting summary stats for the variables specified at '--metadata2report'... 40 | Getting count matrix for the variables specified at '--count-matrix'... 41 | Creating matrix with the count of pango_lineage_usher per country and iso_week... 42 | metadata_report.py is done! 43 | 44 | ------------------------------------------------------------ 45 | 46 | ReporTree is done! If you found any issue please contact us!! 47 | 48 | 49 | End: 2023-05-08 18:01:57.288925 50 | Time elapsed: 0:51:09.575759 51 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2_lineage/output/SARS-CoV-2_XBB.1.16.1_clusterComposition.tsv: -------------------------------------------------------------------------------- 1 | partition cluster cluster_length samples 2 | avg_clade-2x1.0 singleton_243233 1 USA/NY-CDC-QDX48043246/2023|OQ713513.1|2023-03-17 3 | avg_clade-2x1.0 singleton_243260 1 USA/VA-CDC-LC1030341/2023|OQ712355.1|2023-03-17 4 | avg_clade-2x1.0 singleton_243311 1 IND/2963/2023|OQ701311.1|2023-03-13 5 | avg_clade-2x1.0 singleton_243491 1 England/PHEC-YYE19M6/2023|2023-04-04 6 | avg_clade-2x1.0 singleton_243809 1 IND/2941/2023|OQ701295.1|2023-03-08 7 | avg_clade-2x1.0 cluster_337704 2 USA/GA-CDC-QDX48496722/2023|OQ825584.1|2023-03-29,USA/GA-CDC-QDX48496695/2023|OQ825368.1|2023-03-28 8 | avg_clade-2x1.0 singleton_243879 1 USA/WA-CDC-UW23032209634/2023|OQ833327.1|2023-03-22 9 | avg_clade-2x1.0 cluster_336461 6 Wales/PHWC-PKQ9WA/2023|2023-03-22,Wales/PHWC-PKXPUK/2023|2023-03-29,Wales/PHWC-PKXKYW/2023|2023-03-30,Wales/PHWC-PKX8C9/2023|2023-04-01,CHN/HG20230331-145/2023|C_AA008528.1|2023-03-30,USA/GA-CDC-QDX48586938/2023|OQ825934.1|2023-04-01 10 | avg_clade-2x1.0 singleton_243360 1 IND/2939/2023|OQ701293.1|2023-03-07 11 | avg_clade-2x1.0 cluster_337010 8 IND/2922/2023|OQ701280.1|2023-03-08,USA/OH-CDC-QDX47973871/2023|OQ712825.1|2023-03-14,USA/WA-CDC-UW23032466470/2023|OQ833399.1|2023-03-24,USA/RI-CDC-LC1036205/2023|OQ832920.1|2023-04-03,USA/CA-CDC-QDX48637276/2023|OQ827117.1|2023-03-29,CHN/SH-XG2304-2050/2023|C_AA010908.1|2023-03-30,England/PHEP-YYG1ZEQ/2023|2023-04-07,USA/PA-CDC-LC1027064/2023|OQ660759.1|2023-03-05 12 | avg_clade-2x1.0 cluster_336719 5 Scotland/SCOT-28022/2023|OX462557.1|2023-03-03,Scotland/SCOT-28024/2023|OX462598.1|2023-02-28,Scotland/SCOT-28024/2023|2023-02-28,Scotland/SCOT-28022/2023|2023-03-03,USA/FL-CDC-QDX47772044/2023|OQ705280.1|2023-03-11 13 | avg_clade-2x1.0 singleton_243434 1 IND/2909/2023|OQ701271.1|2023-03-08 14 | avg_clade-2x1.0 cluster_336546 2 USA/TX-CDC-QDX47771482/2023|OQ684985.1|2023-03-11,USA/TX-CDC-QDX47594614/2023|OQ655600.1|2023-03-05 15 | avg_clade-2x1.0 singleton_243761 1 USA/WA-CDC-UW23032515183/2023|OQ833441.1|2023-03-25 16 | avg_clade-2x1.0 cluster_336868 2 USA/GA-CDC-QDX48733575/2023|OQ827534.1|2023-04-02,USA/NY-CDC-QDX48144042/2023|OQ728669.1|2023-03-20 17 | avg_clade-2x1.0 cluster_337245 2 England/PHEC-YYEUJUB/2023|2023-04-06,England/PHEC-YYE1H7H/2023|2023-04-05 18 | avg_clade-2x1.0 singleton_243032 1 USA/IL-CDC-QDX47312545/2023|OQ610893.1|2023-02-27 19 | avg_clade-2x1.0 singleton_243918 1 USA/WA-CDC-LC1036446/2023|OQ852081.1|2023-03-31 20 | avg_clade-2x1.0 cluster_337009 3 England/PHEC-YYEU91P/2023|2023-04-06,USA/WA-CDC-UW23040359572/2023|OQ799284.1|2023-04-03,England/PHEP-YYG1DWJ/2023|2023-03-19 21 | avg_clade-2x1.0 cluster_336960 2 IND/2958/2023|OQ701308.1|2023-03-14,IND/2885/2023|OQ701259.1|2023-03-10 22 | avg_clade-2x1.0 cluster_336630 2 England/PHEP-YYGURE9/2023|2023-04-10,England/PHEC-YYEQJUC/2023|2023-03-18 23 | avg_clade-2x1.0 cluster_337562 4 England/BRBR-3270070B/2023|OX454010.1|2023-03-10,England/BRBR-326F8A4A/2023|OX454078.1|2023-03-07,England/PHEC-YYET1UH/2023|2023-03-26,USA/NJ-CDC-QDX48144032/2023|OQ728603.1|2023-03-20 24 | avg_clade-2x1.0 singleton_243740 1 USA/NJ-CDC-LC1035809/2023|OQ832971.1|2023-04-04 25 | avg_clade-2x1.0 cluster_336753 5 IND/2911/2023|OQ701273.1|2023-03-06,IND/2948/2023|OQ701302.1|2023-03-10,IND/2915/2023|OQ701277.1|2023-03-02,IND/NGS_-_1625/2023|OQ852629.1|2023-03-07,IND/2912/2023|OQ701274.1|2023-03-07 26 | avg_clade-2x1.0 cluster_337887 3 USA/TX-CDC-QDX48733706/2023|OQ828034.1|2023-04-04,USA/HI-CDC-QDX48637207/2023|OQ827270.1|2023-03-31,USA/NJ-CDC-LC1035908/2023|OQ832960.1|2023-04-04 27 | avg_clade-2x1.0 cluster_337095 2 IND/2892/2023|OQ701260.1|2023-03-14,IND/2957/2023|OQ701307.1|2023-03-10 28 | avg_clade-2x1.0 cluster_336869 3 IND/2918/2023|OQ701278.1|2023-03-02,IND/2924/2023|OQ701282.1|2023-03-08,IND/2819/2023|OQ701227.1|2023-03-11 29 | avg_clade-2x1.0 singleton_243590 1 England/PHEC-YYEWGOE/2023|2023-04-10 30 | avg_clade-2x1.0 cluster_336958 2 Scotland/SCOT-29102/2023|2023-03-29,USA/MN-CDC-QDX48551717/2023|OQ825384.1|2023-03-28 31 | avg_clade-2x1.0 cluster_337275 2 USA/CA-CDC-LC1033870/2023|OQ748605.1|2023-03-29,USA/FL-CDC-QDX48415259/2023|OQ825235.1|2023-03-27 32 | avg_clade-2x1.0 singleton_243551 1 USA/VA-CDC-QDX48464008/2023|OQ825436.1|2023-03-28 33 | avg_clade-2x1.0 cluster_337011 4 USA/FL-CDC-QDX48587493/2023|OQ827224.1|2023-03-30,USA/PA-CDC-QDX48587130/2023|OQ827341.1|2023-03-31,USA/IL-CDC-QDX48685611/2023|OQ827947.1|2023-04-03,USA/MO-CDC-QDX47771568/2023|OQ684947.1|2023-03-11 34 | avg_clade-2x1.0 cluster_337435 2 CHN/SH-PD202304070006/2023|C_AA010882.1|2023-04-05,CHN/SH-PD202304070004/2023|C_AA010881.1|2023-04-05 35 | avg_clade-2x1.0 singleton_243716 1 USA/MA-CDCBI-CRSP_VEYXSGSUMBUGV47P/2023|OQ727705.1|2023-03-20 36 | avg_clade-2x1.0 singleton_243234 1 IND/2914/2023|OQ701276.1|2023-03-07 37 | avg_clade-2x1.0 singleton_243333 1 IND/2923/2023|OQ701281.1|2023-02-28 38 | avg_clade-2x1.0 cluster_336348 3 CHN/SH-HG20230404-167/2023|C_AA009133.1|2023-04-03,USA/PA-CDC-LC1034152/2023|OQ802536.1|2023-03-29,IND/2960/2023|OQ701309.1|2023-03-10 39 | avg_clade-2x1.0 singleton_243901 1 CHN/SH-HG20230410-190/2023|C_AA010850.1|2023-04-07 40 | avg_clade-2x1.0 cluster_336959 2 IND/2947/2023|OQ701301.1|2023-03-10,IND/2820/2023|OQ701228.1|2023-03-04 41 | avg_clade-2x1.0 singleton_243783 1 England/PHEC-YYEUK6K/2023|2023-04-06 42 | avg_clade-2x1.0 cluster_337886 35 USA/NJ-CDC-LC1036238/2023|OQ832843.1|2023-04-01,CHN/SH-HG20230412-207/2023|C_AA010866.1|2023-04-11,England/PHEC-YYEOSGI/2023|2023-03-25,USA/NY-CDC-QDX48415136/2023|OQ825249.1|2023-03-27,CHN/ZJCDC751/2023|C_AA009615.1|2023-04-02,CHN/ZJCDC754/2023|C_AA009618.1|2023-04-02,CHN/ZJCDC750/2023|C_AA009614.1|2023-04-02,CHN/ZJCDC752/2023|C_AA009616.1|2023-04-02,CHN/ZJCDC710/2023|C_AA008792.1|2023-04-02,CHN/ZJCDC709/2023|C_AA008791.1|2023-04-02,CHN/ZJCDC708/2023|C_AA008790.1|2023-04-02,CHN/ZJCDC712/2023|C_AA008794.1|2023-04-02,CHN/ZJCDC760/2023|C_AA009624.1|2023-03-31,CHN/ZJCDC718/2023|C_AA008800.1|2023-03-31,CHN/ZJCDC753/2023|C_AA009617.1|2023-04-02,CHN/ZJCDC711/2023|C_AA008793.1|2023-04-02,Scotland/SCOT-28892/2023|OX463564.1|2023-03-30,Scotland/SCOT-28892/2023|2023-03-30,Wales/PHWC-PKX1RR/2023|2023-04-08,Switzerland/ZH-UZH-IMV-3ba697c0/2023|OX463582.1|2023-03-26,England/PHEP-YYGT78C/2023|2023-03-21,USA/NJ-CDC-QDX48144116/2023|OQ728515.1|2023-03-20,USA/NY-CDC-QDX48094657/2023|OQ728319.1|2023-03-19,England/BRBR-3270A1F4/2023|OX458423.1|2023-03-17,England/BRBR-3270A1C7/2023|OX458658.1|2023-03-17,England/BRBR-3270AACC/2023|OX458886.1|2023-03-17,USA/NJ-CDC-QDX48043255/2023|OQ713510.1|2023-03-17,USA/NY-CDC-QDX47869972/2023|OQ705510.1|2023-03-13,USA/WA-UW-23031628432/2023|OQ704103.1|2023-03-16,England/PHEC-YYEXNJG/2023|2023-03-20,England/PHEC-YYEXNRO/2023|2023-03-20,England/PHEC-YYEXNCY/2023|2023-03-20,England/BRBR-3270A1F4/2023|2023-03-17,England/BRBR-3270A1C7/2023|2023-03-17,USA/CA-CDC-LC1022590/2023|OQ601346.1|2023-02-28 43 | avg_clade-2x1.0 singleton_243385 1 IND/2946/2023|OQ701300.1|2023-03-09 44 | avg_clade-2x1.0 singleton_243453 1 USA/NC-CDC-QDX48415072/2023|OQ783974.1|2023-03-27 45 | avg_clade-2x1.0 singleton_243433 1 USA/CA-CDC-QDX48365602/2023|OQ782454.1|2023-03-21 46 | avg_clade-2x1.0 cluster_336833 4 Wales/PHWC-PKP8ZC/2023|2023-03-01,Wales/PHWC-PKPG7B/2023|2023-03-01,Wales/PHWC-PKPDPF/2023|2023-03-04,USA/2255668/2023|OQ781076.1|2023-03-20 47 | avg_clade-2x1.0 cluster_337401 2 England/BRBR-3271393C/2023|OX462110.1|2023-03-25,England/BRBR-3271393C/2023|2023-03-25 48 | avg_clade-2x1.0 cluster_336957 3 USA/MD-CDC-VSX-A065194/2023|OQ748665.1|2023-03-23,USA/NJ-CDC-QDX48733632/2023|OQ828022.1|2023-04-04,USA/NJ-CDC-VSX-A065282/2023|OQ748806.1|2023-03-27 49 | avg_clade-2x1.0 singleton_243411 1 USA/CA-CDC-QDX48464119/2023|OQ783898.1|2023-03-27 50 | avg_clade-2x1.0 singleton_243717 1 IND/2952/2023|OQ701305.1|2023-03-07 51 | avg_clade-2x1.0 singleton_243570 1 USA/TX-CDC-QDX48587216/2023|OQ825723.1|2023-03-30 52 | avg_clade-2x1.0 cluster_336381 23 England/PHEC-YYETNBT/2023|2023-03-27,USA/CA-CDC-QDX48366073/2023|OQ783268.1|2023-03-22,USA/CA-CDC-QDX48366074/2023|OQ783289.1|2023-03-22,USA/TX-CDC-QDX48496835/2023|OQ825114.1|2023-03-26,England/BRBR-3270AB32/2023|OX458843.1|2023-03-17,England/BRBR-3270AB32/2023|2023-03-17,CHN/SH-HG20230411-201/2023|C_AA010860.1|2023-04-10,England/QEUH-326F4BC3/2023|OX452305.1|2023-03-02,England/BRBR-3270AA08/2023|OX458743.1|2023-03-17,England/BRBR-3270A18B/2023|OX458889.1|2023-03-17,England/BRBR-3270AB05/2023|OX458344.1|2023-03-17,England/BRBR-3270AA53/2023|OX458493.1|2023-03-17,England/BRBR-3270A992/2023|OX458355.1|2023-03-17,England/BRBR-3270ABAB/2023|OX458891.1|2023-03-17,England/BRBR-3270AA08/2023|2023-03-17,England/BRBR-3270A18B/2023|2023-03-17,England/BRBR-3270AB05/2023|2023-03-17,England/BRBR-3270AA53/2023|2023-03-17,England/BRBR-3270A992/2023|2023-03-17,England/BRBR-3270ABAB/2023|2023-03-17,England/QEUH-326F76ED/2023|OX455077.1|2023-03-02,IND/2936/2023|OQ701290.1|2023-03-03,IND/2925/2023|OQ701283.1|2023-03-08 53 | avg_clade-2x1.0 singleton_243671 1 USA/WA-UW-23031107790/2023|OQ673701.1|2023-03-11 54 | avg_clade-2x1.0 singleton_243474 1 England/PHEC-YYEUJGH/2023|2023-04-09 55 | avg_clade-2x1.0 cluster_336913 2 IND/2890/2023|OQ704263.1|2023-03-13,IND/2967/2023|OQ701314.1|2023-03-10 56 | avg_clade-2x1.0 cluster_337053 9 IND/NGS_-_1536/2023|OQ852625.1|2023-02-19,IND/NGS_-_1490/2023|OQ852620.1|2023-02-05,IND/2900/2023|OQ701265.1|2023-03-05,IND/2950/2023|OQ701304.1|2023-03-13,USA/WI-CDC-VSX-A065209/2023|OQ748644.1|2023-03-22,IND/2907/2023|OQ701270.1|2023-03-06,IND/2872/2023|OQ701252.1|2023-03-02,IND/2938/2023|OQ701292.1|2023-03-07,IND/2937/2023|OQ701291.1|2023-03-02 57 | avg_clade-2x1.0 cluster_336504 3 England/PHEC-YYEU1C8/2023|2023-04-06,USA/WA-CDC-UW23030704434/2023|OQ674602.1|2023-03-07,USA/TX-CDC-VSX-A064973/2023|OQ691835.1|2023-03-15 58 | avg_clade-2x1.0 singleton_243261 1 IND/2949/2023|OQ701303.1|2023-03-10 59 | avg_clade-2x1.0 singleton_243235 1 IND/2928/2023|OQ701286.1|2023-03-08 60 | avg_clade-2x1.0 singleton_243649 1 USA/WA-CDC-UW23032178303/2023|OQ833286.1|2023-03-21 61 | avg_clade-2x1.0 singleton_243139 1 USA/MI-MDHHS-SC44070/2023|OQ682568.1|2023-03-06 62 | avg_clade-2x1.0 singleton_243856 1 USA/WI-CDC-LC1036965/2023|OQ852291.1|2023-04-08 63 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2_lineage/output/SARS-CoV-2_XBB.1.16.1_country_summary.tsv: -------------------------------------------------------------------------------- 1 | country pango_lineage_usher first_seq_date last_seq_date timespan_days 2 | China XBB.1.16.1 (100.0%) (n = 20) 2023-03-30 2023-04-11 12 3 | USA XBB.1.16.1 (100.0%) (n = 61) 2023-02-27 2023-04-08 40 4 | Scotland XBB.1.16.1 (100.0%) (n = 7) 2023-02-28 2023-03-30 30 5 | India XBB.1.16.1 (100.0%) (n = 38) 2023-02-05 2023-03-14 37 6 | Wales XBB.1.16.1 (100.0%) (n = 8) 2023-03-01 2023-04-08 38 7 | Switzerland XBB.1.16.1 (100.0%) (n = 1) 2023-03-26 2023-03-26 0 8 | England XBB.1.16.1 (100.0%) (n = 44) 2023-03-02 2023-04-10 39 9 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2_lineage/output/SARS-CoV-2_XBB.1.16.1_iso_week_summary.tsv: -------------------------------------------------------------------------------- 1 | iso_week pango_lineage_usher country n_country first_seq_date last_seq_date timespan_days 2 | 2023W10 XBB.1.16.1 (100.0%) (n = 31) India (74.2%), USA (19.4%), England (6.5%) (n = 31) 3 2023-03-06 2023-03-11 5 3 | 2023W12 XBB.1.16.1 (100.0%) (n = 25) USA (60.0%), England (32.0%), Wales (4.0%), Switzerland (4.0%) (n = 25) 4 2023-03-20 2023-03-26 6 4 | 2023W14 XBB.1.16.1 (100.0%) (n = 21) USA (38.1%), England (38.1%), China (19.0%), Wales (4.8%) (n = 21) 4 2023-04-03 2023-04-09 6 5 | 2023W05 XBB.1.16.1 (100.0%) (n = 1) India (100.0%) (n = 1) 1 2023-02-05 2023-02-05 0 6 | 2023W13 XBB.1.16.1 (100.0%) (n = 41) USA (48.8%), China (34.1%), Scotland (7.3%), Wales (7.3%), England (2.4%) (n = 41) 5 2023-03-27 2023-04-02 6 7 | 2023W07 XBB.1.16.1 (100.0%) (n = 1) India (100.0%) (n = 1) 1 2023-02-19 2023-02-19 0 8 | 2023W09 XBB.1.16.1 (100.0%) (n = 21) India (38.1%), USA (19.0%), Scotland (19.0%), Wales (14.3%), England (9.5%) (n = 21) 5 2023-02-27 2023-03-05 6 9 | 2023W11 XBB.1.16.1 (100.0%) (n = 34) England (61.8%), USA (23.5%), India (14.7%) (n = 34) 3 2023-03-13 2023-03-19 6 10 | 2023W15 XBB.1.16.1 (100.0%) (n = 4) China (50.0%), England (50.0%) (n = 4) 2 2023-04-10 2023-04-11 1 11 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2_lineage/output/SARS-CoV-2_XBB.1.16.1_pango_lineage_usher_country_iso_week_count_matrix.tsv: -------------------------------------------------------------------------------- 1 | country iso_week XBB.1.16.1 2 | China 2023W05 0 3 | China 2023W07 0 4 | China 2023W09 0 5 | China 2023W10 0 6 | China 2023W11 0 7 | China 2023W12 0 8 | China 2023W13 14 9 | China 2023W14 4 10 | China 2023W15 2 11 | England 2023W05 0 12 | England 2023W07 0 13 | England 2023W09 2 14 | England 2023W10 2 15 | England 2023W11 21 16 | England 2023W12 8 17 | England 2023W13 1 18 | England 2023W14 8 19 | England 2023W15 2 20 | India 2023W05 1 21 | India 2023W07 1 22 | India 2023W09 8 23 | India 2023W10 23 24 | India 2023W11 5 25 | India 2023W12 0 26 | India 2023W13 0 27 | India 2023W14 0 28 | India 2023W15 0 29 | Scotland 2023W05 0 30 | Scotland 2023W07 0 31 | Scotland 2023W09 4 32 | Scotland 2023W10 0 33 | Scotland 2023W11 0 34 | Scotland 2023W12 0 35 | Scotland 2023W13 3 36 | Scotland 2023W14 0 37 | Scotland 2023W15 0 38 | Switzerland 2023W05 0 39 | Switzerland 2023W07 0 40 | Switzerland 2023W09 0 41 | Switzerland 2023W10 0 42 | Switzerland 2023W11 0 43 | Switzerland 2023W12 1 44 | Switzerland 2023W13 0 45 | Switzerland 2023W14 0 46 | Switzerland 2023W15 0 47 | USA 2023W05 0 48 | USA 2023W07 0 49 | USA 2023W09 4 50 | USA 2023W10 6 51 | USA 2023W11 8 52 | USA 2023W12 15 53 | USA 2023W13 20 54 | USA 2023W14 8 55 | USA 2023W15 0 56 | Wales 2023W05 0 57 | Wales 2023W07 0 58 | Wales 2023W09 3 59 | Wales 2023W10 0 60 | Wales 2023W11 0 61 | Wales 2023W12 1 62 | Wales 2023W13 3 63 | Wales 2023W14 1 64 | Wales 2023W15 0 65 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2_lineage/output/SARS-CoV-2_XBB.1.16.1_pango_lineage_usher_summary.tsv: -------------------------------------------------------------------------------- 1 | pango_lineage_usher country n_country first_seq_date last_seq_date timespan_days 2 | XBB.1.16.1 USA (34.1%), England (24.6%), India (21.2%), China (11.2%), Wales (4.5%), Scotland (3.9%), Switzerland (0.6%) (n = 179) 7 2023-02-05 2023-04-11 65 3 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2_lineage/output/SARS-CoV-2_XBB.1.16.1_partitions_summary.tsv: -------------------------------------------------------------------------------- 1 | partition cluster cluster_length samples pango_lineage_usher country n_country first_seq_date last_seq_date timespan_days 2 | avg_clade-2x1.0 cluster_337401 2 England/BRBR-3271393C/2023|2023-03-25,England/BRBR-3271393C/2023|OX462110.1|2023-03-25 XBB.1.16.1 (100.0%) (n = 2) England (100.0%) (n = 2) 1 2023-03-25 2023-03-25 0 3 | avg_clade-2x1.0 cluster_337562 4 England/BRBR-326F8A4A/2023|OX454078.1|2023-03-07,England/BRBR-3270070B/2023|OX454010.1|2023-03-10,England/PHEC-YYET1UH/2023|2023-03-26,USA/NJ-CDC-QDX48144032/2023|OQ728603.1|2023-03-20 XBB.1.16.1 (100.0%) (n = 4) England (75.0%), USA (25.0%) (n = 4) 2 2023-03-07 2023-03-26 19 4 | avg_clade-2x1.0 cluster_336461 6 CHN/HG20230331-145/2023|C_AA008528.1|2023-03-30,USA/GA-CDC-QDX48586938/2023|OQ825934.1|2023-04-01,Wales/PHWC-PKQ9WA/2023|2023-03-22,Wales/PHWC-PKX8C9/2023|2023-04-01,Wales/PHWC-PKXKYW/2023|2023-03-30,Wales/PHWC-PKXPUK/2023|2023-03-29 XBB.1.16.1 (100.0%) (n = 6) Wales (66.7%), China (16.7%), USA (16.7%) (n = 6) 3 2023-03-22 2023-04-01 10 5 | avg_clade-2x1.0 cluster_336504 3 England/PHEC-YYEU1C8/2023|2023-04-06,USA/TX-CDC-VSX-A064973/2023|OQ691835.1|2023-03-15,USA/WA-CDC-UW23030704434/2023|OQ674602.1|2023-03-07 XBB.1.16.1 (100.0%) (n = 3) USA (66.7%), England (33.3%) (n = 3) 2 2023-03-07 2023-04-06 30 6 | avg_clade-2x1.0 cluster_336957 3 USA/MD-CDC-VSX-A065194/2023|OQ748665.1|2023-03-23,USA/NJ-CDC-QDX48733632/2023|OQ828022.1|2023-04-04,USA/NJ-CDC-VSX-A065282/2023|OQ748806.1|2023-03-27 XBB.1.16.1 (100.0%) (n = 3) USA (100.0%) (n = 3) 1 2023-03-23 2023-04-04 12 7 | avg_clade-2x1.0 cluster_336546 2 USA/TX-CDC-QDX47594614/2023|OQ655600.1|2023-03-05,USA/TX-CDC-QDX47771482/2023|OQ684985.1|2023-03-11 XBB.1.16.1 (100.0%) (n = 2) USA (100.0%) (n = 2) 1 2023-03-05 2023-03-11 6 8 | avg_clade-2x1.0 cluster_336630 2 England/PHEC-YYEQJUC/2023|2023-03-18,England/PHEP-YYGURE9/2023|2023-04-10 XBB.1.16.1 (100.0%) (n = 2) England (100.0%) (n = 2) 1 2023-03-18 2023-04-10 23 9 | avg_clade-2x1.0 cluster_336753 5 IND/2911/2023|OQ701273.1|2023-03-06,IND/2912/2023|OQ701274.1|2023-03-07,IND/2915/2023|OQ701277.1|2023-03-02,IND/2948/2023|OQ701302.1|2023-03-10,IND/NGS_-_1625/2023|OQ852629.1|2023-03-07 XBB.1.16.1 (100.0%) (n = 5) India (100.0%) (n = 5) 1 2023-03-02 2023-03-10 8 10 | avg_clade-2x1.0 cluster_336869 3 IND/2819/2023|OQ701227.1|2023-03-11,IND/2918/2023|OQ701278.1|2023-03-02,IND/2924/2023|OQ701282.1|2023-03-08 XBB.1.16.1 (100.0%) (n = 3) India (100.0%) (n = 3) 1 2023-03-02 2023-03-11 9 11 | avg_clade-2x1.0 cluster_337010 8 CHN/SH-XG2304-2050/2023|C_AA010908.1|2023-03-30,England/PHEP-YYG1ZEQ/2023|2023-04-07,IND/2922/2023|OQ701280.1|2023-03-08,USA/CA-CDC-QDX48637276/2023|OQ827117.1|2023-03-29,USA/OH-CDC-QDX47973871/2023|OQ712825.1|2023-03-14,USA/PA-CDC-LC1027064/2023|OQ660759.1|2023-03-05,USA/RI-CDC-LC1036205/2023|OQ832920.1|2023-04-03,USA/WA-CDC-UW23032466470/2023|OQ833399.1|2023-03-24 XBB.1.16.1 (100.0%) (n = 8) USA (62.5%), China (12.5%), India (12.5%), England (12.5%) (n = 8) 4 2023-03-05 2023-04-07 33 12 | avg_clade-2x1.0 cluster_337887 3 USA/HI-CDC-QDX48637207/2023|OQ827270.1|2023-03-31,USA/NJ-CDC-LC1035908/2023|OQ832960.1|2023-04-04,USA/TX-CDC-QDX48733706/2023|OQ828034.1|2023-04-04 XBB.1.16.1 (100.0%) (n = 3) USA (100.0%) (n = 3) 1 2023-03-31 2023-04-04 4 13 | avg_clade-2x1.0 cluster_336868 2 USA/GA-CDC-QDX48733575/2023|OQ827534.1|2023-04-02,USA/NY-CDC-QDX48144042/2023|OQ728669.1|2023-03-20 XBB.1.16.1 (100.0%) (n = 2) USA (100.0%) (n = 2) 1 2023-03-20 2023-04-02 13 14 | avg_clade-2x1.0 cluster_336719 5 Scotland/SCOT-28022/2023|2023-03-03,Scotland/SCOT-28022/2023|OX462557.1|2023-03-03,Scotland/SCOT-28024/2023|2023-02-28,Scotland/SCOT-28024/2023|OX462598.1|2023-02-28,USA/FL-CDC-QDX47772044/2023|OQ705280.1|2023-03-11 XBB.1.16.1 (100.0%) (n = 5) Scotland (80.0%), USA (20.0%) (n = 5) 2 2023-02-28 2023-03-11 11 15 | avg_clade-2x1.0 cluster_336960 2 IND/2885/2023|OQ701259.1|2023-03-10,IND/2958/2023|OQ701308.1|2023-03-14 XBB.1.16.1 (100.0%) (n = 2) India (100.0%) (n = 2) 1 2023-03-10 2023-03-14 4 16 | avg_clade-2x1.0 cluster_337053 9 IND/2872/2023|OQ701252.1|2023-03-02,IND/2900/2023|OQ701265.1|2023-03-05,IND/2907/2023|OQ701270.1|2023-03-06,IND/2937/2023|OQ701291.1|2023-03-02,IND/2938/2023|OQ701292.1|2023-03-07,IND/2950/2023|OQ701304.1|2023-03-13,IND/NGS_-_1490/2023|OQ852620.1|2023-02-05,IND/NGS_-_1536/2023|OQ852625.1|2023-02-19,USA/WI-CDC-VSX-A065209/2023|OQ748644.1|2023-03-22 XBB.1.16.1 (100.0%) (n = 9) India (88.9%), USA (11.1%) (n = 9) 2 2023-02-05 2023-03-22 45 17 | avg_clade-2x1.0 cluster_336348 3 CHN/SH-HG20230404-167/2023|C_AA009133.1|2023-04-03,IND/2960/2023|OQ701309.1|2023-03-10,USA/PA-CDC-LC1034152/2023|OQ802536.1|2023-03-29 XBB.1.16.1 (100.0%) (n = 3) China (33.3%), India (33.3%), USA (33.3%) (n = 3) 3 2023-03-10 2023-04-03 24 18 | avg_clade-2x1.0 cluster_337095 2 IND/2892/2023|OQ701260.1|2023-03-14,IND/2957/2023|OQ701307.1|2023-03-10 XBB.1.16.1 (100.0%) (n = 2) India (100.0%) (n = 2) 1 2023-03-10 2023-03-14 4 19 | avg_clade-2x1.0 cluster_337275 2 USA/CA-CDC-LC1033870/2023|OQ748605.1|2023-03-29,USA/FL-CDC-QDX48415259/2023|OQ825235.1|2023-03-27 XBB.1.16.1 (100.0%) (n = 2) USA (100.0%) (n = 2) 1 2023-03-27 2023-03-29 2 20 | avg_clade-2x1.0 cluster_337435 2 CHN/SH-PD202304070004/2023|C_AA010881.1|2023-04-05,CHN/SH-PD202304070006/2023|C_AA010882.1|2023-04-05 XBB.1.16.1 (100.0%) (n = 2) China (100.0%) (n = 2) 1 2023-04-05 2023-04-05 0 21 | avg_clade-2x1.0 cluster_336959 2 IND/2820/2023|OQ701228.1|2023-03-04,IND/2947/2023|OQ701301.1|2023-03-10 XBB.1.16.1 (100.0%) (n = 2) India (100.0%) (n = 2) 1 2023-03-04 2023-03-10 6 22 | avg_clade-2x1.0 cluster_336381 23 CHN/SH-HG20230411-201/2023|C_AA010860.1|2023-04-10,England/BRBR-3270A18B/2023|2023-03-17,England/BRBR-3270A18B/2023|OX458889.1|2023-03-17,England/BRBR-3270A992/2023|2023-03-17,England/BRBR-3270A992/2023|OX458355.1|2023-03-17,England/BRBR-3270AA08/2023|2023-03-17,England/BRBR-3270AA08/2023|OX458743.1|2023-03-17,England/BRBR-3270AA53/2023|2023-03-17,England/BRBR-3270AA53/2023|OX458493.1|2023-03-17,England/BRBR-3270AB05/2023|2023-03-17,England/BRBR-3270AB05/2023|OX458344.1|2023-03-17,England/BRBR-3270AB32/2023|2023-03-17,England/BRBR-3270AB32/2023|OX458843.1|2023-03-17,England/BRBR-3270ABAB/2023|2023-03-17,England/BRBR-3270ABAB/2023|OX458891.1|2023-03-17,England/PHEC-YYETNBT/2023|2023-03-27,England/QEUH-326F4BC3/2023|OX452305.1|2023-03-02,England/QEUH-326F76ED/2023|OX455077.1|2023-03-02,IND/2925/2023|OQ701283.1|2023-03-08,IND/2936/2023|OQ701290.1|2023-03-03,USA/CA-CDC-QDX48366073/2023|OQ783268.1|2023-03-22,USA/CA-CDC-QDX48366074/2023|OQ783289.1|2023-03-22,USA/TX-CDC-QDX48496835/2023|OQ825114.1|2023-03-26 XBB.1.16.1 (100.0%) (n = 23) England (73.9%), USA (13.0%), India (8.7%), China (4.3%) (n = 23) 4 2023-03-02 2023-04-10 39 23 | avg_clade-2x1.0 cluster_336958 2 Scotland/SCOT-29102/2023|2023-03-29,USA/MN-CDC-QDX48551717/2023|OQ825384.1|2023-03-28 XBB.1.16.1 (100.0%) (n = 2) USA (50.0%), Scotland (50.0%) (n = 2) 2 2023-03-28 2023-03-29 1 24 | avg_clade-2x1.0 cluster_337886 35 CHN/SH-HG20230412-207/2023|C_AA010866.1|2023-04-11,CHN/ZJCDC708/2023|C_AA008790.1|2023-04-02,CHN/ZJCDC709/2023|C_AA008791.1|2023-04-02,CHN/ZJCDC710/2023|C_AA008792.1|2023-04-02,CHN/ZJCDC711/2023|C_AA008793.1|2023-04-02,CHN/ZJCDC712/2023|C_AA008794.1|2023-04-02,CHN/ZJCDC718/2023|C_AA008800.1|2023-03-31,CHN/ZJCDC750/2023|C_AA009614.1|2023-04-02,CHN/ZJCDC751/2023|C_AA009615.1|2023-04-02,CHN/ZJCDC752/2023|C_AA009616.1|2023-04-02,CHN/ZJCDC753/2023|C_AA009617.1|2023-04-02,CHN/ZJCDC754/2023|C_AA009618.1|2023-04-02,CHN/ZJCDC760/2023|C_AA009624.1|2023-03-31,England/BRBR-3270A1C7/2023|2023-03-17,England/BRBR-3270A1C7/2023|OX458658.1|2023-03-17,England/BRBR-3270A1F4/2023|2023-03-17,England/BRBR-3270A1F4/2023|OX458423.1|2023-03-17,England/BRBR-3270AACC/2023|OX458886.1|2023-03-17,England/PHEC-YYEOSGI/2023|2023-03-25,England/PHEC-YYEXNCY/2023|2023-03-20,England/PHEC-YYEXNJG/2023|2023-03-20,England/PHEC-YYEXNRO/2023|2023-03-20,England/PHEP-YYGT78C/2023|2023-03-21,Scotland/SCOT-28892/2023|2023-03-30,Scotland/SCOT-28892/2023|OX463564.1|2023-03-30,Switzerland/ZH-UZH-IMV-3ba697c0/2023|OX463582.1|2023-03-26,USA/CA-CDC-LC1022590/2023|OQ601346.1|2023-02-28,USA/NJ-CDC-LC1036238/2023|OQ832843.1|2023-04-01,USA/NJ-CDC-QDX48043255/2023|OQ713510.1|2023-03-17,USA/NJ-CDC-QDX48144116/2023|OQ728515.1|2023-03-20,USA/NY-CDC-QDX47869972/2023|OQ705510.1|2023-03-13,USA/NY-CDC-QDX48094657/2023|OQ728319.1|2023-03-19,USA/NY-CDC-QDX48415136/2023|OQ825249.1|2023-03-27,USA/WA-UW-23031628432/2023|OQ704103.1|2023-03-16,Wales/PHWC-PKX1RR/2023|2023-04-08 XBB.1.16.1 (100.0%) (n = 35) China (37.1%), England (28.6%), USA (22.9%), Scotland (5.7%), Wales (2.9%), Switzerland (2.9%) (n = 35) 6 2023-02-28 2023-04-11 42 25 | avg_clade-2x1.0 cluster_337245 2 England/PHEC-YYE1H7H/2023|2023-04-05,England/PHEC-YYEUJUB/2023|2023-04-06 XBB.1.16.1 (100.0%) (n = 2) England (100.0%) (n = 2) 1 2023-04-05 2023-04-06 1 26 | avg_clade-2x1.0 cluster_337011 4 USA/FL-CDC-QDX48587493/2023|OQ827224.1|2023-03-30,USA/IL-CDC-QDX48685611/2023|OQ827947.1|2023-04-03,USA/MO-CDC-QDX47771568/2023|OQ684947.1|2023-03-11,USA/PA-CDC-QDX48587130/2023|OQ827341.1|2023-03-31 XBB.1.16.1 (100.0%) (n = 4) USA (100.0%) (n = 4) 1 2023-03-11 2023-04-03 23 27 | avg_clade-2x1.0 cluster_337009 3 England/PHEC-YYEU91P/2023|2023-04-06,England/PHEP-YYG1DWJ/2023|2023-03-19,USA/WA-CDC-UW23040359572/2023|OQ799284.1|2023-04-03 XBB.1.16.1 (100.0%) (n = 3) England (66.7%), USA (33.3%) (n = 3) 2 2023-03-19 2023-04-06 18 28 | avg_clade-2x1.0 cluster_336913 2 IND/2890/2023|OQ704263.1|2023-03-13,IND/2967/2023|OQ701314.1|2023-03-10 XBB.1.16.1 (100.0%) (n = 2) India (100.0%) (n = 2) 1 2023-03-10 2023-03-13 3 29 | avg_clade-2x1.0 cluster_337704 2 USA/GA-CDC-QDX48496695/2023|OQ825368.1|2023-03-28,USA/GA-CDC-QDX48496722/2023|OQ825584.1|2023-03-29 XBB.1.16.1 (100.0%) (n = 2) USA (100.0%) (n = 2) 1 2023-03-28 2023-03-29 1 30 | avg_clade-2x1.0 cluster_336833 4 USA/2255668/2023|OQ781076.1|2023-03-20,Wales/PHWC-PKP8ZC/2023|2023-03-01,Wales/PHWC-PKPDPF/2023|2023-03-04,Wales/PHWC-PKPG7B/2023|2023-03-01 XBB.1.16.1 (100.0%) (n = 4) Wales (75.0%), USA (25.0%) (n = 4) 2 2023-03-01 2023-03-20 19 31 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2_recent_samples/input/README.md: -------------------------------------------------------------------------------- 1 | The SARS-CoV-2 tree used in this example, and that corresponded to this metadata file, was downloaded from Taxonium [https://cov2tree.org/] through: 2 | http://hgdownload.soe.ucsc.edu/goldenPath/wuhCor1/UShER_SARS-CoV-2// 3 | on April 24th, 2023 4 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2_recent_samples/output/SARS-CoV-2_last2months.log: -------------------------------------------------------------------------------- 1 | 2 | ******************** running reportree.py ******************** 3 | 4 | version 2.0.1 last updated on 2023-05-03 5 | 6 | reportree.py -m input/metadata.nwk -t input/tree.nwk --columns_summary_report pango_lineage_usher,country,n_country,first_seq_date,last_seq_date,timespan_days --metadata2report pango_lineage_usher,country,iso_week --method-threshold avg_clade-2 --subset -f date >= 2023-03-01 --count-matrix pango_lineage_usher,country:iso_week -out output/SARS-CoV-2_last2months 7 | start: 2023-05-08 11:14:45.302692 8 | 9 | Tree file provided -> will run partitioning_treecluster.py: 10 | 11 | 12 | -------------------- partitioning_treecluster.py -------------------- 13 | 14 | version 1.2.0 last updated on 2023-05-03 15 | 16 | scripts/partitioning_treecluster.py -t input/tree.nwk -o output/SARS-CoV-2_last2months -d 1.0 --method-threshold avg_clade-2 --root no -m input/metadata.nwk -f date >= 2023-03-01 17 | 18 | Assessing tree specificities... 19 | Threshold magnitude is 1.0 20 | Maximum possible threshold is 148.0 21 | Getting partitions with the following command line(s): 22 | TreeCluster.py -i input/tree.nwk -o output/SARS-CoV-2_last2months.tsv -t 2.0 -s -inf -m avg_clade 23 | Filtering metadata for the following parameters: date >= 2023-03-01 24 | 25 | partitioning_treecluster.py is done! 26 | 27 | -------------------- metadata_report.py -------------------- 28 | 29 | version 1.0.2 last updated on 2023-03-22 30 | 31 | scripts/metadata_report.py -m input/metadata.nwk -p output/SARS-CoV-2_last2months_partitions.tsv -o output/SARS-CoV-2_last2months --columns_summary_report pango_lineage_usher,country,n_country,first_seq_date,last_seq_date,timespan_days --partitions2report all --metadata2report pango_lineage_usher,country,iso_week -f date >= 2023-03-01 --frequency-matrix no --count-matrix pango_lineage_usher,country:iso_week 32 | 33 | Getting information from the partitions table: output/SARS-CoV-2_last2months_partitions.tsv 34 | Getting metadata information... 35 | Filtering metadata for the following parameters: date >= 2023-03-01 36 | Samples present in partitions table but missing in metadata table: 37 | Samples not present in partitions table but present in metadata table: England/LSPA-3F3916D/2022|OX272005.1|2022-07-29,England/LSPA-3EC929D/2022|OX257400.1|2022-07-04,Denmark/DCGC-556743/2022|OX280900.1|2022-07-29,England/LSPA-3EC1B12/2022|OX253511.1|2022-07-04,RNA|OX259219.1|2022-07-04,Wales/PHWC-PJ97JX/2022|2022-07-02,England/PHEC-4G0BAZ6E/2021|2021-12-27,Switzerland/VD-ETHZ-33454535/2021|OU573692.2|2021-07-09,USA/CA-CDC-LC0853044/2022|OP351168.1|2022-08-22,England/PHEC-4G0BBZF2/2021|2021-12-27,RNA|OX258921.1|2022-07-14,England/PHEP-YYFOJQI/2022|2022-07-23,USA/CO-CDPHE-2103388652/2022|OP049366.1|2022-06-20,England/PHEP-YYFMGMJ/2022|2022-07-08,England/PHEP-YYF4BU6/2022|2022-10-07,England/LSPA-3EC1769/2022|OX254428.1|2022-07-04,USA/CA-CDC-STM-6T9EWTCFR/2022|OP848911.1|2022-11-02,Denmark/DCGC-572500/2022|OX330581.1|2022-08-26,USA/WA-UW-22072893481/2022|OP348635.1|2022-07-28,USA/CA-OC-3484/2022|OP174602.1|2022-07-21,England/PHEP-YYFOWES/2022|2022-07-19,USA/CA-CDC-STM-ZFTJF9A86/2022|OQ114635.1|2022-12-03,England/LSPA-3F445BE/2022|OX319998.1|2022-07-28,USA/CA-CDC-VSX-A073760/2023|OQ520548.1|2023-02-13,USA/UT-UPHL-220812467645/2022|OP294511.2|2022-07-20,Wales/PHWC-PKDAST/2022|2022-08-29,England/PHEC-YYG8K4P/2022|2022-07-17,Denmark/DCGC-570894/2022|OX331015.1|2022-08-22,England/PHEC-YYGTQWY/2022|2022-07-30,Northern_Ireland/NIRE-01dee4/2022|2022-07-01,England/PHEC-YYGWD8Y/2022|2022-08-03,USA/CO-CDPHE-2103496831/2022|OP275105.1|2022-07-13,USA/CA-CDC-807969704/2023|OQ381321.1|2023-01-14,Scotland/LSPA-3257E4A9/2022|OX353398.1|2022-08-27,England/LSPA-3F63F8E/2022|OX319962.1|2022-08-15,England/PHEC-YYGE47I/2022|2022-07-14,England/LSPA-3F58CFE/2022|OX298511.1|2022-08-10,Wales/PHWC-PKD7AE/2022|2022-08-30,England/PHEC-4G0BBZ4D/2021|2021-12-27,USA/MN-MDH-27762/2022|OP030635.1|2022-07-13,England/PHEC-YYGM8PD/2022|2022-07-20,Scotland/LSPA-3F22079/2022|OX261900.1|2022-07-26,Switzerland/BL-ETHZ-33576878/2021|OU574723.2|2021-07-25,Denmark/DCGC-570201/2022|OX333187.1|2022-08-22,USA/CT_1139/2021|OL467310.1|2021-11-01,England/PHEC-YYG8K9D/2022|2022-07-18,USA/CO-CDPHE-2103479745/2022|OP278496.1|2022-06-28,England/QEUH-3EFC273/2022|OX261988.1|2022-07-19,England/PHEP-YYFSOT5/2022|2022-09-18,USA/CO-CDPHE-2103515566/2022|OP274885.1|2022-07-14,England/PLYM-15208D2D/2022|OX341171.1|2022-09-12,Denmark/DCGC-556774/2022|OX279321.1|2022-07-29,USA/CO-CDPHE-2103476136/2022|OP280540.1|2022-06-28,England/PHEC-3Z07AZ26/2021|2021-12-27,England/QEUH-3EFF7BD/2022|OX262547.1|2022-07-19,USA/CO-CDPHE-2103508377/2022|OP275030.1|2022-07-12,Denmark/DCGC-569209/2022|OX325509.1|2022-08-19,England/LSPA-3F2B78A/2022|OX271580.1|2022-07-27,Scotland/SCOT-15129/2022|OX425511.1|2022-06-26,England/PHEP-YYF6EBW/2022|2022-10-13,England/LSPA-3EC1787/2022|OX251553.1|2022-07-04,England/QEUH-3EE388C/2022|OX343122.1|2022-07-06,USA/ND-CDC-2-6354247/2022|OP431740.1|2022-08-08,England/PHEP-YYFWP4O/2022|2022-07-27,England/PHEP-YYF3EMK/2022|2022-09-29,England/LSPA-3259AE6D/2022|OX356285.1|2022-10-04,England/PHEC-YYG1DH3/2022|2022-08-02,England/PHEC-YYGEGTB/2022|2022-07-14,England/PHEP-YYFPZ4I/2022|2022-07-10,England/PHEC-YYG8GBR/2022|2022-07-14,England/PLYM-3EEB6D5/2022|OX248484.1|2022-07-15,USA/NC-CDC-LC0819518/2022|OP240328.1|2022-08-02,Wales/PLYM-3F85BA0/2022|OX340062.1|2022-08-29,USA/MA-CDCBI-CRSP_F5DSEHRYSW2L7PPR/2022|OP122708.1|2022-07-19,England/PHEP-YYF5QJR/2022|2022-10-12,England/LSPA-3F42DE5/2022|OX272326.1|2022-08-02,England/PHEC-YYG1RYT/2022|2022-08-02,England/DHSC-CYR3H3Q/2022|2022-07-19,USA/NY-CDC-LC0790717/2022|OP119542.1|2022-07-19,England/PHEP-YYF6FJ8/2022|2022-10-16,USA/CA-CDC-STM-67U4G95S2/2022|OP848854.1|2022-11-01,USA/IL-CDC-LC0865222/2022|OP402430.1|2022-08-28,England/PHEC-YYGSPAX/2022|2022-08-16,Denmark/DCGC-561220/2022|OX295249.1|2022-08-02,England/PHEC-YYGTQ51/2022|2022-07-30,USA/IL-CDC-LC0868712/2022|OP424260.1|2022-08-29,England/QEUH-3ED1C49/2022|OX243245.1|2022-07-06,Denmark/DCGC-555392/2022|OX284255.1|2022-07-24,RNA|OX346154.1|2022-09-21,England/LSPA-325ABE69/2022|OX363762.1|2022-10-10,Denmark/DCGC-565475/2022|OX323420.1|2022-08-14,Denmark/DCGC-564698/2022|OX325488.1|2022-08-08,Wales/PHWC-PKYEM6/2022|2022-07-07,England/QEUH-3EFCCB1/2022|OX263588.1|2022-07-19,England/PHEC-YYGZXQY/2022|2022-08-22,England/DHSC-CYFKAXF/2022|2022-07-11,England/PHEP-YYF1ICJ/2022|2022-08-09,England/QEUH-3EFC176/2022|OX263308.1|2022-07-19,USA/TX-CDC-VSX-A073891/2023|OQ582164.1|2023-02-13,England/PLYM-3EE3A77/2022|OX244359.1|2022-07-12,EGY/CCHE57357_Wave_3_A_023/2021|MZ364992.1|2021-05-08,England/LSPA-3F68F89/2022|OX319403.1|2022-08-17,USA/OKPHL0026332/2023|OQ630218.1|2023-02-27,England/LSPA-3F05E90/2022|OX264902.1|2022-07-22,USA/2207050099/2022|OP821237.1|2022-07-05,USA/WA-UW-22081116722/2022|OP363400.1|2022-08-11,USA/UT-UPHL-230118688287/2023|OQ336429.2|2023-01-07,USA/NJ-CDC-LC0790427/2022|OP118801.1|2022-07-18,England/PHEC-YYGOMQ1/2022|2022-07-28,England/PHEC-3Z07AZ08/2021|2021-12-27,England/LSPA-3EC1620/2022|OX254142.1|2022-07-04,England/LSPA-3EC1918/2022|OX272614.1|2022-07-04,RNA|OX280165.1|2022-08-01,England/PHEC-YYG1MRN/2022|2022-08-01,England/PHEC-YYGFTEX/2022|2022-07-16,England/PHEP-YYFWGKA/2022|2022-08-11,England/PHEC-YYG1KDF/2022|2022-08-03,USA/LA-EVTL2694/2021|OP398720.1|2021-04-22,England/PHEC-YYG8ETB/2022|2022-07-17,USA/IL-CDC-STM-8FR8ZPYN4/2022|OP732470.1|2022-10-03,England/QEUH-32581124/2022|OX354351.1|2022-09-22,England/PHEC-3U048UD4/2021|2021-11-22,England/PHEC-YYG1D9U/2022|2022-08-02,England/PHEC-YYGSD5U/2022|2022-08-07,England/PHEC-YYGESCH/2022|2022-07-15,England/PHEP-YYF9FQ5/2022|2022-11-02,England/PHEC-YY8FCF3/2022|2022-09-12,USA/CA-CDPH-FS25390315/2022|OP421100.1|2022-08-05,Denmark/DCGC-572753/2022|OX331927.1|2022-08-27,England/PHEC-3Z079ZB0/2021|2021-12-16,USA/IN-CDC-STM-FT6S9GDUQ/2022|OP794952.1|2022-09-28,England/LSPA-3F68C64/2022|OX319810.1|2022-08-17,England/LSPA-3ED5CEB/2022|OX255909.1|2022-07-08,USA/NY-CDC-LC0880231/2022|OP483531.1|2022-09-10,USA/CA-CDC-VSX-A063413/2023|OQ691934.1|2023-03-17,Denmark/DCGC-572343/2022|OX329472.1|2022-08-27,USA/CA-CDPH-500098396/2022|OQ641277.1|2022-12-26,USA/FL-CDC-STM-XN89XXE83/2022|OP504271.1|2022-09-08,USA/VA-CDC-LC0775270/2022|OP065676.1|2022-07-14,England/PLYM-3EE7800/2022|OX248663.1|2022-07-13,England/PHEC-YYGSMAT/2022|2022-08-16,England/QEUH-325B553A/2022|OX364015.1|2022-10-11,England/PHEP-YYFPQ46/2022|2022-07-11,England/DHSC-CYGHP9J/2022|2022-07-08,Denmark/DCGC-544914/2022|OX222530.1|2022-07-02,England/PHEP-YYFI475/2022|2022-09-07,Scotland/LSPA-3F1788E/2022|OX259801.1|2022-07-23,England/LSPA-3F4A688/2022|OX281701.1|2022-08-03,England/PHEC-YYGO7QY/2022|2022-07-28,England/PHEC-YYGPMHH/2022|2022-07-28,England/LSPA-3EC2346/2022|OX254160.1|2022-07-04,England/PHEC-YY8YRC7/2022|2022-09-29,England/QEUH-325A2916/2022|OX363537.1|2022-10-06,USA/UT-UPHL-230106408183/2022|OQ268412.2|2022-12-23,USA/CO-CDPHE-2103491186/2022|OP355012.1|2022-07-08,England/PHEP-YYFXEAR/2022|2022-07-09,USA/CA-CDPH-7000016858/2022|OQ335728.1|2022-08-02,USA/FL-CDC-LC0794988/2022|OP118419.1|2022-07-18,England/PLYM-3EF0646/2022|OX248097.1|2022-07-16,USA/UT-UPHL-220702498994/2022|ON942608.2|2022-06-13,England/PLYM-325D6BB9/2022|OX372260.1|2022-10-28,Switzerland/SH-ETHZ-33447906/2021|OU573421.2|2021-07-09,England/LSPA-3ED353D/2022|OX256501.1|2022-07-07,USA/IL-CDC-LC0875140/2022|OP463192.1|2022-09-01,England/QEUH-3EF0EB7/2022|OX248843.1|2022-07-15,Denmark/DCGC-564569/2022|OX323642.1|2022-08-08,USA/CA-CDC-LC0873878/2022|OP439403.1|2022-09-04 38 | Getting summary stats for the variables specified at '--partitions2report'... 39 | Getting summary stats for the variables specified at '--metadata2report'... 40 | Getting count matrix for the variables specified at '--count-matrix'... 41 | Creating matrix with the count of pango_lineage_usher per country and iso_week... 42 | metadata_report.py is done! 43 | 44 | ------------------------------------------------------------ 45 | 46 | ReporTree is done! If you found any issue please contact us!! 47 | 48 | 49 | End: 2023-05-08 11:56:34.551159 50 | Time elapsed: 0:41:49.248467 51 | -------------------------------------------------------------------------------- /examples/SARS-CoV-2_recent_samples/output/SARS-CoV-2_last2months_country_summary.tsv: -------------------------------------------------------------------------------- 1 | country pango_lineage_usher first_seq_date last_seq_date timespan_days 2 | England XBB.1.5 (33.9%), XBB.1.9.1 (13.1%), XBB.1.5.7 (5.9%), CH.1.1.1 (5.6%), XBB.1.5.18 (3.7%), CH.1.1 (2.8%), XBB.1.9.2 (2.3%), BQ.1.1 (1.5%), EG.1 (1.4%), XBB.1.5.13 (1.3%), EM.1 (1.2%), XBB.1.5.24 (1.2%), XBB.1.5.20 (1.1%), DU.1 (1.0%), DV.1.1 (0.9%), XBB.1.17.1 (0.8%), XBB.1.5.15 (0.7%), XBB.1.5.16 (0.7%), CH.1.1.2 (0.6%), XBB.1 (0.5%), XBB.1.5.4 (0.5%), FL.2 (0.5%), CH.1.1.6 (0.5%), XBB.2.3 (0.5%), XBB.1.16 (0.5%), XBF (0.5%), XBB.1.5.12 (0.4%), XBB.1.5.23 (0.4%), CH.1.1.8 (0.4%), XBB.2.3.1 (0.4%), XBB.1.9 (0.4%), XBB.1.5.19 (0.3%), BQ.1.1.45 (0.3%), BQ.1.1.71 (0.3%), XBB.1.5.37 (0.3%), DV.3 (0.3%), CH.1.1.19 (0.3%), CH.1.1.10 (0.3%), XBB.1.22 (0.3%), FD.1 (0.3%), XBB.1.19.1 (0.3%), XBF.4 (0.3%), XBB.1.16.1 (0.2%), CH.1.1.11 (0.2%), BQ.1.1.2 (0.2%), DV.2 (0.2%), FN.1 (0.2%), XBF.1.1 (0.2%), XBB.1.5.1 (0.2%), XBF.3 (0.2%), CH.1.1.21 (0.2%), DN.1.1 (0.2%), XBB.1.5.31 (0.2%), BQ.1.1.10 (0.2%), FL.1 (0.2%), BQ.1.1.23 (0.2%), EK.3 (0.2%), FM.1 (0.2%), XBB.1.5.21 (0.2%), CH.1.1.15 (0.2%), XBB.1.22.2 (0.2%), XBQ (0.2%), XCA (0.2%), CH.1.1.23 (0.2%), EF.2 (0.2%), BR.2.1 (0.2%), CH.1.1.14 (0.1%), XBB.1.5.25 (0.1%), BA.2.75 (0.1%), XBB.1.5.28 (0.1%), FH.1 (0.1%), DS.2 (0.1%), BA.5.11 (0.1%), BQ.1.11 (0.1%), FJ.1 (0.1%), EF.1 (0.1%), XBB.1.5.33 (0.1%), BQ.1.1.31 (0.1%), XBB.1.18.1 (0.1%), XBB.2.4 (0.1%), DN.1.1.3 (0.1%), BQ.1.1.6 (0.1%), XBB.1.5.6 (0.1%), XBB.1.5.30 (0.1%), EL.1 (0.1%), BQ.1.1.1 (0.1%), DS.1 (0.1%), XBB.1.11.1 (0.1%), DV.5 (0.1%), XBK (0.1%), EU.1.1 (0.1%), ER.1.1 (0.1%), EA.1 (0.1%), BQ.1.1.22 (0.1%), ER.1 (0.1%), XBB.1.5.32 (0.1%), BE.13 (0.1%), XBB.1.22.1 (0.1%), XBB.1.5.17 (0.1%), CH.1.1.13 (0.1%), DV.1 (0.1%), XBB.1.5.14 (0.1%), BQ.1.2 (0.1%), EF.1.1.1 (0.1%), CK.1 (0.1%), CH.1.1.20 (0.1%), CH.1.1.5 (0.1%), BQ.1 (0.1%), XBB.1.5.5 (0.1%), DN.1.1.4 (0.1%), FG.3 (0.1%), DT.1 (0.1%), XBB.2.3.2 (0.1%), BQ.1.1.24 (0.1%), BN.1.7 (0.1%), XBB.1.5.11 (0.1%), BQ.1.1.69 (0.1%), XBB.2 (0.1%), CH.1.1.9 (0.1%), EF.1.1 (0.1%), ET.1 (0.1%), BQ.1.1.42 (0.1%), EC.1.1 (0.1%), CH.1.1.18 (0.1%), XBF.7 (0.1%), XBB.1.5.38 (0.1%), BN.1.3.1 (0.1%), BQ.1.1.8 (0.1%), BQ.1.18 (0.0%), BQ.1.13.1 (0.0%), BM.1.1.4 (0.0%), BA.5.2 (0.0%), BQ.1.1.47 (0.0%), BQ.1.1.32 (0.0%), EV.1 (0.0%), BQ.1_17039 (0.0%), BF.11.2 (0.0%), BQ.1.1.58 (0.0%), CH.1.1.16 (0.0%), EC.1 (0.0%), XBB.1.13 (0.0%), XBB.1.28.1 (0.0%), FB.2 (0.0%), XBB.1.5.39 (0.0%), BA.5.3.1 (0.0%), XBB.1.15 (0.0%), BN.1.3 (0.0%), FD.2 (0.0%), FK.1 (0.0%), CH.1.1.17 (0.0%), FF.1 (0.0%), XBB.1.5.2 (0.0%), XBF.7.1 (0.0%), BQ.1.3 (0.0%), XBB.1.5.36 (0.0%), DN.1 (0.0%), XBB.2.3.3 (0.0%), BQ.1.1.13 (0.0%), XBB.1.19 (0.0%), BQ.1.25.1 (0.0%), BQ.1.1.18 (0.0%), ES.1 (0.0%), BQ.1.1.5 (0.0%), DV.3.1 (0.0%), XBF.9 (0.0%), EH.1 (0.0%), BN.1.4.3 (0.0%), BQ.1.1.72 (0.0%), CH.1.1.12 (0.0%), BQ.1.8 (0.0%), BE.9 (0.0%), BA.5.1 (0.0%), BQ.1.25 (0.0%), EU.1 (0.0%), BQ.1.23 (0.0%), BQ.1.10.1 (0.0%), BQ.1.1.14 (0.0%), XBB.1.12 (0.0%), CJ.1.2 (0.0%), FE.1 (0.0%), CH.1.1.3 (0.0%), CG.1 (0.0%), EN.1 (0.0%), DY.1 (0.0%), XBB.1.4.1 (0.0%), BA.5.2.35 (0.0%), BA.5.3.5 (0.0%), BQ.1.1.3 (0.0%), FB.1 (0.0%), BA.5.1.26 (0.0%), BQ.1.1.4 (0.0%), CH.1.1.7 (0.0%), XBB.1.5.9 (0.0%), XBF.5 (0.0%), XBB.1.5.3 (0.0%), BQ.1.1.17 (0.0%), BQ.1.10.3 (0.0%), XBL (0.0%), EG.2 (0.0%), CR.1 (0.0%), BA.5.2.1 (0.0%), XBB.1.5.40 (0.0%), XBB.2.3.4 (0.0%), XAY.1.1.1 (0.0%), BA.5.2.34 (0.0%), BN.1.2.3 (0.0%), EF.1.2 (0.0%), XBR (0.0%), BQ.1.1.20 (0.0%), DN.1.1.2 (0.0%), BN.1.2 (0.0%), EG.1.1 (0.0%), FK.1.1 (0.0%), BE.1.2 (0.0%), BN.1.5 (0.0%), XBB.1.5.8 (0.0%), BQ.1.1.28 (0.0%), BN.1 (0.0%), BM.1.1 (0.0%), XBB.3 (0.0%), CM.8.1 (0.0%), XBS (0.0%), XBB.1.5.35 (0.0%), BN.1.3.6 (0.0%), XBU (0.0%), BQ.1.1.29 (0.0%), BF.5 (0.0%), EZ.1 (0.0%), XBB.1.9.5 (0.0%), BQ.1.1.44 (0.0%), BF.11 (0.0%), BQ.1.1.68 (0.0%), BW.1.1.1 (0.0%), BQ.1.1.66 (0.0%), BQ.1.1.39 (0.0%), BQ.1.1.19 (0.0%), CV.1 (0.0%), EE.4 (0.0%) (n = 17831) 2023-03-01 2023-04-16 46 3 | Brazil FE.1 (55.0%), XBB.1.5 (40.0%), XBB.1.18.1 (5.0%) (n = 20) 2023-03-06 2023-04-01 26 4 | India XBB.1.16.1 (66.0%), XBB.2.3 (17.0%), XBB.1.16 (7.5%), XBB.1.9.2 (3.8%), XBB.2.3.3 (1.9%), XBB.2.3.4 (1.9%), XBB.2 (1.9%) (n = 53) 2023-03-02 2023-03-14 12 5 | Scotland XBB.1.5 (32.1%), XBB.1.9.1 (15.1%), XBB.1.5.13 (11.5%), XBB.1.5.18 (6.0%), CH.1.1 (3.3%), CH.1.1.1 (2.6%), XBB.1.5.7 (2.4%), BQ.1.1 (2.0%), XBB.1.9.2 (1.5%), XBB.1.5.15 (1.3%), DV.1.1 (1.2%), XBB.1.22 (1.2%), XBB.1.17.1 (1.1%), EK.2.1 (1.0%), EU.1.1 (1.0%), XBF (1.0%), XBB.1.5.24 (0.9%), EK.3 (0.7%), XBB.1.5.31 (0.7%), DU.1 (0.6%), BQ.1.1.29 (0.6%), CH.1.1.11 (0.6%), EG.1 (0.6%), CH.1.1.2 (0.6%), BQ.1.1.45 (0.6%), EL.1 (0.6%), XBB.1 (0.5%), DN.1.1 (0.4%), XBB.1.5.16 (0.4%), EM.1 (0.4%), CH.1.1.8 (0.4%), XBF.3 (0.3%), BQ.1.1.2 (0.3%), XBB.1.5.33 (0.3%), FD.1 (0.3%), XBB.2.3.1 (0.3%), XBB.1.5.23 (0.3%), XBB.2.3 (0.3%), BQ.1.1.41 (0.2%), XBB.1.5.37 (0.2%), XBB.1.16 (0.2%), XBB.1.5.21 (0.2%), XBB.1.5.36 (0.2%), XBB.1.5.20 (0.2%), BQ.1.13.1 (0.2%), BQ.1.1.3 (0.2%), BQ.1.14 (0.2%), XBB.1.5.19 (0.2%), XBB.1.5.12 (0.2%), FL.2 (0.1%), CH.1.1.23 (0.1%), XBB.1.16.1 (0.1%), CH.1.1.9 (0.1%), ER.1 (0.1%), CH.1.1.14 (0.1%), CH.1.1.20 (0.1%), DV.3 (0.1%), CH.1.1.19 (0.1%), BQ.1.1.35 (0.1%), XBB.2.3.3 (0.1%), XBB.1.5.40 (0.1%), XBB.1.5.39 (0.1%), BA.2.75.5 (0.1%), DV.1 (0.1%), CH.1.1.6 (0.1%), XBB.1.5.11 (0.1%), BA.5.3.1 (0.1%), EA.1 (0.1%), CH.1.1.16 (0.1%), XBB.1.19 (0.1%), XBB.1.19.1 (0.1%), FN.1 (0.1%), FK.1 (0.1%), BQ.1.1.18 (0.1%), EC.1.1 (0.1%), BQ.1.8.2 (0.1%), XBB.1.5.8 (0.1%), XBB.1.18.1 (0.1%), XBK.1 (0.1%), XBB.1.5.17 (0.1%), XBB.1.21 (0.1%), XBB.1.5.38 (0.1%), BA.5.2_no28330 (0.1%), FG.2 (0.1%), FH.1 (0.1%), BQ.1.1.10 (0.1%), BA.5.2.1 (0.0%), CH.1.1.12 (0.0%), XBB.1.5.4 (0.0%), XBK (0.0%), CH.1.1.15 (0.0%), EF.1 (0.0%), XBT (0.0%), BQ.1 (0.0%), XBF.4 (0.0%), BN.1.5 (0.0%), FF.1 (0.0%), XBB.1.5.5 (0.0%), CH.1.1.7 (0.0%), BR.2 (0.0%), BQ.1.1.32 (0.0%), FL.1 (0.0%) (n = 3914) 2023-03-01 2023-04-10 40 6 | USA XBB.1.5 (63.2%), XBB.1.22 (2.4%), XBB.1.9.1 (2.1%), XBB.1.5.15 (1.9%), BQ.1.1 (1.7%), XBB.1.5.13 (1.4%), XBB.1.5.1 (1.4%), XBB.1 (1.3%), XBB.1.5.32 (1.1%), FD.2 (1.1%), XBB.1.5.17 (1.1%), XBB.1.5.35 (1.0%), XBB.1.5.31 (0.8%), XBB.1.16 (0.8%), XBB.1.9.2 (0.7%), XBB.1.5.2 (0.7%), XBB.1.5.20 (0.7%), XBB.1.5.21 (0.6%), XBB.1.5.33 (0.6%), XBB.1.5.16 (0.6%), XBB.1.5.4 (0.6%), XBB.1.5.30 (0.6%), XBB.1.5.19 (0.5%), XBB.1.5.11 (0.5%), XBB.1.5.10 (0.5%), CH.1.1 (0.4%), XBB.1.5.7 (0.4%), XBB.1.5.5 (0.4%), XBB.1.5.3 (0.4%), XBB.1.5.34 (0.3%), EK.2 (0.3%), FL.2 (0.3%), XBB.2.3 (0.3%), XBB.1.5.39 (0.3%), BQ.1.1.32 (0.3%), DT.2 (0.3%), XBB.1.16.1 (0.3%), EG.1 (0.2%), CH.1.1.1 (0.2%), XBB.1.17.1 (0.2%), XBB.1.5.27 (0.2%), XBB.1.5.9 (0.2%), XBB.1.5.14 (0.2%), XBB.1.5.24 (0.2%), XBB.6.1 (0.1%), XBB.1.5.12 (0.1%), XBB.2 (0.1%), EU.1.1 (0.1%), XBB.1.5.18 (0.1%), BQ.1 (0.1%), XBB.1.19.1 (0.1%), XBB.1.5.23 (0.1%), XBF (0.1%), BQ.1.1.63 (0.1%), FE.1 (0.1%), BQ.1.25.1 (0.1%), DV.1.1 (0.1%), BQ.1.1.5 (0.1%), BQ.1.1.3 (0.1%), FG.2 (0.1%), XBB.1.5.26 (0.1%), CM.8.1 (0.1%), XBB.1.5.28 (0.1%), BQ.1.1.65 (0.1%), BQ.1.1.10 (0.1%), FL.1 (0.1%), BQ.1.28 (0.1%), XBB.1.5.8 (0.1%), BQ.1_17039 (0.1%), BQ.1.2 (0.1%), FK.1 (0.1%), BQ.1.1.18 (0.1%), XBB.1.5.37 (0.1%), XBB.2.3.2 (0.1%), EY.1 (0.1%), BQ.1.1.2 (0.1%), BQ.1.1.4 (0.1%), XBB.1.18.1 (0.1%), XBB.2.6 (0.1%), FG.1 (0.1%), BN.1 (0.1%), BQ.1.1.43 (0.1%), FH.1 (0.1%), EK.2.1 (0.1%), XBB.1.15 (0.1%), XBB.1.5.36 (0.1%), BQ.1.13.1 (0.1%), CH.1.1.2 (0.1%), BQ.1.1.52 (0.1%), BA.2 (0.1%), FB.1 (0.1%), BQ.1.1.68 (0.1%), BQ.1.11 (0.1%), BQ.1.1.45 (0.1%), BQ.1.1.13 (0.0%), CL.1 (0.0%), BQ.1.1.41 (0.0%), BQ.1.1.48 (0.0%), EF.1 (0.0%), BQ.1.10 (0.0%), BQ.1.1.69 (0.0%), CK.1 (0.0%), BQ.1.1.40 (0.0%), BQ.1.25 (0.0%), BQ.1.3 (0.0%), BN.1.3 (0.0%), DR.2 (0.0%), XBB.2.3.3 (0.0%), BQ.1.1.15 (0.0%), XBB.1.5.6 (0.0%), XBB.1.5.29 (0.0%), BN.1.2 (0.0%), XBB.1.5.38 (0.0%), XBB.1.30 (0.0%), EF.1.1 (0.0%), EU.1 (0.0%), BQ.1.10.1 (0.0%), XBF.2 (0.0%), BQ.1.18 (0.0%), BQ.1.1.1 (0.0%), EN.1 (0.0%), XBB.1.22.1 (0.0%), BA.5.2 (0.0%), FK.1.1 (0.0%), XBB.2.4 (0.0%), ED.3 (0.0%), BQ.1.1.54 (0.0%), BQ.1.1.51 (0.0%), BQ.1.12 (0.0%), XBK (0.0%), FD.1.1 (0.0%), XBB.1.22.2 (0.0%), BA.5.3.1 (0.0%), XBB.3 (0.0%), BQ.1.1.29 (0.0%), BQ.1.23 (0.0%), XBL (0.0%), BR.2.1 (0.0%), EL.1 (0.0%), EA.1 (0.0%), XBB.1.4 (0.0%), XBB (0.0%), BQ.1.1.23 (0.0%), XBB.1.5.40 (0.0%), XBB.2.3.4 (0.0%), BQ.1.32 (0.0%), CH.1.1.14 (0.0%), BW.1.1 (0.0%), BE.1.2.1 (0.0%), EE.3 (0.0%), BQ.1.22 (0.0%), DV.1 (0.0%), BN.1.1 (0.0%), CH.1.1.7 (0.0%), EM.1 (0.0%), BQ.1.14 (0.0%), XBB.1.13 (0.0%), BA.5.1.27 (0.0%), XBB.1.21 (0.0%), EW.2 (0.0%), BA.5.2_no28330 (0.0%), CH.1.1.11 (0.0%), BQ.1.1.35 (0.0%), BE.10 (0.0%), DN.1 (0.0%), BA.5.2.1 (0.0%), BF.7.4.1 (0.0%), BA.2.12.1 (0.0%), BQ.1.13 (0.0%), BQ.1.8 (0.0%), DU.1 (0.0%), EE.2 (0.0%), BF.7 (0.0%), BQ.1.1.53 (0.0%), BQ.1.1.28 (0.0%), XBP (0.0%), DF.1.1 (0.0%), XBB.1.9 (0.0%), CA.3.1 (0.0%), EF.2 (0.0%), BW.1.1.1 (0.0%), CQ.2 (0.0%), XBB.2.2 (0.0%), DT.3 (0.0%), BQ.1.15.1 (0.0%), XBB.1.19 (0.0%), BN.1.3.5 (0.0%), BM.1.1.1 (0.0%), XBB.1.9.3 (0.0%), EK.3 (0.0%), CH.1.1.3 (0.0%), BA.5.2.34 (0.0%), BQ.1.1.31 (0.0%), BQ.1.10.2 (0.0%), CR.1.1 (0.0%), BA.5.3 (0.0%), BA.4.6 (0.0%), XBB.2.5 (0.0%), DS.1 (0.0%), BQ.1.9 (0.0%), BQ.1.1.42 (0.0%), BN.1.2.3 (0.0%), DR.1 (0.0%), BQ.1.1.67 (0.0%), XBB.1.10 (0.0%), XBB.1.14 (0.0%), BA.5.1 (0.0%), BQ.1.16 (0.0%), BQ.1.1.6 (0.0%), BE.1 (0.0%), CH.1.1.18 (0.0%), BA.1.1 (0.0%), ES.1 (0.0%), CH.1.1.19 (0.0%), BW.1.1.2 (0.0%), XBF.5 (0.0%), BA.5.2.59 (0.0%), BQ.1.1.47 (0.0%), BN.1.3.1 (0.0%), BQ.1.1.39 (0.0%), BU.1 (0.0%), FB.2 (0.0%), BA.5.2.54 (0.0%), CZ.2 (0.0%), BN.1.7 (0.0%), BF.7.22 (0.0%), BQ.1.1.37 (0.0%), XBC.1.6 (0.0%), XBB.2.7.1 (0.0%), XBB.1.7 (0.0%), XBB.1.12 (0.0%), BA.4.6.1 (0.0%), BN.1.4 (0.0%), BQ.1.6 (0.0%), BF.7.4 (0.0%), BQ.1.1.61 (0.0%), XBB.4 (0.0%), EP.1 (0.0%), XBB.1.1 (0.0%), BK.1 (0.0%), XBC.1.2.1 (0.0%), BF.7.21 (0.0%), BQ.1.15 (0.0%), BA.5.2.13 (0.0%), BA.5.2.6 (0.0%), BQ.1.1.20 (0.0%), BQ.1.5 (0.0%), B.1.1.529 (0.0%), BN.3.1 (0.0%), DV.3 (0.0%), BF.26 (0.0%), BQ.1.1.74 (0.0%), CH.1.1.15 (0.0%), BN.1.3.3 (0.0%), BA.5 (0.0%), DS.2 (0.0%), XBF.4 (0.0%), DJ.1.1.1 (0.0%), XBB.1.24 (0.0%), XBB.6 (0.0%), BA.4.1 (0.0%), BQ.1.1.24 (0.0%), XBB.1.11.1 (0.0%), CP.1 (0.0%), BE.9_alt (0.0%), BQ.1.31 (0.0%), BQ.1.1.71 (0.0%), CK.2.1 (0.0%), BN.1.3.6 (0.0%), CH.1.1.10 (0.0%), BQ.1.1.57 (0.0%), DV.5 (0.0%), XBK.1 (0.0%), XBF.7.1 (0.0%), BA.5.1.18 (0.0%), BA.2.10.1 (0.0%), DV.2 (0.0%), BY.1 (0.0%), FA.1 (0.0%), BR.2 (0.0%), DN.1.1.4 (0.0%), CM.10 (0.0%), BQ.1.1.44 (0.0%), FG.3 (0.0%), CM.2.1 (0.0%), XBB.1.5.22 (0.0%), BA.2.3.20 (0.0%), BN.1.3.7 (0.0%), BA.5.1.5 (0.0%), XBF.9 (0.0%), BQ.1.1.56 (0.0%), BQ.1.1.36 (0.0%), XBB.2.3.5 (0.0%), EG.2 (0.0%), miscBA2BA1PostSpike (0.0%), EF.1.1.1 (0.0%) (n = 22990) 2023-03-01 2023-04-13 43 7 | Kuwait EG.1 (60.0%), XBB.1.5 (20.0%), FL.2 (20.0%) (n = 5) 2023-03-01 2023-03-10 9 8 | Northern Ireland XBB.1.5 (57.7%), CH.1.1.1 (7.7%), XBB.1.5.20 (3.8%), XBB.1.9.1 (3.8%), XBB.1.5.7 (2.9%), DV.1.1 (2.9%), BQ.1.1.22 (1.9%), XBB.1.11.1 (1.9%), BA.2 (1.9%), CH.1.1.8 (1.9%), XBB.1.22 (1.9%), XBB.1.5.15 (1.9%), FL.2 (1.9%), XBB.1.17.1 (1.0%), BQ.1.23 (1.0%), XBB.1.5.16 (1.0%), XBB.1.18.1 (1.0%), BQ.1.18 (1.0%), XBB.1.5.35 (1.0%), XBB.1.9.3 (1.0%), XBB.1.5.17 (1.0%) (n = 104) 2023-03-01 2023-03-26 25 9 | Slovakia XBB.1.5 (40.8%), BQ.1.1 (7.0%), XBF (5.6%), XBB.1 (4.2%), BN.1.3 (4.2%), XBB.1.5.12 (4.2%), XBB.1.9.1 (4.2%), BN.1.5 (2.8%), BQ.1.1.2 (2.8%), CH.1.1 (2.8%), BN.1.2 (2.8%), BQ.1.26 (2.8%), EN.1 (1.4%), BQ.1.1.42 (1.4%), XBB.1.5.13 (1.4%), XBB.1.5.7 (1.4%), BQ.1_17039 (1.4%), XBB.1.5.14 (1.4%), EF.1 (1.4%), XBT (1.4%), XBF.7.1 (1.4%), BQ.1 (1.4%), BN.1.1.1 (1.4%) (n = 71) 2023-03-01 2023-03-26 25 10 | Switzerland XBB.1.5 (31.9%), XBB.1.9.1 (10.9%), EG.1 (7.1%), XBB.1.9.2 (3.6%), BQ.1.1 (3.2%), XBB.1.22 (3.0%), EZ.1 (2.6%), CH.1.1 (2.2%), XBB.1.5.7 (2.2%), XBB.1.5.13 (1.9%), XBB.1 (1.7%), XBB.1.5.35 (1.7%), XBB.1.5.38 (1.7%), XBB.1.16 (1.6%), XBB.1.5.20 (1.2%), XBB.1.5.12 (1.2%), BQ.1.1.63 (0.9%), BQ.1.1.5 (0.9%), BQ.1.1.23 (0.8%), FL.2 (0.8%), XBB.1.17.1 (0.8%), BQ.1.1.45 (0.8%), XBB.1.5.15 (0.7%), BQ.1.10 (0.7%), XBB.1.5.5 (0.7%), EL.1 (0.7%), XBB.1.5.21 (0.5%), XBB.1.5.26 (0.5%), XBB.1.5.16 (0.4%), EU.1.1 (0.4%), XBB.1.5.1 (0.4%), XBB.1.5.14 (0.4%), XBK.1 (0.4%), XBB.1.5.24 (0.4%), CH.1.1.1 (0.4%), BQ.1.1.22 (0.3%), XBB.1.5.33 (0.3%), BQ.1.13.1 (0.3%), BN.1.1.1 (0.3%), EN.1 (0.3%), XBB.1.22.1 (0.3%), BA.5.2.60 (0.3%), BQ.1 (0.3%), XBB.1.5.11 (0.3%), EM.1 (0.3%), XBF.7.1 (0.3%), BN.1.4.2 (0.3%), BN.1.3.1 (0.3%), BQ.1_17039 (0.3%), CH.1.1.11 (0.3%), CH.1.1.8 (0.3%), XBB.1.5.32 (0.1%), XBB.1.5.18 (0.1%), XBB.1.5.23 (0.1%), BQ.1.1.37 (0.1%), XBB.1.5.39 (0.1%), XBB.1.19 (0.1%), CL.1 (0.1%), FE.1 (0.1%), BA.5.2.34 (0.1%), CH.1.1.3 (0.1%), BA.4.6 (0.1%), CH.1.1.12 (0.1%), BQ.1.1.18 (0.1%), XBB.1.5.37 (0.1%), XBB.1.5.4 (0.1%), XBB.2.3 (0.1%), BQ.1.1.20 (0.1%), BQ.1.12 (0.1%), CH.1.1.15 (0.1%), DV.1.1 (0.1%), DU.1 (0.1%), XBB.1.5.25 (0.1%), EC.1.1 (0.1%), XBB.1.22.2 (0.1%), BN.1.5 (0.1%), BQ.1.1.2 (0.1%), XBB.2.4 (0.1%), XBB.1.5.6 (0.1%), BA.1.1 (0.1%), XBB.1.11.1 (0.1%), BQ.1.25 (0.1%), XBF (0.1%), XBB.1.5.17 (0.1%), XBB.1.28.1 (0.1%), BQ.1.1.59 (0.1%), XBB.1.21 (0.1%), BQ.1.1.68 (0.1%), BQ.1.1.47 (0.1%), BR.2.1 (0.1%), XBB.1.5.22 (0.1%), XBB.1.15 (0.1%), BQ.1.1.32 (0.1%), XBB.1.16.1 (0.1%), EA.1 (0.1%), FL.1 (0.1%), BQ.1.1.46_25406 (0.1%) (n = 743) 2023-03-01 2023-04-09 39 11 | Bahrain XBB.1.9.1 (40.2%), XBB.1.9.2 (17.7%), FL.2 (6.6%), XBB.1.5 (5.7%), XBB.1.5.24 (4.8%), XBB.2.3 (3.4%), XBB.1.22.1 (2.3%), XBB.1.22 (2.3%), XBB.1.22.2 (1.7%), EG.1 (1.7%), XBB.1.5.4 (1.4%), BA.2.10.1 (1.4%), XBB.1.16 (1.1%), CH.1.1.1 (1.1%), XBB.1 (0.9%), XBB.1.5.13 (0.9%), BA.2 (0.9%), EG.2 (0.9%), XBB.1.19.1 (0.6%), XBB.1.9 (0.6%), FL.1 (0.6%), XBB.1.5.23 (0.3%), XBB.1.5.21 (0.3%), XBB.1.19 (0.3%), BA.2.10 (0.3%), BQ.1.1 (0.3%), CH.1.1.11 (0.3%), XBB.1.5.20 (0.3%), EA.1 (0.3%), XBB.2.3.2 (0.3%), DB.1 (0.3%), XBB.1.28.1 (0.3%), XBB.1.5.11 (0.3%) (n = 351) 2023-03-05 2023-03-26 21 12 | Thailand XBB.1.9.1 (40.0%), BN.1.3 (20.0%), XBB.1.16 (10.0%), CH.1.1 (10.0%), XBB.1.5 (10.0%), XBL (10.0%) (n = 10) 2023-03-01 2023-03-21 20 13 | China BF.7.14 (23.3%), DY.2 (20.2%), DY.1 (10.0%), BA.5.2.48 (9.0%), DY.4 (8.3%), DY.3 (3.0%), BF.7.14.1 (2.9%), BF.7.14.5 (2.4%), BA.5.2.49 (2.4%), XBB.1.9.1 (2.1%), XBB.1.5 (1.8%), BF.7.14.4 (1.5%), XBB.1.16 (1.1%), BA.5.2 (1.0%), XBB.1.9.2 (0.8%), DY.1.1 (0.7%), XBB.1.16.1 (0.6%), BQ.1.1.13 (0.5%), FL.2 (0.5%), XBB.1.5.24 (0.5%), DZ.2 (0.4%), BF.7.14.6 (0.4%), BN.1.2.3 (0.4%), BN.1.3.5 (0.3%), BF.7.14.2 (0.3%), BA.5.2.50 (0.3%), XBB.1.22.1 (0.3%), BN.1.3 (0.3%), XBB.2.3 (0.3%), XBB.1.17.1 (0.2%), DZ.1 (0.2%), BF.7.14.7 (0.2%), XBB.1 (0.2%), BF.7.14.3 (0.2%), BN.1.3.9 (0.2%), XBB.1.5.23 (0.1%), BN.1.2 (0.1%), BN.1.5 (0.1%), XBB.1.22 (0.1%), CH.1.1 (0.1%), XBB.1.19.1 (0.1%), EG.1 (0.1%), BN.1 (0.1%), BN.1.3.6 (0.1%), XAY.1.1 (0.1%), BA.5.2.1 (0.1%), XBB.1.5.1 (0.1%), XBB.1.22.2 (0.1%), CH.1.1.1 (0.1%), XBB.2.3.3 (0.1%), FE.1 (0.1%), XBB.1.5.13 (0.1%), BN.1.3.10 (0.1%), BF.7.18 (0.1%), BQ.1.1 (0.1%), XBF (0.1%), BA.2.75 (0.1%), EJ.2 (0.1%), EL.1 (0.1%), BQ.1.2 (0.1%), XBB.1.12 (0.0%), XBF.3 (0.0%), XBB.1.5.20 (0.0%), BN.1.5.2 (0.0%), XBB.1.5.15 (0.0%), BA.5.2.6 (0.0%), XBB.1.5.16 (0.0%), BA.2 (0.0%), EU.1.1 (0.0%), BA.5 (0.0%), FK.1.1 (0.0%), XBB.2.3.2 (0.0%), CJ.1.3 (0.0%), BF.7 (0.0%), FB.1 (0.0%), CH.1.1.18 (0.0%), XBB.1.9.5 (0.0%), BA.5.1.32 (0.0%), XBB.1.5.38 (0.0%), BR.2.1 (0.0%), EG.2 (0.0%), BQ.1_17039 (0.0%), BN.1.4.3 (0.0%), EA.1 (0.0%), XBB.1.5.12 (0.0%), FL.1 (0.0%) (n = 3462) 2023-03-01 2023-04-16 46 14 | Israel XBB.1.5.1 (100.0%) (n = 1) 2023-03-01 2023-03-01 0 15 | South Africa XBB.1.5 (50.0%), XBB.1.5.25 (26.9%), XBB.1.22 (19.2%), XBB.1.5.15 (3.8%) (n = 26) 2023-03-02 2023-03-17 15 16 | Wales XBB.1.5 (36.1%), XBB.1.9.1 (8.7%), CH.1.1.1 (7.7%), EG.1 (6.1%), CH.1.1 (5.8%), XBB.1.5.7 (5.3%), XBB.1.9.2 (3.1%), XBB.1.5.18 (2.0%), FG.3 (1.7%), DV.1.1 (1.4%), XBB.1.5.36 (1.3%), ER.1 (1.2%), BQ.1.1.35 (1.2%), DV.1 (1.1%), BQ.1.1.2 (1.0%), BQ.1.1 (0.8%), BQ.1.1.23 (0.7%), EM.1 (0.6%), XBB.1.5.12 (0.6%), XBB.1.22 (0.5%), BQ.1.1.20 (0.5%), BQ.1.1.71 (0.5%), XBB.2.3.1 (0.5%), FL.2 (0.4%), XBB.1.17.1 (0.4%), XBB.1.5.16 (0.4%), EU.1.1 (0.4%), XBB.1.16.1 (0.4%), DV.3 (0.4%), BQ.1.1.8 (0.4%), BQ.1.1.36 (0.4%), EA.1 (0.4%), XBB.1 (0.3%), XBU (0.3%), XBB.1.5.19 (0.3%), BQ.1.1.72 (0.3%), XBB.1.5.21 (0.3%), FJ.1 (0.3%), DN.1.1 (0.3%), XBB.1.19.1 (0.3%), XBB.1.5.15 (0.3%), DV.3.1 (0.3%), XBB.1.5.20 (0.2%), CH.1.1.2 (0.2%), XBB.1.5.13 (0.2%), BQ.1.1.42 (0.2%), XBB.2.3 (0.2%), CH.1.1.13 (0.2%), XBB.1.5.24 (0.2%), XBF.9 (0.2%), CH.1.1.11 (0.2%), CH.1.1.5 (0.2%), XBB.1.18.1 (0.2%), XBB.1.5.14 (0.2%), BA.4.6.3 (0.1%), EK.3 (0.1%), EN.1 (0.1%), XBB.1.5.37 (0.1%), XBB.1.5.1 (0.1%), BQ.1.1.45 (0.1%), EU.1 (0.1%), BQ.1.1.29 (0.1%), XBF.7.1 (0.1%), CA.7 (0.1%), FH.1 (0.1%), EL.1 (0.1%), BQ.1.1.10 (0.1%), XBB.1.5.23 (0.1%), XBB.1.5.33 (0.1%), XBB.2.3.4 (0.1%), XBB.1.19 (0.1%), BN.1.4 (0.1%), FD.2 (0.1%), ET.1 (0.1%), BQ.1.1.18 (0.1%), XBB.1.5.4 (0.1%), BQ.1.21 (0.1%), BQ.1.1.15 (0.1%), DU.1 (0.1%), CH.1.1.6 (0.1%), XBB.1.22.2 (0.1%), BN.1.5 (0.1%), XBB.1.5.31 (0.1%), XBK.1 (0.1%), XBB.1.5.30 (0.1%), XBB.1.5.17 (0.1%), XBF (0.1%), EF.2 (0.1%), XBB.1.16 (0.1%), FL.1 (0.1%) (n = 1844) 2023-03-01 2023-04-08 38 17 | Japan BA.5.1.5 (100.0%) (n = 1) 2023-03-03 2023-03-03 0 18 | Pakistan XBB.1.9.1 (30.6%), XBB.1.19.1 (25.0%), XBB.1 (16.7%), XBB.1.5.24 (8.3%), XBB.2.3 (5.6%), XBB.1.22.1 (5.6%), XBB.1.9.2 (2.8%), XBB.1.12 (2.8%), XBB.1.5 (2.8%) (n = 36) 2023-03-03 2023-03-14 11 19 | Italy XBB.1.5.7 (100.0%) (n = 1) 2023-03-21 2023-03-21 0 20 | -------------------------------------------------------------------------------- /examples/readme_examples: -------------------------------------------------------------------------------- 1 | # This directory contains the input and output files of the examples provided in ReporTree wiki 2 | -------------------------------------------------------------------------------- /reportree_env.yml: -------------------------------------------------------------------------------- 1 | name: reportree 2 | channels: 3 | - conda-forge 4 | - etetoolkit 5 | - anaconda 6 | - bioconda 7 | - defaults 8 | - insapathogenomics 9 | dependencies: 10 | - biopython=1.77 11 | - cgmlst-dists_genpat-it=0.4.0 12 | - ete3 13 | - grapetree=2.1 14 | - numba=0.55.1 15 | - numpy=1.19.2 16 | - numpy-base=1.19.2 17 | - pandas=1.1.3 18 | - python=3.8 19 | - scikit-learn 20 | - snp-sites=2.5.1 21 | - treecluster=1.0.3 22 | - zip 23 | - pytest 24 | -------------------------------------------------------------------------------- /reportree_env_osx.yml: -------------------------------------------------------------------------------- 1 | name: reportree 2 | channels: 3 | - etetoolkit 4 | - anaconda 5 | - bioconda 6 | - conda-forge 7 | - defaults 8 | dependencies: 9 | - biopython=1.77 10 | - cgmlst-dists 11 | - ete3 12 | - grapetree=2.1 13 | - numba=0.55.1 14 | - numpy=1.19.2 15 | - numpy-base=1.19.2 16 | - pandas=1.1.3 17 | - python=3.8 18 | - scikit-learn 19 | - snp-sites=2.5.1 20 | - treecluster=1.0.3 21 | - zip 22 | - pytest 23 | -------------------------------------------------------------------------------- /reportree_logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/insapathogenomics/ReporTree/d0e1d7526905a332b49de68ea3b7b7021159f91d/reportree_logo.png -------------------------------------------------------------------------------- /scripts/newick4reportree.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | 3 | """ 4 | Convert a Newick file into a newick format compatible with ReporTree 5 | 6 | By Veronica Mixao 7 | @INSA 8 | """ 9 | 10 | import argparse 11 | import textwrap 12 | import ete3 13 | 14 | def read_tree(tree, tree_format): 15 | t = ete3.Tree(tree, format = tree_format) 16 | 17 | return t 18 | 19 | def print_tree(tree, out): 20 | tree.write(outfile = str(out) + ".nwk") 21 | 22 | def main(): 23 | 24 | # argument options ---------- 25 | 26 | parser = argparse.ArgumentParser(prog="newick4reportree.py", formatter_class=argparse.RawDescriptionHelpFormatter, description=textwrap.dedent("""\ 27 | ############################################################################### 28 | # # 29 | # newick4reportree.py # 30 | # # 31 | ############################################################################### 32 | 33 | Convert a newick file into a Newick format compatible with ReporTree :-) 34 | 35 | -------------------------------------------------------------------------------""")) 36 | 37 | ## parameters 38 | 39 | parser.add_argument("-t", "--tree", dest="tree", required=True, type=str, help="Input tree in Newick format") 40 | parser.add_argument("-o", "--out", dest="output", required=True, type=str, help="Tag for output file") 41 | parser.add_argument("-f", "--format", dest="tree_format", required=True, type=int, help="Format of the Newick file provided as input (check http://etetoolkit.org/docs/latest/tutorial/tutorial_trees.html#reading-and-writing-newick-trees)") 42 | 43 | args = parser.parse_args() 44 | 45 | ## pipeline 46 | 47 | t = read_tree(args.tree, args.tree_format) 48 | print_tree(t, args.output) 49 | 50 | if __name__ == "__main__": 51 | main() 52 | -------------------------------------------------------------------------------- /tests/HC_test.py: -------------------------------------------------------------------------------- 1 | """ 2 | Runs Several Tests for ReporTree functionality 3 | author daniel.sobral@insa.min-saude.pt 4 | """ 5 | import os 6 | import shutil 7 | import subprocess 8 | import pandas as pd 9 | import datetime 10 | 11 | def test_HC(): 12 | 13 | if not os.path.exists(os.path.join("tests","TEST3")): 14 | os.mkdir(os.path.join("tests","TEST3")) 15 | 16 | result = subprocess.check_output("python reportree.py " + \ 17 | "-a tests/allele_matrix.tsv -out tests/TEST3/TEST3 --analysis HC -m tests/metadata.tsv " + \ 18 | "-f \'country_origin != B;date >= 2023-02-02\' --subset --missing-code 0 --loci-called 0.98 " + \ 19 | "--sample-ATCG-content 0.99 --nomenclature-file tests/nomenclature.tsv --nomenclature-code-levels single-8,country_origin " + \ 20 | "--columns_summary_report first_seq_date,last_seq_date,timespan_days,country_origin,n_country_origin,source_isolation,n_source_isolation " + \ 21 | "--partitions2report single-1-10", shell=True) 22 | 23 | 24 | assert(os.path.exists(os.path.join("tests","TEST3","TEST3_clusterComposition.tsv"))) 25 | assert(os.path.exists(os.path.join("tests","TEST3","TEST3_dist_hamming.tsv"))) 26 | assert(os.path.exists(os.path.join("tests","TEST3","TEST3_flt_samples_matrix.tsv"))) 27 | assert(os.path.exists(os.path.join("tests","TEST3","TEST3_loci_report.tsv"))) 28 | assert(os.path.exists(os.path.join("tests","TEST3","TEST3_metadata_w_partitions.tsv"))) 29 | metadata_w_partitions = pd.read_csv(os.path.join("tests","TEST3","TEST3_metadata_w_partitions.tsv"), sep='\t', header=None) 30 | assert(metadata_w_partitions.iloc[3,9] == "C1-X_Y_Z") 31 | assert(os.path.exists(os.path.join("tests","TEST3","TEST3_nomenclature_changes.tsv"))) 32 | current_date = "%s" % (datetime.date.today()) 33 | assert(os.path.exists(os.path.join("tests","TEST3","TEST3_nomenclature_code_"+current_date+"_summary.tsv"))) 34 | assert(os.path.exists(os.path.join("tests","TEST3","TEST3_partitions.tsv"))) 35 | assert(os.path.exists(os.path.join("tests","TEST3","TEST3_partitions_summary.tsv"))) 36 | assert(os.path.exists(os.path.join("tests","TEST3","TEST3_single_HC.nwk"))) 37 | assert(os.path.exists(os.path.join("tests","TEST3","TEST3_subset_matrix.tsv"))) 38 | 39 | shutil.rmtree(os.path.join("tests","TEST3")) 40 | -------------------------------------------------------------------------------- /tests/alignment.fas: -------------------------------------------------------------------------------- 1 | >sample1 2 | CTCGATCGATCGATCGATCGATCGATCGATCGATCGATATCGATCGATCGATCGATCGATCGATCGACTAGCTAGCTAGNNNCGATCGATCGATcGATC---GATCGATCGATACGTCGTCGTN 3 | >sample2 4 | ATCGATAGATCGATCGATCGATCNATCGATCNATCGNTCGATNNATCGANCGATCGATCGATNGATCGATCGATCGNTCGATCGATCGATCGATNGATCGGGGATCGATCGATCGATCGATTTT 5 | >sample3 6 | ATCGATCGTTCGATCGATCGATCTATGCTGCGATCGATCGATNGATCGANCGNTCGATCGATCGATCGATCGATCGNTCGATCGATCGATCGATcGATC---GTTCGATCGATCnATCGAAAAA 7 | >sample3_2 8 | ATCGATCGTTCGATCGATCGATCTATGCTGCGATCGATCGATNGATCGANCGNTCGATCGATCGATCGATCGATCGNTCGATCGATCGATCGATcGATC---GTTCGATCGATCnATCGAAAAA 9 | >sample4 10 | ATCGATCGACCatcgatcgatcgactagctaCTTTTTTTTATNGATCGANCGNTCGATCGATNGATCGATCGATCGNTCGATCGATCGATCGATcATCGGGGGTACGATCGATCGATTTTTTTT 11 | >sample5 12 | ATCGATCGATNCATCGATCGATCTATCGATCGATCGATCGAANAAAAAAACGNTCGATCGATNGATCGATCGATCGNTCGATCGATCGATCGATcGATC---GAAAAANAANAAANAAANAAAA 13 | >sample6 14 | ANCGATCGATCGCTCGATCGATCGATCGATCGATCGATCGATNGATCGANNNNNNNNNTGATCGATCGATCGATCGNTCGATTTTTTTTTCGATcGATC---GTTTTTTTTTTTTTTTTTTTTT 15 | >sample7 16 | ATCGATCGATCGACTGATCGATCTATCGATCGATCGATCGATNGATCGATCGNTCGATCTTTTTTTT-ATCGATCGNTCGATCGATCGATCCATNGATC---GATCGA-CGATCGATCGA-TGT 17 | >sample3_3 18 | ATCGATCGTTCGATCGATCGATCTATGCTGCGATCGATCGATNGATCGANCGNTCGATCNATCGATCGATCGATCGNTCGATCGATCGATCGATcGATC---GTTCGATCGATCGATCGAAAAA 19 | >sample8 20 | ATCGATCAAAAAAAAAAAAANNNNNNNNNNNNNNNNNNCGATNGATCGATCGNTCGATCGATCGATCGTTCGATCGNTCGATCGATCGNTCGATcGATC---GATCGATCGATCnTGGGTTTTA 21 | >sample9_2 22 | ATCGATCGATCGATCGACCGATCGA-CGATCGATCGATCGATNGATCGATCGNTCGATCGATCGATCTATCGAT-GNTCGATCGATCGATNGATNGATC---GATCGATCGATCGATCGATCGA 23 | >sample9 24 | ATCGATCGATCGATCGACCGATCGA-CGATCGATCGATCGATNGATCGATCGNTCGATCGATCGATCTATCGAT-GNTCGATCGATCGATNGATtGATC---GATCGATCGATCGATCGATCGA 25 | >sample10 26 | ATCCATCCATCGATCGATTGATCGATCGATCGATCGATCGAANAAAAAAACGATCGATCGATCGATCGACCCCTCGNTCGATCGATCGATCGATNGATC---GATCGATCGATCGATCGATCGA 27 | >sample11 28 | ATCGATCGATCGATCGATCCATCGATCGATCGATCGATCGATNGATCGATCGATCGATCGATCGATCGATCGAGGGGGGGGTTTTTCCCCTTAAATATC---GAAAAAAATTTTTTTTAAAAAA 29 | >sample15 30 | NTNGNTNGNT-GNTCGANCGTNCNANNGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATTTTTTTTTCCATCGATC---GAAAAAAAAAAAAAAAAAAAAA 31 | >sample_reference 32 | ATCGATCGATCGATCGATCGATGGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCC-GTAAAAAAAAAAAAAAAAAAAAA 33 | >sample12 34 | ATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATC---GAAAAAAAAAAAAAAAAAAAAA 35 | >sample13 36 | ATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGC-CAAAAAAAAAAAAAAAAAAAAA 37 | >sample14 38 | ATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATC---GAAAAAAAAAAAAAAAAAAAAA 39 | -------------------------------------------------------------------------------- /tests/alignment_test.py: -------------------------------------------------------------------------------- 1 | """ 2 | Runs Several Tests for ReporTree functionality 3 | author daniel.sobral@insa.min-saude.pt 4 | """ 5 | import os 6 | import shutil 7 | import subprocess 8 | import pandas as pd 9 | import datetime 10 | 11 | def test_alignment(): 12 | 13 | if not os.path.exists(os.path.join("tests","TEST1")): 14 | os.mkdir(os.path.join("tests","TEST1")) 15 | 16 | result = subprocess.check_output("python reportree.py " + \ 17 | "-align tests/alignment.fas -out tests/TEST1/TEST1 --analysis grapetree -m tests/metadata.tsv " + \ 18 | "-f \'country_origin != B;date >= 2023-02-02\' --subset --missing-code N --site-inclusion 0.9 --sample-ATCG-content 0.9 " + \ 19 | "--remove-reference --use-reference-coords -r sample_reference --get-position-correspondence all " + \ 20 | "--nomenclature-file tests/nomenclature.tsv --nomenclature-code-levels 15,7,4 --columns_summary_report " + \ 21 | "first_seq_date,last_seq_date,timespan_days,country_origin,n_country_origin,source_isolation,n_source_isolation " + \ 22 | "--partitions2report stability_regions --sample_of_interest sample9 --zoom-cluster-of-interest 15 --subtree-of-interest 5 " + \ 23 | "--frequency-matrix country_origin,source_isolation --count-matrix nomenclature_code,country_origin --pivot", shell=True) 24 | 25 | 26 | 27 | ### Check if files exist: 28 | assert(os.path.exists(os.path.join("tests","TEST1","TEST1.log"))) 29 | assert(os.path.exists(os.path.join("tests","TEST1","TEST1.nwk"))) 30 | assert(os.path.exists(os.path.join("tests","TEST1","TEST1_SAMPLES_OF_INTEREST_partitions_summary.tsv"))) 31 | assert(os.path.exists(os.path.join("tests","TEST1","TEST1_MST-15x1.0_cluster_2.zip"))) 32 | assert(os.path.exists(os.path.join("tests","TEST1","TEST1_align_position_correspondence.tsv"))) 33 | 34 | align_position_correspondence = pd.read_csv(os.path.join("tests","TEST1","TEST1_align_position_correspondence.tsv"), sep='\t', header=None) 35 | assert(align_position_correspondence.iloc[9,3] == "9 (T)") 36 | assert(align_position_correspondence.iloc[26,11] == "25.1") 37 | 38 | assert(os.path.exists(os.path.join("tests","TEST1","TEST1_align_profile.fasta"))) 39 | assert(os.path.exists(os.path.join("tests","TEST1","TEST1_align_profile.tsv"))) 40 | 41 | align_profile = pd.read_csv(os.path.join("tests","TEST1","TEST1_align_profile.tsv"), sep='\t', header=None) 42 | assert(len(align_profile) == 15) 43 | assert(len(align_profile.columns) == 95) 44 | 45 | assert(os.path.exists(os.path.join("tests","TEST1","TEST1_clusterComposition.tsv"))) 46 | cluster_composition = pd.read_csv(os.path.join("tests","TEST1","TEST1_clusterComposition.tsv"), sep='\t', header=None) 47 | assert(len(cluster_composition) == 207) 48 | assert(len(cluster_composition.columns) == 4) 49 | 50 | assert(os.path.exists(os.path.join("tests","TEST1","TEST1_country_origin_source_isolation_freq_matrix.tsv"))) 51 | assert(os.path.exists(os.path.join("tests","TEST1","TEST1_country_origin_source_isolation_freq_pivot.tsv"))) 52 | assert(os.path.exists(os.path.join("tests","TEST1","TEST1_dist.tsv"))) 53 | assert(os.path.exists(os.path.join("tests","TEST1","TEST1_dist_grapetree.tsv"))) 54 | assert(os.path.exists(os.path.join("tests","TEST1","TEST1_dist_hamming.tsv"))) 55 | 56 | assert(os.path.exists(os.path.join("tests","TEST1","TEST1_metadata_w_partitions.tsv"))) 57 | metadata_w_partitions = pd.read_csv(os.path.join("tests","TEST1","TEST1_metadata_w_partitions.tsv"), sep='\t', header=None) 58 | assert(metadata_w_partitions.iloc[0,9] == "nomenclature_code_" + "%s" % (datetime.date.today())) 59 | assert(metadata_w_partitions.iloc[0,11] == "MST-21x1.0") 60 | assert(metadata_w_partitions.iloc[0,14] == "QUAL_called") 61 | assert(metadata_w_partitions.iloc[12,9] == "C2-C2-C2.1") 62 | 63 | assert(os.path.exists(os.path.join("tests","TEST1","TEST1_metrics.tsv"))) 64 | 65 | assert(os.path.exists(os.path.join("tests","TEST1","TEST1_nomenclature_changes.tsv"))) 66 | nomenclature_changes = pd.read_csv(os.path.join("tests","TEST1","TEST1_nomenclature_changes.tsv"), sep='\t', header=None) 67 | assert(nomenclature_changes.iloc[1,2] == "7") 68 | assert(nomenclature_changes.iloc[9,6] == "1") 69 | 70 | assert(os.path.exists(os.path.join("tests","TEST1","TEST1_partitions.tsv"))) 71 | assert(os.path.exists(os.path.join("tests","TEST1","TEST1_partitions_summary.tsv"))) 72 | partitions_summary = pd.read_csv(os.path.join("tests","TEST1","TEST1_partitions_summary.tsv"), sep='\t', header=None) 73 | assert(partitions_summary.iloc[1,9] == 'A (83.3%), X Y Z (16.7%) (n = 12)') 74 | 75 | assert(os.path.exists(os.path.join("tests","TEST1","TEST1_pos_report.tsv"))) 76 | assert(os.path.exists(os.path.join("tests","TEST1","TEST1_redundantSamples.txt"))) 77 | assert(os.path.exists(os.path.join("tests","TEST1","TEST1_sample9_closest5.zip"))) 78 | assert(os.path.exists(os.path.join("tests","TEST1","TEST1_samples_excluded.txt"))) 79 | assert(os.path.exists(os.path.join("tests","TEST1","TEST1_stableRegions.tsv"))) 80 | 81 | shutil.rmtree(os.path.join("tests","TEST1")) 82 | 83 | -------------------------------------------------------------------------------- /tests/allele_matrix.tsv: -------------------------------------------------------------------------------- 1 | #ID 1 4 7 8 9 10 12 13 14 15 16 17 18 19 20 21 22 23 25 27 28 29 30 31 32 33 35 36 37 39 40 41 42 44 45 46 47 48 49 51 52 54 55 56 57 58 59 60 61 62 64 65 66 67 69 70 71 72 73 74 76 78 79 81 83 84 85 87 88 89 90 92 94 96 97 98 99 103 104 105 106 109 110 111 112 113 115 116 117 118 119 121 122 123 2 | sample1 C G C G A T G A T C G A T C G A T C A C G A T C G A C G A A T C G T C G A T C A T G A T C G A T C G T C G A T A G C T A C A G 0 C G A C G A T G T G A T C A T C G C G A T A G T C G T G T 0 3 | sample2 A G A G A T G A T C G A T C G A T C A C G A T C 0 A C G 0 C G A T 0 A T C G A C G T C G A T C G A T G A T C A T C G A T G T C A C G A C G A T G T G A T C A T C G C G A T C A T C G A T T T 4 | sample3 A G C G T T G A T C G A T C G A T C A G C T G C G A C G A C G A T G A T C G A C G T C G A T C G A T G A T C A T C G A T G T C A C G A C G A T G T G A T C T T C G C G A T C A T C G A A A A 5 | sample3_2 A G C G T T G A T C G A T C G A T C A G C T G C G A C G A C G A T G A T C G A C G T C G A T C G A T G A T C A T C G A T G T C A C G A C G A T G T G A T C T T C G C G A T C A T C G A A A A 6 | sample4 A G C G A C A T C G A T C G A T C G C A G C T A C T T T T T T A T G A T C G A C G T C G A T C G A T G A T C A T C G A T G T C A C G A C G A T G T A T C G T A C G C G A T C A T T T T T T T 7 | sample5 A G C G A T C A T C G A T C G A T C A C G A T C G A C G A C G A A A A A A A A C G T C G A T C G A T G A T C A T C G A T G T C A C G A C G A T G T G A T C A A A A A A 0 A A 0 A A A 0 A A A 8 | sample7 A G C G A T G A C T G A T C G A T C A C G A T C G A C G A C G A T G A T C G A C G T C G A T C T T T T T T T A T C G A T G T C A C G A C G A T C T G A T C A T C G C G A T C A T C G A T G T 9 | sample3_3 A G C G T T G A T C G A T C G A T C A G C T G C G A C G A C G A T G A T C G A C G T C G A T C 0 A T G A T C A T C G A T G T C A C G A C G A T G T G A T C T T C G C G A T C A T C G A A A A 10 | sample9_2 A G C G A T G A T C G A C C G A T C A C G A T C G A C G A C G A T G A T C G A C G T C G A T C G A T G A T C A T C G A T G T C A C G A C G A T G T G A T C A T C G C G A T C A T C G A C G A 11 | sample9 A G C G A T G A T C G A C C G A T C A C G A T C G A C G A C G A T G A T C G A C G T C G A T C G A T G A T C A T C G A T G T C A C G A C G A T G T G A T C A T C G C G A T C A T C G A C G A 12 | sample10 A C C C A T G A T C G A T T G A T C A C G A T C G A C G A C G A A A A A A A A C G T C G A T C G A T G A T C A C C C C T G T C A C G A C G A T G T G A T C A T C G C G A T C A T C G A C G A 13 | sample11 A G C G A T G A T C G A T C C A T C A C G A T C G A C G A C G A T G A T C G A C G T C G A T C G A T G A T C A T C G A G G G G G T T T C C C C T A T A T C A A A A A T T T T T T T A A A A A 14 | sample15 0 G 0 G 0 T G 0 T C G A 0 C G T 0 C A 0 G A T C G A C G A C G A T G A T C G A C G T C G A T C G A T G A T C A T C G A T G T C A T T T T T T T C T G A T C A A A A A A A A A A A A A A A A A 15 | sample12 A G C G A T G A T C G A T C G A T C A C G A T C G A C G A C G A T G A T C G A C G T C G A T C G A T G A T C A T C G A T G T C A C G A C G A T G T G A T C A A A A A A A A A A A A A A A A A 16 | -------------------------------------------------------------------------------- /tests/grapetree_test.py: -------------------------------------------------------------------------------- 1 | """ 2 | Runs Several Tests for ReporTree functionality 3 | author daniel.sobral@insa.min-saude.pt 4 | """ 5 | import os 6 | import shutil 7 | import subprocess 8 | import pandas as pd 9 | import datetime 10 | 11 | def test_grapetree(): 12 | 13 | if not os.path.exists(os.path.join("tests","TEST2")): 14 | os.mkdir(os.path.join("tests","TEST2")) 15 | 16 | result = subprocess.check_output("python reportree.py " + \ 17 | "-a tests/allele_matrix.tsv -out tests/TEST2/TEST2 --analysis grapetree -m tests/metadata.tsv " + \ 18 | "-f \'country_origin != B;date >= 2023-02-02\' --subset --missing-code 0 --loci-called 0.98 " + \ 19 | "--sample-ATCG-content 0.99 --nomenclature-file tests/nomenclature.tsv --nomenclature-code-levels 8,country_origin " + \ 20 | "--columns_summary_report first_seq_date,last_seq_date,timespan_days,country_origin,n_country_origin,source_isolation,n_source_isolation " + \ 21 | "--partitions2report 1-10", shell=True) 22 | 23 | 24 | ### Check if files exist: 25 | assert(os.path.exists(os.path.join("tests","TEST2","TEST2.log"))) 26 | assert(os.path.exists(os.path.join("tests","TEST2","TEST2.nwk"))) 27 | assert(os.path.exists(os.path.join("tests","TEST2","TEST2_clusterComposition.tsv"))) 28 | assert(os.path.exists(os.path.join("tests","TEST2","TEST2_dist.tsv"))) 29 | assert(os.path.exists(os.path.join("tests","TEST2","TEST2_dist_grapetree.tsv"))) 30 | assert(os.path.exists(os.path.join("tests","TEST2","TEST2_dist_hamming.tsv"))) 31 | assert(os.path.exists(os.path.join("tests","TEST2","TEST2_flt_samples_matrix.tsv"))) 32 | assert(os.path.exists(os.path.join("tests","TEST2","TEST2_loci_report.tsv"))) 33 | 34 | assert(os.path.exists(os.path.join("tests","TEST2","TEST2_metadata_w_partitions.tsv"))) 35 | metadata_w_partitions = pd.read_csv(os.path.join("tests","TEST2","TEST2_metadata_w_partitions.tsv"), sep='\t', header=None) 36 | assert(metadata_w_partitions.iloc[3,9] == "C1-X_Y_Z") 37 | 38 | assert(os.path.exists(os.path.join("tests","TEST2","TEST2_nomenclature_changes.tsv"))) 39 | current_date = "%s" % (datetime.date.today()) 40 | assert(os.path.exists(os.path.join("tests","TEST2","TEST2_nomenclature_code_"+current_date+"_summary.tsv"))) 41 | assert(os.path.exists(os.path.join("tests","TEST2","TEST2_partitions.tsv"))) 42 | assert(os.path.exists(os.path.join("tests","TEST2","TEST2_partitions_summary.tsv"))) 43 | assert(os.path.exists(os.path.join("tests","TEST2","TEST2_redundantSamples.txt"))) 44 | assert(os.path.exists(os.path.join("tests","TEST2","TEST2_subset_matrix.tsv"))) 45 | 46 | shutil.rmtree(os.path.join("tests","TEST2")) 47 | -------------------------------------------------------------------------------- /tests/metadata.tsv: -------------------------------------------------------------------------------- 1 | sample date country origin source isolation 2 | sample1 2023-03-02 X Y Z A 3 | sample2 2023-03-03 A B 4 | sample3 2023-03-04 X Y Z C 5 | sample3_2 2023-03-05 A D 6 | sample3_3 2023-03-06 A A 7 | sample4 2023-03-02 A B 8 | sample5 2023-03-03 A C 9 | sample6 2023-03-04 A D 10 | sample7 2023-03-05 X Y Z A 11 | sample8 2023-03-06 A B 12 | sample9 2023-03-02 A C 13 | sample9_2 2023-03-03 A D 14 | sample10 2023-03-04 A A 15 | sample11 2023-03-05 A B 16 | sample12 2023-03-01 A C 17 | sample13 2023-03-02 B D 18 | sample14 2023 A A 19 | sample15 2023-03-04 A B 20 | sample_reference 2023-03-05 A C 21 | -------------------------------------------------------------------------------- /tests/nomenclature.tsv: -------------------------------------------------------------------------------- 1 | sequence single-3x1.0 MST-4x1.0 MST-0.01 MST-0.02 single-0.02 single-0.03 root_dist-10x1.0 2 | sample1 cluster_1 cluster_1 cluster_1 cluster_1 cluster_1 cluster_1 cluster_1 3 | sample3 cluster_2 cluster_1 cluster_1 cluster_1 cluster_1 cluster_1 cluster_2 4 | sample3_2 cluster_3 cluster_1 cluster_1 cluster_1 cluster_1 cluster_1 cluster_3 5 | sample4 cluster_1 cluster_1 cluster_1 cluster_1 cluster_1 cluster_1 cluster_1 6 | sample7 cluster_2 cluster_1 cluster_1 cluster_1 cluster_1 cluster_1 cluster_2 7 | sample3_3 cluster_3 cluster_1 cluster_1 cluster_1 cluster_1 cluster_1 cluster_3 8 | sample9_2 cluster_1 cluster_2 cluster_2 cluster_2 cluster_2 cluster_2 cluster_1 9 | sample9 cluster_2 cluster_2 cluster_2 cluster_2 cluster_2 cluster_2 cluster_2 10 | sample10 cluster_3 cluster_2 cluster_2 cluster_2 cluster_2 cluster_2 cluster_3 11 | sample11 cluster_1 cluster_2 cluster_2 cluster_2 cluster_2 cluster_2 cluster_1 12 | sample12 cluster_2 cluster_2 cluster_2 cluster_2 cluster_2 cluster_2 cluster_2 13 | sample13 cluster_3 cluster_2 cluster_2 cluster_2 cluster_2 cluster_2 cluster_3 14 | sample14 singleton_1 cluster_2 cluster_2 cluster_2 cluster_2 cluster_2 singleton_1 15 | extra cluster_1 cluster_1 cluster_1 cluster_1 cluster_1 cluster_1 cluster_1 16 | -------------------------------------------------------------------------------- /tests/pos_list.tsv: -------------------------------------------------------------------------------- 1 | sample1 sample2 2 | 1 1 3 | 12 25 4 | 1111 5 | 6 | -------------------------------------------------------------------------------- /tests/tree.nwk: -------------------------------------------------------------------------------- 1 | (((((((sample9:0.00,sample9_2:0.00):9.00,((sample3_2:0.00,sample3:0.00):0.00,sample3_3:0.00):9.00):2.00,sample7:11.00):1.00,sample10:12.00):1.00,sample12:13.00):8.00,sample11:21.00):14.00,sample4:35.00); 2 | -------------------------------------------------------------------------------- /tests/treecluster_test.py: -------------------------------------------------------------------------------- 1 | """ 2 | Runs Several Tests for ReporTree functionality 3 | author daniel.sobral@insa.min-saude.pt 4 | """ 5 | import os 6 | import shutil 7 | import subprocess 8 | import pandas as pd 9 | import datetime 10 | 11 | def test_treecluster(): 12 | 13 | if not os.path.exists(os.path.join("tests","TEST4")): 14 | os.mkdir(os.path.join("tests","TEST4")) 15 | 16 | result = subprocess.check_output("python reportree.py -t tests/tree.nwk " + \ 17 | "-out tests/TEST4/TEST4 --analysis HC -m tests/metadata.tsv --nomenclature-file tests/nomenclature.tsv " + \ 18 | "--columns_summary_report first_seq_date,last_seq_date,timespan_days,country_origin,n_country_origin,source_isolation,n_source_isolation", shell=True) 19 | 20 | assert(os.path.exists(os.path.join("tests","TEST4","TEST4.log"))) 21 | assert(os.path.exists(os.path.join("tests","TEST4","TEST4_clusterComposition.tsv"))) 22 | assert(os.path.exists(os.path.join("tests","TEST4","TEST4_metadata_w_partitions.tsv"))) 23 | 24 | assert(os.path.exists(os.path.join("tests","TEST4","TEST4_nomenclature_changes.tsv"))) 25 | nomenclature_changes = pd.read_csv(os.path.join("tests","TEST4","TEST4_nomenclature_changes.tsv"), sep='\t', header=None) 26 | assert(nomenclature_changes.iloc[2,2] == "multiple clusters") 27 | assert(nomenclature_changes.iloc[2,3] == "new (split_merge)") 28 | 29 | assert(os.path.exists(os.path.join("tests","TEST4","TEST4_partitions.tsv"))) 30 | assert(os.path.exists(os.path.join("tests","TEST4","TEST4_partitions_summary.tsv"))) 31 | 32 | shutil.rmtree(os.path.join("tests","TEST4")) 33 | 34 | -------------------------------------------------------------------------------- /useful_loci_lists/README.md: -------------------------------------------------------------------------------- 1 | # Useful loci lists 2 | This directory contains loci lists that may be useful for clustering analysis in routine surveillance: 3 | 4 | - _cgMLST_Salmonella_enterica_chewie-NS.txt_ - List of cgMLST loci of the INNUENDO wgMLST schema available at [chewie-NS for _Salmonella enterica_](https://chewbbaca.online/species/8) 5 | 6 | - _cgMLST_Escherichia_coli_chewie-NS.txt_ - List of cgMLST loci of the INNUENDO wgMLST schema available at [chewie-NS for _Escherichia coli_](https://chewbbaca.online/species/5) 7 | 8 | - _cgMLST_Campylobacter_jejuni_chewie-NS.txt_ - List of cgMLST loci of the INNUENDO wgMLST schema available at [chewie-NS for _Campylobacter jejuni_](https://chewbbaca.online/species/4) 9 | -------------------------------------------------------------------------------- /useful_loci_lists/cgMLST_Campylobacter_jejuni_chewie-NS.txt: -------------------------------------------------------------------------------- 1 | INNUENDO_wgMLST-00013232 2 | INNUENDO_wgMLST-00013233 3 | INNUENDO_wgMLST-00013234 4 | INNUENDO_wgMLST-00013236 5 | INNUENDO_wgMLST-00013241 6 | INNUENDO_wgMLST-00013245 7 | INNUENDO_wgMLST-00013246 8 | INNUENDO_wgMLST-00013247 9 | INNUENDO_wgMLST-00013250 10 | INNUENDO_wgMLST-00013251 11 | INNUENDO_wgMLST-00013252 12 | INNUENDO_wgMLST-00013254 13 | INNUENDO_wgMLST-00013257 14 | INNUENDO_wgMLST-00013260 15 | INNUENDO_wgMLST-00013261 16 | INNUENDO_wgMLST-00013262 17 | INNUENDO_wgMLST-00013264 18 | INNUENDO_wgMLST-00013266 19 | INNUENDO_wgMLST-00013267 20 | INNUENDO_wgMLST-00013269 21 | INNUENDO_wgMLST-00013271 22 | INNUENDO_wgMLST-00013272 23 | INNUENDO_wgMLST-00013273 24 | INNUENDO_wgMLST-00013274 25 | INNUENDO_wgMLST-00013275 26 | INNUENDO_wgMLST-00013276 27 | INNUENDO_wgMLST-00013278 28 | INNUENDO_wgMLST-00013282 29 | INNUENDO_wgMLST-00013283 30 | INNUENDO_wgMLST-00013289 31 | INNUENDO_wgMLST-00013290 32 | INNUENDO_wgMLST-00013292 33 | INNUENDO_wgMLST-00013294 34 | INNUENDO_wgMLST-00013303 35 | INNUENDO_wgMLST-00013304 36 | INNUENDO_wgMLST-00013306 37 | INNUENDO_wgMLST-00013307 38 | INNUENDO_wgMLST-00013308 39 | INNUENDO_wgMLST-00013309 40 | INNUENDO_wgMLST-00013310 41 | INNUENDO_wgMLST-00013313 42 | INNUENDO_wgMLST-00013315 43 | INNUENDO_wgMLST-00013316 44 | INNUENDO_wgMLST-00013317 45 | INNUENDO_wgMLST-00013320 46 | INNUENDO_wgMLST-00013321 47 | INNUENDO_wgMLST-00013322 48 | INNUENDO_wgMLST-00013323 49 | INNUENDO_wgMLST-00013326 50 | INNUENDO_wgMLST-00013329 51 | INNUENDO_wgMLST-00013332 52 | INNUENDO_wgMLST-00013333 53 | INNUENDO_wgMLST-00013336 54 | INNUENDO_wgMLST-00013337 55 | INNUENDO_wgMLST-00013338 56 | INNUENDO_wgMLST-00013339 57 | INNUENDO_wgMLST-00013341 58 | INNUENDO_wgMLST-00013342 59 | INNUENDO_wgMLST-00013343 60 | INNUENDO_wgMLST-00013350 61 | INNUENDO_wgMLST-00013352 62 | INNUENDO_wgMLST-00013353 63 | INNUENDO_wgMLST-00013354 64 | INNUENDO_wgMLST-00013355 65 | INNUENDO_wgMLST-00013356 66 | INNUENDO_wgMLST-00013357 67 | INNUENDO_wgMLST-00013359 68 | INNUENDO_wgMLST-00013361 69 | INNUENDO_wgMLST-00013362 70 | INNUENDO_wgMLST-00013363 71 | INNUENDO_wgMLST-00013365 72 | INNUENDO_wgMLST-00013366 73 | INNUENDO_wgMLST-00013367 74 | INNUENDO_wgMLST-00013368 75 | INNUENDO_wgMLST-00013369 76 | INNUENDO_wgMLST-00013373 77 | INNUENDO_wgMLST-00013379 78 | INNUENDO_wgMLST-00013380 79 | INNUENDO_wgMLST-00013381 80 | INNUENDO_wgMLST-00013383 81 | INNUENDO_wgMLST-00013391 82 | INNUENDO_wgMLST-00013393 83 | INNUENDO_wgMLST-00013397 84 | INNUENDO_wgMLST-00013400 85 | INNUENDO_wgMLST-00013401 86 | INNUENDO_wgMLST-00013402 87 | INNUENDO_wgMLST-00013403 88 | INNUENDO_wgMLST-00013404 89 | INNUENDO_wgMLST-00013407 90 | INNUENDO_wgMLST-00013408 91 | INNUENDO_wgMLST-00013410 92 | INNUENDO_wgMLST-00013411 93 | INNUENDO_wgMLST-00013412 94 | INNUENDO_wgMLST-00013413 95 | INNUENDO_wgMLST-00013414 96 | INNUENDO_wgMLST-00013415 97 | INNUENDO_wgMLST-00013416 98 | INNUENDO_wgMLST-00013417 99 | INNUENDO_wgMLST-00013424 100 | INNUENDO_wgMLST-00013426 101 | INNUENDO_wgMLST-00013427 102 | INNUENDO_wgMLST-00013428 103 | INNUENDO_wgMLST-00013430 104 | INNUENDO_wgMLST-00013431 105 | INNUENDO_wgMLST-00013432 106 | INNUENDO_wgMLST-00013433 107 | INNUENDO_wgMLST-00013434 108 | INNUENDO_wgMLST-00013435 109 | INNUENDO_wgMLST-00013437 110 | INNUENDO_wgMLST-00013438 111 | INNUENDO_wgMLST-00013439 112 | INNUENDO_wgMLST-00013441 113 | INNUENDO_wgMLST-00013442 114 | INNUENDO_wgMLST-00013443 115 | INNUENDO_wgMLST-00013444 116 | INNUENDO_wgMLST-00013445 117 | INNUENDO_wgMLST-00013446 118 | INNUENDO_wgMLST-00013447 119 | INNUENDO_wgMLST-00013451 120 | INNUENDO_wgMLST-00013452 121 | INNUENDO_wgMLST-00013453 122 | INNUENDO_wgMLST-00013454 123 | INNUENDO_wgMLST-00013455 124 | INNUENDO_wgMLST-00013456 125 | INNUENDO_wgMLST-00013457 126 | INNUENDO_wgMLST-00013458 127 | INNUENDO_wgMLST-00013459 128 | INNUENDO_wgMLST-00013463 129 | INNUENDO_wgMLST-00013464 130 | INNUENDO_wgMLST-00013465 131 | INNUENDO_wgMLST-00013466 132 | INNUENDO_wgMLST-00013467 133 | INNUENDO_wgMLST-00013469 134 | INNUENDO_wgMLST-00013470 135 | INNUENDO_wgMLST-00013471 136 | INNUENDO_wgMLST-00013472 137 | INNUENDO_wgMLST-00013473 138 | INNUENDO_wgMLST-00013474 139 | INNUENDO_wgMLST-00013475 140 | INNUENDO_wgMLST-00013476 141 | INNUENDO_wgMLST-00013478 142 | INNUENDO_wgMLST-00013480 143 | INNUENDO_wgMLST-00013482 144 | INNUENDO_wgMLST-00013484 145 | INNUENDO_wgMLST-00013486 146 | INNUENDO_wgMLST-00013487 147 | INNUENDO_wgMLST-00013490 148 | INNUENDO_wgMLST-00013492 149 | INNUENDO_wgMLST-00013493 150 | INNUENDO_wgMLST-00013494 151 | INNUENDO_wgMLST-00013495 152 | INNUENDO_wgMLST-00013496 153 | INNUENDO_wgMLST-00013502 154 | INNUENDO_wgMLST-00013504 155 | INNUENDO_wgMLST-00013505 156 | INNUENDO_wgMLST-00013506 157 | INNUENDO_wgMLST-00013508 158 | INNUENDO_wgMLST-00013509 159 | INNUENDO_wgMLST-00013514 160 | INNUENDO_wgMLST-00013539 161 | INNUENDO_wgMLST-00013557 162 | INNUENDO_wgMLST-00013559 163 | INNUENDO_wgMLST-00013566 164 | INNUENDO_wgMLST-00013571 165 | INNUENDO_wgMLST-00013573 166 | INNUENDO_wgMLST-00013575 167 | INNUENDO_wgMLST-00013578 168 | INNUENDO_wgMLST-00013609 169 | INNUENDO_wgMLST-00013614 170 | INNUENDO_wgMLST-00013616 171 | INNUENDO_wgMLST-00013630 172 | INNUENDO_wgMLST-00013633 173 | INNUENDO_wgMLST-00013636 174 | INNUENDO_wgMLST-00013637 175 | INNUENDO_wgMLST-00013638 176 | INNUENDO_wgMLST-00013647 177 | INNUENDO_wgMLST-00013664 178 | INNUENDO_wgMLST-00013669 179 | INNUENDO_wgMLST-00013686 180 | INNUENDO_wgMLST-00013699 181 | INNUENDO_wgMLST-00013710 182 | INNUENDO_wgMLST-00013711 183 | INNUENDO_wgMLST-00013712 184 | INNUENDO_wgMLST-00013713 185 | INNUENDO_wgMLST-00013715 186 | INNUENDO_wgMLST-00013717 187 | INNUENDO_wgMLST-00013719 188 | INNUENDO_wgMLST-00013721 189 | INNUENDO_wgMLST-00013722 190 | INNUENDO_wgMLST-00013733 191 | INNUENDO_wgMLST-00013765 192 | INNUENDO_wgMLST-00013778 193 | INNUENDO_wgMLST-00013780 194 | INNUENDO_wgMLST-00013794 195 | INNUENDO_wgMLST-00013795 196 | INNUENDO_wgMLST-00013796 197 | INNUENDO_wgMLST-00013797 198 | INNUENDO_wgMLST-00013798 199 | 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