├── scripts ├── 05_EASEL │ ├── .nextflow │ │ ├── cache │ │ │ ├── 6e2b4859-4b6c-42c8-835a-ed9985ddf7ae │ │ │ │ ├── db │ │ │ │ │ ├── LOCK │ │ │ │ │ ├── CURRENT │ │ │ │ │ ├── 000003.log │ │ │ │ │ └── MANIFEST-000002 │ │ │ │ └── index.adoring_ekeblad │ │ │ └── b8ee6227-e332-4451-9670-dc4bc642c95a │ │ │ │ ├── db │ │ │ │ ├── LOCK │ │ │ │ ├── CURRENT │ │ │ │ ├── 000003.log │ │ │ │ └── MANIFEST-000002 │ │ │ │ └── index.dreamy_leavitt │ │ └── history │ ├── easel.sh │ └── params.yaml ├── 03_helixer │ ├── entap_config.txt │ ├── 01_helixer.sh │ ├── 05_EnTAP.sh │ ├── 03_agat_codingsequences.sh │ ├── 04_busco.sh │ └── 02_agat_stats.sh ├── 04_braker_proteins │ ├── entap_config.txt │ ├── 01_getproteins.sh │ ├── 06_EnTAP.sh │ ├── 04_agat_codingsequences.sh │ ├── 05_busco.sh │ ├── 02_braker_proteins.sh │ ├── 03_agat_stats.sh │ └── busco_downloads │ │ └── file_versions.tsv ├── 02_mask_repeats │ ├── 01_create_db.sh │ ├── 02_repeatmodeler.sh │ ├── 03_repeatmasker.sh │ └── 04_busco.sh ├── alignment_exercise │ ├── 01_hisat2index.sh │ ├── 02_align.sh │ ├── 04_braker_qualimap.sh │ ├── 03_helixer_qualimap.sh │ └── Arabidopsis_thaliana.agat.log ├── 06_gffcompare │ └── 01_gffcompare.sh ├── 01_raw_data │ ├── get_genome.sh │ ├── sra_download.sh │ └── symlink_data.sh └── runall.sh ├── .DS_Store ├── .gitignore ├── images ├── busco_gfacts.png ├── gc_content.png ├── 15_braker_busco.png ├── busco_marker_rounds.png ├── fastqc_basic_stats.png ├── per_base_seq_content.png ├── per_seq_quality_score.png ├── busco_08a_gfacs_general.png ├── 16_gfacts_mono_multi_busco.png ├── Per_base_sequence_quality.png ├── maker2_gfacs_mono-vs-multi.png ├── 16_gfacts_general_mono_multi_busco.png └── 11_gfacs_2_round_general_mono_multi.png ├── FASTQC.md ├── LICENSE └── README.md /scripts/05_EASEL/.nextflow/cache/6e2b4859-4b6c-42c8-835a-ed9985ddf7ae/db/LOCK: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /scripts/05_EASEL/.nextflow/cache/b8ee6227-e332-4451-9670-dc4bc642c95a/db/LOCK: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CBC-UCONN/Structural-Annotation/HEAD/.DS_Store -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | *.Rproj 3 | *.out 4 | *.err 5 | *.fastq 6 | *.gz 7 | **/data 8 | **/results -------------------------------------------------------------------------------- /scripts/05_EASEL/.nextflow/cache/6e2b4859-4b6c-42c8-835a-ed9985ddf7ae/db/CURRENT: -------------------------------------------------------------------------------- 1 | MANIFEST-000002 2 | 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/scripts/05_EASEL/.nextflow/cache/b8ee6227-e332-4451-9670-dc4bc642c95a/db/000003.log: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CBC-UCONN/Structural-Annotation/HEAD/scripts/05_EASEL/.nextflow/cache/b8ee6227-e332-4451-9670-dc4bc642c95a/db/000003.log -------------------------------------------------------------------------------- /scripts/05_EASEL/.nextflow/cache/6e2b4859-4b6c-42c8-835a-ed9985ddf7ae/db/MANIFEST-000002: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CBC-UCONN/Structural-Annotation/HEAD/scripts/05_EASEL/.nextflow/cache/6e2b4859-4b6c-42c8-835a-ed9985ddf7ae/db/MANIFEST-000002 -------------------------------------------------------------------------------- /scripts/05_EASEL/.nextflow/cache/b8ee6227-e332-4451-9670-dc4bc642c95a/db/MANIFEST-000002: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CBC-UCONN/Structural-Annotation/HEAD/scripts/05_EASEL/.nextflow/cache/b8ee6227-e332-4451-9670-dc4bc642c95a/db/MANIFEST-000002 -------------------------------------------------------------------------------- /scripts/05_EASEL/.nextflow/cache/b8ee6227-e332-4451-9670-dc4bc642c95a/index.dreamy_leavitt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CBC-UCONN/Structural-Annotation/HEAD/scripts/05_EASEL/.nextflow/cache/b8ee6227-e332-4451-9670-dc4bc642c95a/index.dreamy_leavitt -------------------------------------------------------------------------------- /scripts/05_EASEL/.nextflow/cache/6e2b4859-4b6c-42c8-835a-ed9985ddf7ae/index.adoring_ekeblad: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CBC-UCONN/Structural-Annotation/HEAD/scripts/05_EASEL/.nextflow/cache/6e2b4859-4b6c-42c8-835a-ed9985ddf7ae/index.adoring_ekeblad -------------------------------------------------------------------------------- /scripts/05_EASEL/easel.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=EASEL 3 | #SBATCH -N 1 4 | #SBATCH -n 1 5 | #SBATCH -c 4 6 | #SBATCH --partition=general 7 | #SBATCH --qos=general 8 | #SBATCH --mem=10G 9 | #SBATCH --mail-user= 10 | 11 | module load nextflow 12 | 13 | SINGULARITY_TMPDIR=$PWD 14 | export SINGULARITY_TMPDIR 15 | 16 | nextflow run -hub gitlab PlantGenomicsLab/easel -profile singularity,xanadu -params-file params.yaml 17 | 18 | 19 | -------------------------------------------------------------------------------- /scripts/05_EASEL/.nextflow/history: -------------------------------------------------------------------------------- 1 | 2023-05-26 13:45:11 - adoring_ekeblad - c15db1324a4a5e7cbd5b753a457f4d3c4600f6c9 6e2b4859-4b6c-42c8-835a-ed9985ddf7ae nextflow run -hub gitlab PlantGenomicsLab/easel -profile singularity,xanadu -params-file params.yaml 2 | 2023-05-29 05:53:38 - dreamy_leavitt - c15db1324a4a5e7cbd5b753a457f4d3c4600f6c9 b8ee6227-e332-4451-9670-dc4bc642c95a nextflow run -hub gitlab PlantGenomicsLab/easel -profile singularity,xanadu -params-file params.yaml 3 | -------------------------------------------------------------------------------- /FASTQC.md: -------------------------------------------------------------------------------- 1 | ## FASTQC SRR6852085_1.fastq 2 | 3 | ### basic statistics 4 | ![](images/fastqc_basic_stats.png) 5 | 6 | ### Per base sequence quality 7 | ![](images/Per_base_sequence_quality.png) 8 | 9 | ### Per sequence quality score 10 | ![](images/per_seq_quality_score.png) 11 | 12 | 13 | ### Per base sequence content 14 | ![](images/per_base_seq_content.png) 15 | 16 | ### Per sequence GC content 17 | ![](images/gc_content.png) 18 | 19 | 20 | 21 | -------------------------------------------------------------------------------- /scripts/03_helixer/entap_config.txt: -------------------------------------------------------------------------------- 1 | diamond_exe_path=diamond 2 | rsem_exe_path=/isg/shared/apps/EnTAP/0.9.0-beta/libs/RSEM-1.3.0 3 | genemarkst_exe_path=perl /isg/shared/apps/EnTAP/0.9.0-beta/libs/gmst_linux_64/gmst.pl 4 | eggnog_sql_database=/isg/shared/apps/EnTAP/0.9.0-beta/databases/databases/eggnog.db 5 | eggnog_dmnd_database=/isg/shared/apps/EnTAP/0.9.0-beta/databases/bin/eggnog_proteins.dmnd 6 | interpro_exe_path=interproscan.sh 7 | entap_database_sql_path= 8 | entap_database_bin_path=/isg/shared/apps/EnTAP/0.9.0-beta/databases/bin/entap_database.bin 9 | entap_graphing_script= 10 | -------------------------------------------------------------------------------- /scripts/04_braker_proteins/entap_config.txt: -------------------------------------------------------------------------------- 1 | diamond_exe_path=diamond 2 | rsem_exe_path=/isg/shared/apps/EnTAP/0.9.0-beta/libs/RSEM-1.3.0 3 | genemarkst_exe_path=perl /isg/shared/apps/EnTAP/0.9.0-beta/libs/gmst_linux_64/gmst.pl 4 | eggnog_sql_database=/isg/shared/apps/EnTAP/0.9.0-beta/databases/databases/eggnog.db 5 | eggnog_dmnd_database=/isg/shared/apps/EnTAP/0.9.0-beta/databases/bin/eggnog_proteins.dmnd 6 | interpro_exe_path=interproscan.sh 7 | entap_database_sql_path= 8 | entap_database_bin_path=/isg/shared/apps/EnTAP/0.9.0-beta/databases/bin/entap_database.bin 9 | entap_graphing_script= 10 | -------------------------------------------------------------------------------- /scripts/05_EASEL/params.yaml: -------------------------------------------------------------------------------- 1 | outdir : "arabidopsis" 2 | genome : "/core/cbc/tutorials/workshopdirs/Structural-Annotation-Version3/results/02_mask_repeats/repeatmasker/repeatmasker_out/GCF_000001735.4_TAIR10.1_genomic.fna.masked" 3 | user_reads : "/core/cbc/tutorials/workshopdirs/Structural-Annotation-Version3/data/rnaseq/*{1,2}.fastq" 4 | busco_lineage : "embryophyta" 5 | order : "Brassicales" 6 | prefix : "arabidopsis" 7 | reference_db : "/isg/shared/databases/Diamond/RefSeq/plant.protein.faa.208.dmnd" 8 | taxon : "arabidopsis" 9 | external_protein : "true" 10 | -------------------------------------------------------------------------------- /scripts/02_mask_repeats/01_create_db.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=repeatmodeler_db 3 | #SBATCH -N 1 4 | #SBATCH -n 1 5 | #SBATCH -c 1 6 | #SBATCH --partition=general 7 | #SBATCH --qos=general 8 | #SBATCH --mail-type=END 9 | #SBATCH --mail-user=your.email@uconn.edu 10 | #SBATCH --mem=10G 11 | #SBATCH -o %x_%j.out 12 | #SBATCH -e %x_%j.err 13 | 14 | hostname 15 | date 16 | 17 | # load software 18 | module load RepeatModeler/2.0.4 19 | 20 | # set variables 21 | REPDIR=../../results/02_mask_repeats 22 | mkdir -p ${REPDIR} 23 | 24 | # genome 25 | GENOME=../../data/genome/GCF_000001735.4_TAIR10.1_genomic.fna 26 | 27 | BuildDatabase -name "${REPDIR}/athaliana_db" ${GENOME} 28 | -------------------------------------------------------------------------------- /scripts/02_mask_repeats/02_repeatmodeler.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=repeatmodeler_model 3 | #SBATCH -N 1 4 | #SBATCH -n 1 5 | #SBATCH -c 30 6 | #SBATCH --partition=general 7 | #SBATCH --qos=general 8 | #SBATCH --mail-type=ALL 9 | #SBATCH --mail-user=your.email@uconn.edu 10 | #SBATCH --mem=50G 11 | #SBATCH -o %x_%j.out 12 | #SBATCH -e %x_%j.err 13 | 14 | hostname 15 | date 16 | 17 | # load software 18 | module load RepeatModeler/2.0.4 19 | module load ninja/0.95 20 | 21 | # go to repeat directory 22 | REPDIR=../../results/02_mask_repeats 23 | 24 | cd ${REPDIR} 25 | 26 | # set repdb and run repeatmodeler 27 | REPDB=athaliana_db 28 | 29 | RepeatModeler -threads 30 -database ${REPDB} -LTRStruct 30 | 31 | date 32 | 33 | -------------------------------------------------------------------------------- /scripts/02_mask_repeats/03_repeatmasker.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=repeatmasker 3 | #SBATCH -N 1 4 | #SBATCH -n 1 5 | #SBATCH -c 8 6 | #SBATCH --partition=xeon 7 | #SBATCH --qos=general 8 | #SBATCH --mail-type=ALL 9 | #SBATCH --mail-user=first.last@uconn.edu 10 | #SBATCH --mem=30G 11 | #SBATCH -o %x_%j.out 12 | #SBATCH -e %x_%j.err 13 | 14 | hostname 15 | date 16 | 17 | # load software 18 | module load RepeatMasker/4.1.2 19 | 20 | # set variables 21 | REPLIB=../../results/02_mask_repeats/athaliana_db-families.fa 22 | OUTDIR=../../results/02_mask_repeats/repeatmasker 23 | mkdir -p ${OUTDIR} 24 | 25 | GENOME=../../data/genome/GCF_000001735.4_TAIR10.1_genomic.fna 26 | 27 | RepeatMasker -dir ${OUTDIR}/repeatmasker_out -pa 8 -lib ${REPLIB} -gff -a -noisy -xsmall ${GENOME} 28 | 29 | date 30 | -------------------------------------------------------------------------------- /scripts/alignment_exercise/01_hisat2index.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=hisat2_index 3 | #SBATCH -n 1 4 | #SBATCH -N 1 5 | #SBATCH -c 4 6 | #SBATCH --mem=10G 7 | #SBATCH --partition=general 8 | #SBATCH --qos=general 9 | #SBATCH --mail-type=ALL 10 | #SBATCH --mail-user=first.last@uconn.edu 11 | #SBATCH -o %x_%j.out 12 | #SBATCH -e %x_%j.err 13 | 14 | echo `hostname` 15 | date 16 | 17 | ################################################################# 18 | # Index the Genome 19 | ################################################################# 20 | 21 | # load software 22 | module load hisat2/2.2.0 23 | 24 | # input/output directories 25 | OUTDIR=../../results/alignment_exercise/hisat2_index 26 | mkdir -p $OUTDIR 27 | 28 | GENOME=../../data/genome/GCF_000001735.4_TAIR10.1_genomic.fna 29 | 30 | hisat2-build -p 16 $GENOME $OUTDIR/Athaliana 31 | -------------------------------------------------------------------------------- /scripts/03_helixer/01_helixer.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=helixer 3 | #SBATCH -N 1 4 | #SBATCH -n 1 5 | #SBATCH -c 12 6 | #SBATCH --partition=gpu 7 | #SBATCH --qos=general 8 | #SBATCH --constraint="AVX2&FMA3" 9 | #SBATCH --mail-type=ALL 10 | #SBATCH --mem=20G 11 | #SBATCH --mail-user= 12 | #SBATCH -o %x_%j.out 13 | #SBATCH -e %x_%j.err 14 | 15 | hostname 16 | date 17 | 18 | module load singularity/3.9.2 19 | mkdir tmp 20 | 21 | export TMPDIR=$PWD/tmp 22 | OUTDIR=../../results/03_helixer/ 23 | mkdir -p ${OUTDIR} 24 | 25 | GENOME=../../results/02_mask_repeats/repeatmasker/repeatmasker_out/GCF_000001735.4_TAIR10.1_genomic.fna.masked 26 | 27 | singularity exec /isg/shared/databases/nfx_singularity_cache/helixer-docker_helixer_v0.3.0a0_cuda_11.2.0-cudnn8.sif Helixer.py --lineage land_plant --fasta-path ${GENOME} --gff-output-path ${OUTDIR}/Arabidopsis_thaliana.gff3 28 | -------------------------------------------------------------------------------- /scripts/04_braker_proteins/01_getproteins.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=get_proteins 3 | #SBATCH -N 1 4 | #SBATCH -n 1 5 | #SBATCH -c 2 6 | #SBATCH --partition=general 7 | #SBATCH --qos=general 8 | #SBATCH --mail-type=END 9 | #SBATCH --mail-user=first.last@uconn.edu 10 | #SBATCH --mem=10G 11 | #SBATCH -o %x_%j.out 12 | #SBATCH -e %x_%j.err 13 | 14 | hostname 15 | date 16 | 17 | OUTDIR=../../results/04_braker/proteins 18 | mkdir -p ${OUTDIR} 19 | cd ${OUTDIR} 20 | 21 | # per the braker documentation, a protein database can be obtained as below 22 | # this code downloads protein sequences in fasta format for all genes in orthoDB for the Viridiplantae 23 | # then it concatenates them into a single fasta file 24 | # https://github.com/gatech-genemark/ProtHint#protein-database-preparation 25 | wget --no-check-certificate https://v100.orthodb.org/download/odb10_plants_fasta.tar.gz 26 | tar -xvzf odb10_plants_fasta.tar.gz 27 | cat plants/Rawdata/*fs >proteins.fa 28 | -------------------------------------------------------------------------------- /scripts/03_helixer/05_EnTAP.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=entap_helixer 3 | #SBATCH -n 1 4 | #SBATCH -N 1 5 | #SBATCH -c 16 6 | #SBATCH --mem=50G 7 | #SBATCH --partition=general 8 | #SBATCH --qos=general 9 | #SBATCH --mail-type=ALL 10 | #SBATCH --mail-user= 11 | #SBATCH -o %x_%j.out 12 | #SBATCH -e %x_%j.err 13 | 14 | hostname 15 | date 16 | 17 | ########################################## 18 | ## EnTap ## 19 | ########################################## 20 | module load EnTAP/0.9.0-beta 21 | module load diamond/0.9.36 22 | 23 | OUTDIR=../../results/03_helixer/EnTAP 24 | mkdir -p ${OUTDIR} 25 | 26 | cp entap_config.txt ${OUTDIR} 27 | cd ${OUTDIR} 28 | 29 | PROTEINS=../agat/codingsequences/helixer_proteins.fasta.faa 30 | 31 | EnTAP --runP \ 32 | -i ${PROTEINS} \ 33 | -d /isg/shared/databases/Diamond/RefSeq/complete.protein.faa.216.dmnd \ 34 | -d /isg/shared/databases/Diamond/Uniprot/uniprot_sprot.dmnd \ 35 | --ontology 0 \ 36 | --threads 16 37 | 38 | date 39 | -------------------------------------------------------------------------------- /scripts/04_braker_proteins/06_EnTAP.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=entap_braker 3 | #SBATCH -n 1 4 | #SBATCH -N 1 5 | #SBATCH -c 16 6 | #SBATCH --mem=50G 7 | #SBATCH --partition=general 8 | #SBATCH --qos=general 9 | #SBATCH --mail-type=ALL 10 | #SBATCH --mail-user= 11 | #SBATCH -o %x_%j.out 12 | #SBATCH -e %x_%j.err 13 | 14 | hostname 15 | date 16 | 17 | ########################################## 18 | ## EnTap ## 19 | ########################################## 20 | module load EnTAP/0.9.0-beta 21 | module load diamond/0.9.36 22 | 23 | OUTDIR=../../results/04_braker/EnTAP 24 | mkdir -p ${OUTDIR} 25 | 26 | cp entap_config.txt ${OUTDIR} 27 | cd ${OUTDIR} 28 | 29 | PROTEINS=../agat/codingsequences/braker_proteins.fasta.faa 30 | 31 | EnTAP --runP \ 32 | -i ${PROTEINS} \ 33 | -d /isg/shared/databases/Diamond/RefSeq/complete.protein.faa.216.dmnd \ 34 | -d /isg/shared/databases/Diamond/Uniprot/uniprot_sprot.dmnd \ 35 | --ontology 0 \ 36 | --threads 16 37 | 38 | date 39 | -------------------------------------------------------------------------------- /scripts/06_gffcompare/01_gffcompare.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=gffcmp 3 | #SBATCH -n 1 4 | #SBATCH -N 1 5 | #SBATCH -c 1 6 | #SBATCH --mem=50G 7 | #SBATCH --partition=general 8 | #SBATCH --qos=general 9 | #SBATCH --mail-type=ALL 10 | #SBATCH --mail-user= 11 | #SBATCH -o %x_%j.out 12 | #SBATCH -e %x_%j.err 13 | 14 | hostname 15 | date 16 | 17 | module load gffcompare/0.10.4 18 | 19 | # output directory 20 | OUTDIR=../../results/06_gffcompare_2 21 | mkdir -p ${OUTDIR} 22 | 23 | # files 24 | NCBIANNO=../../data/genome/GCF_000001735.4_TAIR10.1_genomic.gff 25 | HELIXER=../../results/03_helixer/Arabidopsis_thaliana.gff3 26 | BRAKERPROTEIN=../../results/04_braker/proteins/braker/augustus.hints.gtf 27 | EASEL=../../results/05_EASEL/arabidopsis/final_predictions/arabidopsis_filtered.gtf 28 | 29 | # run gffcompare 30 | gffcompare -R -r <(awk '$7 !~ /?/' ${NCBIANNO}) -o ${OUTDIR}/helixer ${HELIXER} 31 | 32 | gffcompare -R -r <(awk '$7 !~ /?/' ${NCBIANNO}) -o ${OUTDIR}/braker ${BRAKERPROTEIN} 33 | 34 | gffcompare -R -r <(awk '$7 !~ /?/' ${NCBIANNO}) -o ${OUTDIR}/easel ${EASEL} 35 | 36 | -------------------------------------------------------------------------------- /scripts/03_helixer/03_agat_codingsequences.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=agat_codingsequences_helixer 3 | #SBATCH -N 1 4 | #SBATCH -n 1 5 | #SBATCH -c 1 6 | #SBATCH --partition=general 7 | #SBATCH --qos=general 8 | #SBATCH --mail-type=ALL 9 | #SBATCH --mem=50G 10 | #SBATCH --mail-user=your.name@uconn.edu 11 | #SBATCH -o %x_%j.out 12 | #SBATCH -e %x_%j.err 13 | 14 | hostname 15 | date 16 | 17 | module load singularity 18 | 19 | OUTDIR="../../results/03_helixer/agat/codingsequences" 20 | mkdir -p ${OUTDIR} 21 | 22 | cd ${OUTDIR} 23 | 24 | singularity exec /isg/shared/databases/nfx_singularity_cache/depot.galaxyproject.org-singularity-agat-1.0.0--pl5321hdfd78af_0.img agat_sp_keep_longest_isoform.pl \ 25 | --gff ../../Arabidopsis_thaliana.gff3 \ 26 | -o helixer_longest_isoform.gtf 27 | 28 | 29 | singularity exec /isg/shared/databases/nfx_singularity_cache/depot.galaxyproject.org-singularity-agat-1.0.0--pl5321hdfd78af_0.img agat_sp_extract_sequences.pl \ 30 | -g helixer_longest_isoform.gtf \ 31 | -f ../../../../data/genome/GCF_000001735.4_TAIR10.1_genomic.fna \ 32 | -p \ 33 | -o helixer_proteins.fasta.faa 34 | -------------------------------------------------------------------------------- /scripts/04_braker_proteins/04_agat_codingsequences.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=agat_codingsequences_braker 3 | #SBATCH -N 1 4 | #SBATCH -n 1 5 | #SBATCH -c 1 6 | #SBATCH --partition=general 7 | #SBATCH --qos=general 8 | #SBATCH --mail-type=ALL 9 | #SBATCH --mem=50G 10 | #SBATCH --mail-user=your.email@uconn.edu 11 | #SBATCH -o %x_%j.out 12 | #SBATCH -e %x_%j.err 13 | 14 | hostname 15 | date 16 | 17 | module load singularity 18 | 19 | OUTDIR="../../results/04_braker/agat/codingsequences" 20 | mkdir -p ${OUTDIR} 21 | 22 | cd ${OUTDIR} 23 | 24 | singularity exec /isg/shared/databases/nfx_singularity_cache/depot.galaxyproject.org-singularity-agat-1.0.0--pl5321hdfd78af_0.img agat_sp_keep_longest_isoform.pl \ 25 | --gff ../../proteins/braker/augustus.hints.gtf \ 26 | -o braker_longest_isoform.gtf 27 | 28 | 29 | singularity exec /isg/shared/databases/nfx_singularity_cache/depot.galaxyproject.org-singularity-agat-1.0.0--pl5321hdfd78af_0.img agat_sp_extract_sequences.pl \ 30 | -g braker_longest_isoform.gtf \ 31 | -f ../../../../data/genome/GCF_000001735.4_TAIR10.1_genomic.fna \ 32 | -p \ 33 | -o braker_proteins.fasta.faa 34 | -------------------------------------------------------------------------------- /scripts/04_braker_proteins/05_busco.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=busco_braker 3 | #SBATCH -n 1 4 | #SBATCH -N 1 5 | #SBATCH -c 8 6 | #SBATCH --mem=10G 7 | #SBATCH --partition=general 8 | #SBATCH --qos=general 9 | #SBATCH --mail-type=ALL 10 | #SBATCH --mail-user=your.email@uconn.edu 11 | #SBATCH -o %x_%A.out 12 | #SBATCH -e %x_%A.err 13 | 14 | hostname 15 | date 16 | 17 | ########################################################## 18 | ## BUSCO ## 19 | ########################################################## 20 | 21 | module load busco/5.4.5 22 | 23 | # green plant busco database. already on the xanadu cluster. see documentation for how to obtain 24 | BUSCODB=/isg/shared/databases/BUSCO/odb10/lineages/viridiplantae_odb10 25 | 26 | #Output directory 27 | OUTDIR=../../results/04_braker/busco 28 | mkdir -p ${OUTDIR} 29 | 30 | 31 | # predicted proteins 32 | PROTEINS=../../results/04_braker/agat/codingsequences/braker_proteins.fasta.faa 33 | 34 | 35 | # run busco 36 | busco -i ${PROTEINS} \ 37 | -o ${OUTDIR} \ 38 | -c 8 \ 39 | -l ${BUSCODB} \ 40 | -m prot \ 41 | -f 42 | -------------------------------------------------------------------------------- /scripts/03_helixer/04_busco.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=busco_helixer 3 | #SBATCH -n 1 4 | #SBATCH -N 1 5 | #SBATCH -c 8 6 | #SBATCH --mem=10G 7 | #SBATCH --partition=general 8 | #SBATCH --qos=general 9 | #SBATCH --mail-type=ALL 10 | #SBATCH --mail-user=your.email@uconn.edu 11 | #SBATCH -o %x_%A.out 12 | #SBATCH -e %x_%A.err 13 | 14 | hostname 15 | date 16 | 17 | ########################################################## 18 | ## BUSCO ## 19 | ########################################################## 20 | 21 | module load busco/5.4.5 22 | 23 | # green plant busco database. already on the xanadu cluster. see documentation for how to obtain 24 | BUSCODB=/isg/shared/databases/BUSCO/odb10/lineages/viridiplantae_odb10 25 | 26 | #Output directory 27 | OUTDIR=../../results/03_helixer/busco 28 | mkdir -p ${OUTDIR} 29 | 30 | 31 | 32 | # predicted proteins 33 | PROTEINS=../../results/03_helixer/agat/codingsequences/helixer_proteins.fasta.faa 34 | 35 | 36 | # run busco 37 | busco -i ${PROTEINS} \ 38 | -o ${OUTDIR} \ 39 | -c 8 \ 40 | -l ${BUSCODB} \ 41 | -m prot \ 42 | -f 43 | 44 | -------------------------------------------------------------------------------- /scripts/04_braker_proteins/02_braker_proteins.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=braker_proteins 3 | #SBATCH -N 1 4 | #SBATCH -n 1 5 | #SBATCH -c 16 6 | #SBATCH --partition=general 7 | #SBATCH --qos=general 8 | #SBATCH --mail-type=END 9 | #SBATCH --mail-user=first.last@uconn.edu 10 | #SBATCH --mem=20G 11 | #SBATCH -o %x_%j.out 12 | #SBATCH -e %x_%j.err 13 | 14 | hostname 15 | date 16 | 17 | # load software 18 | module load BRAKER/2.1.6 19 | module load ProtHint/2.6.0 20 | 21 | # for testing new perl version: 22 | module unload perl 23 | module load perl/5.36.0 24 | 25 | # set braker output directory and cd there 26 | OUTDIR=../../results/04_braker/proteins 27 | mkdir -p ${OUTDIR} 28 | cd ${OUTDIR} 29 | 30 | # copy augustus config, set variable 31 | export AUGUSTUS_CONFIG_PATH=$(pwd)/config 32 | cp -r /isg/shared/apps/augustus/3.6.0/config/ config 33 | 34 | # create a temp directory for braker temp files 35 | export TMPDIR=$(pwd)/braker_tmp 36 | mkdir -p ${TMPDIR} 37 | 38 | # set variables for input/output files 39 | GENOME="../../02_mask_repeats/repeatmasker/repeatmasker_out/GCF_000001735.4_TAIR10.1_genomic.fna.masked" 40 | PROTEINDB=proteins.fa 41 | 42 | # run braker 43 | braker.pl \ 44 | --genome=${GENOME} \ 45 | --prot_seq=${PROTEINDB} \ 46 | --softmasking \ 47 | --cores 16 \ 48 | --gff3 49 | -------------------------------------------------------------------------------- /scripts/01_raw_data/get_genome.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=getgenome 3 | #SBATCH -N 1 4 | #SBATCH -n 1 5 | #SBATCH -c 1 6 | #SBATCH --partition=general 7 | #SBATCH --qos=general 8 | #SBATCH --mail-type=END 9 | #SBATCH --mail-user=your.email@uconn.edu 10 | #SBATCH --mem=10G 11 | #SBATCH -o %x_%j.out 12 | #SBATCH -e %x_%j.err 13 | 14 | hostname 15 | date 16 | 17 | OUTDIR=../../data/genome 18 | mkdir -p ${OUTDIR} 19 | 20 | cd ${OUTDIR} 21 | 22 | # Data: Arabidopsis thaliana TAIR10.1 assembly. 23 | # NCBI Accession: GCF_000001735.4 24 | 25 | # download genome 26 | wget https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/001/735/GCF_000001735.4_TAIR10.1/GCF_000001735.4_TAIR10.1_genomic.fna.gz 27 | 28 | # decompress 29 | gunzip GCF_000001735.4_TAIR10.1_genomic.fna.gz 30 | 31 | # strip off extra sequence name info after the space, e.g. 32 | # this: >NC_003070.9 Arabidopsis thaliana chromosome 1 sequence 33 | # becomes this: >NC_003070.9 34 | sed 's/ .*//' GCF_000001735.4_TAIR10.1_genomic.fna >tmp.fna 35 | mv tmp.fna GCF_000001735.4_TAIR10.1_genomic.fna 36 | 37 | # download the annotation (for comparison) 38 | wget https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/001/735/GCF_000001735.4_TAIR10.1/GCF_000001735.4_TAIR10.1_genomic.gff.gz 39 | 40 | # decompress 41 | gunzip GCF_000001735.4_TAIR10.1_genomic.gff.gz 42 | -------------------------------------------------------------------------------- /scripts/02_mask_repeats/04_busco.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=busco 3 | #SBATCH -n 1 4 | #SBATCH -N 1 5 | #SBATCH -c 8 6 | #SBATCH --mem=10G 7 | #SBATCH --partition=xeon 8 | #SBATCH --qos=general 9 | #SBATCH --mail-type=ALL 10 | #SBATCH --mail-user=first.last@uconn.edu 11 | #SBATCH -o %x_%A.out 12 | #SBATCH -e %x_%A.err 13 | 14 | hostname 15 | date 16 | 17 | ########################################################## 18 | ## BUSCO ## 19 | ########################################################## 20 | 21 | module load busco/5.4.5 22 | 23 | # AUG_HOME=$HOME/Augustus 24 | # export AUGUSTUS_CONFIG_PATH=${AUG_HOME}/config 25 | 26 | # set output directory for busco, cd into parent directory 27 | BUSCODIR=../../results/02_mask_repeats/busco/ 28 | mkdir -p ${BUSCODIR} 29 | cd ${BUSCODIR} 30 | 31 | OUTDIR=masked_genome 32 | 33 | # input masked genome 34 | MASKED_GENOME=../../02_mask_repeats/repeatmasker/repeatmasker_out/GCF_000001735.4_TAIR10.1_genomic.fna.masked 35 | 36 | # green plant busco database, already downloaded on xanadu. see busco documentation for how to obtain 37 | BUSCODB=/isg/shared/databases/BUSCO/odb10/lineages/viridiplantae_odb10 38 | 39 | # run busco 40 | busco -i ${MASKED_GENOME} \ 41 | -o ${OUTDIR} \ 42 | -c 8 \ 43 | -f \ 44 | -l ${BUSCODB} \ 45 | -m genome 46 | 47 | 48 | -------------------------------------------------------------------------------- /scripts/alignment_exercise/02_align.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=hisat2_align 3 | #SBATCH -n 1 4 | #SBATCH -N 1 5 | #SBATCH -c 4 6 | #SBATCH --mem=20G 7 | #SBATCH --partition=xeon 8 | #SBATCH --qos=general 9 | #SBATCH --mail-type=ALL 10 | #SBATCH --mail-user=first.last@uconn.edu 11 | #SBATCH -o %x_%j.out 12 | #SBATCH -e %x_%j.err 13 | 14 | echo `hostname` 15 | 16 | ################################################################# 17 | # Align reads to genome 18 | ################################################################# 19 | module load hisat2/2.2.1 20 | module load samtools/1.12 21 | 22 | INDIR=../../data/rnaseq/ 23 | OUTDIR=../../results/alignment_exercise/alignments 24 | mkdir -p $OUTDIR 25 | 26 | INDEX=../../results/alignment_exercise/hisat2_index/Athaliana 27 | 28 | 29 | # run hisat2 30 | hisat2 \ 31 | -p 2 \ 32 | -x $INDEX \ 33 | -1 $INDIR/SRR6852085_1.fastq \ 34 | -2 $INDIR/SRR6852085_2.fastq | \ 35 | samtools view -@ 1 -S -h -u - | \ 36 | samtools sort -@ 1 -T SRR6852085 - >$OUTDIR/SRR6852085.bam 37 | 38 | # index bam files 39 | samtools index $OUTDIR/SRR6852085.bam 40 | 41 | # run hisat2 on second sample 42 | hisat2 \ 43 | -p 2 \ 44 | -x $INDEX \ 45 | -1 $INDIR/SRR6852086_1.fastq \ 46 | -2 $INDIR/SRR6852086_2.fastq | \ 47 | samtools view -@ 1 -S -h -u - | \ 48 | samtools sort -@ 1 -T SRR6852086 - >$OUTDIR/SRR6852086.bam 49 | 50 | # index bam files 51 | samtools index $OUTDIR/SRR6852086.bam 52 | -------------------------------------------------------------------------------- /scripts/03_helixer/02_agat_stats.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=agat_stats_helixer 3 | #SBATCH -N 1 4 | #SBATCH -n 1 5 | #SBATCH -c 1 6 | #SBATCH --partition=general 7 | #SBATCH --qos=general 8 | #SBATCH --mail-type=ALL 9 | #SBATCH --mem=50G 10 | #SBATCH --mail-user=your.email@uconn.edu 11 | #SBATCH -o %x_%j.out 12 | #SBATCH -e %x_%j.err 13 | 14 | hostname 15 | date 16 | 17 | module load singularity 18 | 19 | OUTDIR="../../results/03_helixer/agat/statistics" 20 | mkdir -p ${OUTDIR} 21 | 22 | cd ${OUTDIR} 23 | 24 | singularity exec /isg/shared/databases/nfx_singularity_cache/depot.galaxyproject.org-singularity-agat-1.0.0--pl5321hdfd78af_0.img agat_sp_statistics.pl \ 25 | --gff ../../Arabidopsis_thaliana.gff3 \ 26 | -g ../../../../data/genome/GCF_000001735.4_TAIR10.1_genomic.fna \ 27 | -o stats.txt 28 | 29 | 30 | singularity exec /isg/shared/databases/nfx_singularity_cache/depot.galaxyproject.org-singularity-agat-1.0.0--pl5321hdfd78af_0.img agat_sq_stat_basic.pl \ 31 | --gff ../../Arabidopsis_thaliana.gff3 \ 32 | -g ../../../../data/genome/GCF_000001735.4_TAIR10.1_genomic.fna \ 33 | -o basic_stats.txt 34 | 35 | singularity exec /isg/shared/databases/nfx_singularity_cache/depot.galaxyproject.org-singularity-agat-1.0.0--pl5321hdfd78af_0.img agat_sp_functional_statistics.pl \ 36 | --gff ../../Arabidopsis_thaliana.gff3 \ 37 | -g ../../../../data/genome/GCF_000001735.4_TAIR10.1_genomic.fna \ 38 | -o functional_statistics 39 | 40 | 41 | -------------------------------------------------------------------------------- /scripts/04_braker_proteins/03_agat_stats.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=agat_stats_braker 3 | #SBATCH -N 1 4 | #SBATCH -n 1 5 | #SBATCH -c 1 6 | #SBATCH --partition=general 7 | #SBATCH --qos=general 8 | #SBATCH --mail-type=ALL 9 | #SBATCH --mem=50G 10 | #SBATCH --mail-user=your.email@uconn.edu 11 | #SBATCH -o %x_%j.out 12 | #SBATCH -e %x_%j.err 13 | 14 | hostname 15 | date 16 | 17 | module load singularity 18 | 19 | OUTDIR="../../results/04_braker/agat/statistics" 20 | mkdir -p ${OUTDIR} 21 | 22 | cd ${OUTDIR} 23 | 24 | singularity exec /isg/shared/databases/nfx_singularity_cache/depot.galaxyproject.org-singularity-agat-1.0.0--pl5321hdfd78af_0.img agat_sp_statistics.pl \ 25 | --gff ../../proteins/braker/augustus.hints.gtf \ 26 | -g ../../../../data/genome/GCF_000001735.4_TAIR10.1_genomic.fna \ 27 | -o stats.txt 28 | 29 | 30 | singularity exec /isg/shared/databases/nfx_singularity_cache/depot.galaxyproject.org-singularity-agat-1.0.0--pl5321hdfd78af_0.img agat_sq_stat_basic.pl \ 31 | --gff ../../proteins/braker/augustus.hints.gtf \ 32 | -g ../../../../data/genome/GCF_000001735.4_TAIR10.1_genomic.fna \ 33 | -o basic_stats.txt 34 | 35 | singularity exec /isg/shared/databases/nfx_singularity_cache/depot.galaxyproject.org-singularity-agat-1.0.0--pl5321hdfd78af_0.img agat_sp_functional_statistics.pl \ 36 | --gff ../../proteins/braker/augustus.hints.gtf \ 37 | -g ../../../../data/genome/GCF_000001735.4_TAIR10.1_genomic.fna \ 38 | -o functional_statistics 39 | 40 | 41 | -------------------------------------------------------------------------------- /scripts/01_raw_data/sra_download.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=sra_download 3 | #SBATCH -N 1 4 | #SBATCH -n 1 5 | #SBATCH -c 4 6 | #SBATCH --partition=general 7 | #SBATCH --qos=general 8 | #SBATCH --mail-type=END 9 | #SBATCH --mail-user=your.email@uconn.edu 10 | #SBATCH --mem=8G 11 | #SBATCH -o %x_%j.out 12 | #SBATCH -e %x_%j.err 13 | 14 | hostname 15 | date 16 | 17 | # run this script to download the data from the SRA 18 | # if you're on xanadu and want to save time, run the other 19 | # script in this directory to symlink the data instead 20 | 21 | # data 22 | # Illumina paired-end RNA-seq from Arabidopsis leaf tissue 23 | # bioproject: PRJNA438701 24 | # biosample: SAMN08724106 25 | # SRA runs: 26 | # SRR6852085 27 | # SRR6852086 28 | 29 | # Oxford nanopore long read cDNA 30 | # bioproject: PRJNA594286 31 | # biosamples: SAMN13510394,SAMN13510392,SAMN13510391 32 | # SRA runs: 33 | # SRR10611193 34 | # SRR10611194 35 | # SRR10611195 36 | 37 | # load software 38 | module load sratoolkit/2.11.3 39 | 40 | # output directory, create if it doesn't exist 41 | OUTDIR=../../data/rnaseq 42 | mkdir -p ${OUTDIR} 43 | 44 | cd ${OUTDIR} 45 | 46 | fasterq-dump SRR6852085 47 | fasterq-dump SRR6852086 48 | 49 | # nanopore outdir 50 | NANODIR=../nanopore 51 | mkdir -p ${NANODIR} 52 | 53 | cd ${NANODIR} 54 | 55 | fasterq-dump SRR10611193 56 | fasterq-dump SRR10611194 57 | fasterq-dump SRR10611195 58 | 59 | date 60 | -------------------------------------------------------------------------------- /scripts/alignment_exercise/04_braker_qualimap.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=braker_qualimap 3 | #SBATCH --mail-user= 4 | #SBATCH --mail-type=ALL 5 | #SBATCH -o %x_%j.out 6 | #SBATCH -e %x_%j.err 7 | #SBATCH --ntasks=1 8 | #SBATCH --cpus-per-task=1 9 | #SBATCH --mem=10G 10 | #SBATCH --qos=general 11 | #SBATCH --partition=general 12 | 13 | hostname 14 | date 15 | 16 | ################################## 17 | # calculate stats on alignments 18 | ################################## 19 | # this time we'll use qualimap 20 | 21 | # load software-------------------------------------------------------------------------- 22 | module load qualimap/2.2.1 23 | 24 | 25 | # input, output directories-------------------------------------------------------------- 26 | 27 | INDIR=../../results/alignment_exercise/alignments 28 | OUTDIR=../../results/alignment_exercise/qualimap_reports/braker 29 | mkdir -p $OUTDIR 30 | 31 | # gtf annotation is required here 32 | GFF=../../results/04_braker/proteins/braker/augustus.hints.gtf 33 | 34 | qualimap \ 35 | rnaseq \ 36 | -bam $INDIR/SRR6852085.bam \ 37 | -gtf $GFF \ 38 | -outdir $OUTDIR/SRR6852085 \ 39 | --java-mem-size=10G 40 | 41 | qualimap \ 42 | rnaseq \ 43 | -bam $INDIR/SRR6852086.bam \ 44 | -gtf $GFF \ 45 | -outdir $OUTDIR/SRR6852086 \ 46 | --java-mem-size=10G 47 | 48 | ##aggregate qualimap results 49 | module load MultiQC/1.9 50 | 51 | MultiQC_OUT=../../results/alignment_exercise/qualimap_reports/braker/multiqc 52 | multiqc -f -o $MultiQC_OUT $OUTDIR 53 | -------------------------------------------------------------------------------- /scripts/runall.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # get the data 4 | cd 01_raw_data/ 5 | jid1=$( sbatch --parsable get_genome.sh ) 6 | jid2=$( sbatch --parsable sra_download.sh ) 7 | 8 | # repeat modeling/masking start when genome is downloaded 9 | cd ../02_mask_repeats 10 | jid3=$( sbatch --parsable --dependency=afterok:$jid1 01_create_db.sh ) 11 | jid4=$( sbatch --parsable --dependency=afterok:$jid3 02_repeatmodeler.sh ) 12 | jid5=$( sbatch --parsable --dependency=afterok:$jid4 03_repeatmasker.sh ) 13 | jid6=$( sbatch --parsable --dependency=afterok:$jid5 04_busco.sh ) 14 | 15 | #helixer 16 | cd ../03_helixer 17 | jid7=$( sbatch --parsable --dependency=afterok:$jid6 01_helixer.sh ) 18 | jid8=$( sbatch --parsable --dependency=afterok:$jid7 02_agat_stats.sh ) 19 | jid9=$( sbatch --parsable --dependency=afterok:$jid8 03_agat_codingsequences.sh ) 20 | jid10=$( sbatch --parsable --dependency=afterok:$jid9 04_busco.sh ) 21 | jid11=$( sbatch --parsable --dependency=afterok:$jid10 05_EnTAP.sh ) 22 | 23 | # braker 24 | cd ../04_braker_proteins 25 | jid12=$( sbatch --parsable --dependency=afterok:$jid6 01_getproteins.sh ) 26 | jid13=$( sbatch --parsable --dependency=afterok:$jid12 02_braker_proteins.sh ) 27 | jid14=$( sbatch --parsable --dependency=afterok:$jid13 03_agat_stats.sh ) 28 | jid15=$( sbatch --parsable --dependency=afterok:$jid14 04_agat_codingsequences.sh ) 29 | jid16=$( sbatch --parsable --dependency=afterok:$jid15 05_busco.sh ) 30 | jid17=$( sbatch --parsable --dependency=afterok:$jid16 06_EnTAP.sh ) 31 | 32 | # EASEL 33 | cd ../05_EASEL 34 | jid18=$( sbatch --parsable --dependency=afterok:$jid1,$jid2 easel.sh ) 35 | 36 | # compare 37 | cd ../06_gffcompare 38 | jid19=$( sbatch --parsable --dependency=afterok:$jid7,$jid13,$jid18 01_gffcompare.sh ) 39 | -------------------------------------------------------------------------------- /scripts/01_raw_data/symlink_data.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=symlink_fastqs 3 | #SBATCH -N 1 4 | #SBATCH -n 1 5 | #SBATCH -c 4 6 | #SBATCH --partition=general 7 | #SBATCH --qos=general 8 | #SBATCH --mail-type=END 9 | #SBATCH --mail-user=your.email@uconn.edu 10 | #SBATCH --mem=8G 11 | #SBATCH -o %x_%j.out 12 | #SBATCH -e %x_%j.err 13 | 14 | hostname 15 | date 16 | 17 | # run this script to symlink the data instead of downloading from the SRA 18 | # if you want to download the data instead, run the other script 19 | 20 | # data 21 | # RNA-seq from Arabidopsis leaf tissue 22 | # bioproject: PRJNA438701 23 | # biosample: SAMN08724106 24 | # SRA runs: 25 | # SRR6852085 26 | # SRR6852086 27 | 28 | # output directory, create if it doesn't exist 29 | OUTDIR=../../data/rnaseq 30 | mkdir -p $OUTDIR 31 | 32 | cd ${OUTDIR} 33 | 34 | # symlink data from fpath 35 | fpath="/core/cbc/tutorials/rawdata/Structural_Annotation/v1/arabidopsis_leaf/" 36 | 37 | for f in ${fpath}*; do 38 | echo $f 39 | echo `basename ${f}` 40 | ln -s ${f} `basename ${f}` 41 | done 42 | 43 | 44 | 45 | # Oxford nanopore long read cDNA 46 | # bioproject: PRJNA594286 47 | # biosamples: SAMN13510394,SAMN13510392,SAMN13510391 48 | # SRA runs: 49 | # SRR10611193 50 | # SRR10611194 51 | # SRR10611195 52 | 53 | # output directory, create if it doesn't exist 54 | OUTDIR=../../data/nanopore 55 | mkdir -p $OUTDIR 56 | 57 | cd ${OUTDIR} 58 | 59 | # symlink data from fpath 60 | fpath="/core/cbc/tutorials/rawdata/Structural_Annotation/v1/nanopore/" 61 | 62 | for f in ${fpath}*; do 63 | echo $f 64 | echo `basename ${f}` 65 | ln -s ${f} `basename ${f}` 66 | done 67 | -------------------------------------------------------------------------------- /scripts/alignment_exercise/03_helixer_qualimap.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH --job-name=helixer_qualimap 3 | #SBATCH --mail-user= 4 | #SBATCH --mail-type=ALL 5 | #SBATCH -o %x_%j.out 6 | #SBATCH -e %x_%j.err 7 | #SBATCH --ntasks=1 8 | #SBATCH --cpus-per-task=1 9 | #SBATCH --mem=10G 10 | #SBATCH --qos=general 11 | #SBATCH --partition=general 12 | 13 | hostname 14 | date 15 | 16 | ################################## 17 | # calculate stats on alignments 18 | ################################## 19 | # this time we'll use qualimap 20 | 21 | 22 | 23 | # input, output directories-------------------------------------------------------------- 24 | 25 | INDIR=../../results/alignment_exercise/alignments 26 | OUTDIR=../../results/alignment_exercise/qualimap_reports/helixer 27 | mkdir -p $OUTDIR 28 | 29 | #get gff3 in gtf format 30 | #module load singularity 31 | #GFF=../../results/03_helixer/Arabidopsis_thaliana.gff3 32 | 33 | 34 | #singularity exec /isg/shared/databases/nfx_singularity_cache/depot.galaxyproject.org-singularity-agat-1.0.0--pl5321hdfd78af_0.img agat_convert_sp_gff2gtf.pl --gff $GFF -o ../../results/03_helixer/Arabidopsis_thaliana.gtf 35 | 36 | # gtf annotation is required here 37 | GTF=../../results/03_helixer/Arabidopsis_thaliana.gtf 38 | 39 | # load software-------------------------------------------------------------------------- 40 | module load qualimap/2.2.1 41 | 42 | qualimap \ 43 | rnaseq \ 44 | -bam $INDIR/SRR6852085.bam \ 45 | -gtf $GTF \ 46 | -outdir $OUTDIR/SRR6852085 \ 47 | --java-mem-size=10G 48 | 49 | qualimap \ 50 | rnaseq \ 51 | -bam $INDIR/SRR6852086.bam \ 52 | -gtf $GTF \ 53 | -outdir $OUTDIR/SRR6852086 \ 54 | --java-mem-size=10G 55 | 56 | ##aggregate qualimap results 57 | module load MultiQC/1.9 58 | 59 | MultiQC_OUT=../../results/alignment_exercise/qualimap_reports/helixer/multiqc 60 | multiqc -f -o $MultiQC_OUT $OUTDIR 61 | -------------------------------------------------------------------------------- /scripts/alignment_exercise/Arabidopsis_thaliana.agat.log: -------------------------------------------------------------------------------- 1 | 2 | ------------------------------------------------------------------------------ 3 | | Another GFF Analysis Toolkit (AGAT) - Version: v1.0.0 | 4 | | https://github.com/NBISweden/AGAT | 5 | | National Bioinformatics Infrastructure Sweden (NBIS) - www.nbis.se | 6 | ------------------------------------------------------------------------------ 7 | 06/09/2023 at 17h52m56s 8 | 9 | ******************************************************************************** 10 | * - Start parsing - * 11 | ******************************************************************************** 12 | -------------------------- parse options and metadata -------------------------- 13 | => Accessing the feature_levels YAML file 14 | Using standard /usr/local/lib/perl5/site_perl/auto/share/dist/AGAT/feature_levels.yaml file 15 | => Attribute used to group features when no Parent/ID relationship exists (i.e common tag): 16 | * locus_tag 17 | * gene_id 18 | => merge_loci option deactivated 19 | => Machine information: 20 | This script is being run by perl v5.32.1 21 | Bioperl location being used: /usr/local/lib/perl5/site_perl/Bio/ 22 | Operating system being used: linux 23 | => Accessing Ontology 24 | No ontology accessible from the gff file header! 25 | We use the SOFA ontology distributed with AGAT: 26 | /usr/local/lib/perl5/site_perl/auto/share/dist/AGAT/so.obo 27 | Read ontology /usr/local/lib/perl5/site_perl/auto/share/dist/AGAT/so.obo: 28 | 4 root terms, and 2596 total terms, and 1516 leaf terms 29 | Filtering ontology: 30 | The feature type (3rd column) is constrained to be either a term from the Sequence Ontology or an SO accession number. The latter alternative is distinguished using the syntax SO:000000. In either case, it must be sequence_feature (SO:0000110) or an is_a child of it. 31 | We filter the ontology to apply this rule. We found 1861 terms that are sequence_feature or is_a child of it. 32 | --------------------------------- parsing file --------------------------------- 33 | => Number of line in file: 408528 34 | => Number of comment lines: 0 35 | => Fasta included: No 36 | => Number of features lines: 408528 37 | => Number of feature type (3rd column): 6 38 | * Level1: 1 => gene 39 | * level2: 1 => mRNA 40 | * level3: 4 => CDS three_prime_UTR five_prime_UTR exon 41 | * unknown: 0 => 42 | => Version of the Bioperl GFF parser selected by AGAT: 3 43 | ******************************************************************************** 44 | * - End parsing - * 45 | * done in 35 seconds * 46 | ******************************************************************************** 47 | 48 | ******************************************************************************** 49 | * - Start checks - * 50 | ******************************************************************************** 51 | ---------------------------- Check1: feature types ----------------------------- 52 | ----------------------------------- ontology ----------------------------------- 53 | All feature types in agreement with the Ontology. 54 | ------------------------------------- agat ------------------------------------- 55 | AGAT can deal with all the encountered feature types (3rd column) 56 | ------------------------------ done in 0 seconds ------------------------------- 57 | 58 | ------------------------------ Check2: duplicates ------------------------------ 59 | None found 60 | ------------------------------ done in 0 seconds ------------------------------- 61 | 62 | -------------------------- Check3: sequential bucket --------------------------- 63 | Nothing to check as sequential bucket! 64 | ------------------------------ done in 0 seconds ------------------------------- 65 | 66 | --------------------------- Check4: l2 linked to l3 ---------------------------- 67 | No problem found 68 | ------------------------------ done in 0 seconds ------------------------------- 69 | 70 | --------------------------- Check5: l1 linked to l2 ---------------------------- 71 | No problem found 72 | ------------------------------ done in 0 seconds ------------------------------- 73 | 74 | --------------------------- Check6: remove orphan l1 --------------------------- 75 | We remove only those not supposed to be orphan 76 | None found 77 | ------------------------------ done in 0 seconds ------------------------------- 78 | 79 | ------------------------- Check7: all level3 locations ------------------------- 80 | ------------------------------ done in 4 seconds ------------------------------- 81 | 82 | ------------------------------ Check8: check cds ------------------------------- 83 | No problem found 84 | ------------------------------ done in 0 seconds ------------------------------- 85 | 86 | ----------------------------- Check9: check exons ------------------------------ 87 | No exons created 88 | No exons locations modified 89 | No supernumerary exons removed 90 | No level2 locations modified 91 | ------------------------------ done in 4 seconds ------------------------------- 92 | 93 | ----------------------------- Check10: check utrs ------------------------------ 94 | No UTRs created 95 | No UTRs locations modified 96 | No supernumerary UTRs removed 97 | ------------------------------ done in 2 seconds ------------------------------- 98 | 99 | ------------------------ Check11: all level2 locations ------------------------- 100 | No problem found 101 | ------------------------------ done in 5 seconds ------------------------------- 102 | 103 | ------------------------ Check12: all level1 locations ------------------------- 104 | No problem found 105 | ------------------------------ done in 0 seconds ------------------------------- 106 | 107 | ---------------------- Check13: remove identical isoforms ---------------------- 108 | None found 109 | ------------------------------ done in 0 seconds ------------------------------- 110 | ******************************************************************************** 111 | * - End checks - * 112 | * done in 15 seconds * 113 | ******************************************************************************** 114 | 115 | => OmniscientI total time: 50 seconds 116 | -------------------------------------------------------------------------------- /scripts/04_braker_proteins/busco_downloads/file_versions.tsv: -------------------------------------------------------------------------------- 1 | acidobacteria_odb10 2020-03-06 2d141e67a5fcd237c1db7cf2ac418d0f Prokaryota lineages 2 | aconoidasida_odb10 2020-08-05 adf61fbf84c7f222bb7ce6b1781f2926 Eukaryota lineages 3 | actinobacteria_class_odb10 2021-02-23 38032291926fdaf510c9c48036148644 Prokaryota lineages 4 | actinobacteria_phylum_odb10 2021-02-23 5f73378fb872cb94b698a6cd8c776dda Prokaryota lineages 5 | actinopterygii_odb10 2021-02-19 b3a29afc90e82dad596cb3b29102dffe Eukaryota lineages 6 | agaricales_odb10 2020-08-05 affd427a56e545b88bb7fff00bfdad3d Eukaryota lineages 7 | agaricomycetes_odb10 2020-08-05 c2880cb5ae1719890a7d493bc8d00254 Eukaryota lineages 8 | alphaproteobacteria_odb10 2021-02-23 c58d65ae5c7d518db5ef63c4c70800bc Prokaryota lineages 9 | alteromonadales_odb10 2021-02-23 285152f93caaade94f8fedf9ff41140c Prokaryota lineages 10 | alveolata_odb10 2020-09-10 b4295c597c6b79c20d31a5c792882b33 Eukaryota lineages 11 | apicomplexa_odb10 2020-09-10 d3d363c81a3143a3cd943e4859819981 Eukaryota lineages 12 | aquificae_odb10 2021-02-23 24d4d93ec4d4710d92463ce21b860d44 Prokaryota lineages 13 | arachnida_odb10 2020-08-05 bc082278b8c4bf154138029d06274818 Eukaryota lineages 14 | archaea_odb10 2021-02-23 e9d5d92c92ef6c7c5b2737ece2a18ce0 Prokaryota lineages 15 | arthropoda_odb10 2020-09-10 120880f396b589a52dd523fba555b29f Eukaryota lineages 16 | ascomycota_odb10 2020-09-10 fb9ff09632c714e87e9813f3e0f7a282 Eukaryota lineages 17 | aves_odb10 2021-02-19 31c62d84d069449d464c4710bc0f7aca Eukaryota lineages 18 | bacillales_odb10 2021-02-23 079e0586cd45d7bb3610ec3afa8c9c11 Prokaryota lineages 19 | bacilli_odb10 2021-02-23 bfe452ba1cb520a6a922e78d6845131c Prokaryota lineages 20 | bacteria_odb10 2020-03-06 938c9fa3b6326112334fec79c4a54b57 Prokaryota lineages 21 | bacteroidales_odb10 2021-02-23 bb5a179da3a4c337380487b826a10b83 Prokaryota lineages 22 | bacteroidetes-chlorobi_group_odb10 2021-02-23 eaed067069f2e6041a4b5228175b5753 Prokaryota lineages 23 | bacteroidetes_odb10 2021-02-23 226870db6fa453ec2a245a7644b2b047 Prokaryota lineages 24 | bacteroidia_odb10 2021-02-23 c8c3a1694b7e2a45dc4bb1806cb46976 Prokaryota lineages 25 | basidiomycota_odb10 2020-09-10 902b52e48eeeeafaa4d5bd8a9a58c3a8 Eukaryota lineages 26 | betaproteobacteria_odb10 2021-02-23 3bcfd10bb4b822b946c97b66b92cf277 Prokaryota lineages 27 | boletales_odb10 2020-08-05 949e92ee737f5192981856dddf1842b4 Eukaryota lineages 28 | brassicales_odb10 2020-08-05 cbbadbf123b9848edf4c5f832a624ef1 Eukaryota lineages 29 | burkholderiales_odb10 2021-02-23 f59e27ec2378e59abd122bfe82c1d105 Prokaryota lineages 30 | campylobacterales_odb10 2020-03-06 577592ec5383a570cd8be9beec560525 Prokaryota lineages 31 | capnodiales_odb10 2020-08-05 307cff52681697718e796ecbeef108b6 Eukaryota lineages 32 | carnivora_odb10 2021-02-19 b3f12f32f04545c13c72de45cbbb2c7f Eukaryota lineages 33 | cellvibrionales_odb10 2020-03-06 a26de678af35ed6d0f887fb2270d1729 Prokaryota lineages 34 | cetartiodactyla_odb10 2021-02-19 e96dfc6299c567768085ee9569b6ab15 Eukaryota lineages 35 | chaetothyriales_odb10 2020-08-05 1f5ae267023a7440f191a25955c71b3d Eukaryota lineages 36 | chlamydiae_odb10 2020-03-06 2173f9d1deb8536fd533c70219b674bd Prokaryota lineages 37 | chlorobi_odb10 2020-03-06 206c59743579333d82a63ac34eed7b91 Prokaryota lineages 38 | chloroflexi_odb10 2020-03-06 a50e7763a1d545724b4747043afaf1d2 Prokaryota lineages 39 | chlorophyta_odb10 2020-08-05 1e7eb8376e0d6b08451b8bdb33f58776 Eukaryota lineages 40 | chromatiales_odb10 2020-03-06 f6a8e3bf1b2bd52b4cad4827228c1831 Prokaryota lineages 41 | chroococcales_odb10 2020-03-06 c4e9cf5f1f7cf71071025f496f0c1348 Prokaryota lineages 42 | clostridiales_odb10 2020-03-06 b2be6299fc011ffd1e8516c1f06240eb Prokaryota lineages 43 | clostridia_odb10 2020-03-06 eaefff5206a835262cd8f51baa02fdf4 Prokaryota lineages 44 | coccidia_odb10 2020-08-05 c8ed309d74b8ae78fa2401458d1612b9 Eukaryota lineages 45 | coriobacteriales_odb10 2020-03-06 2b10fbf070ee0922617fcfe49aa80c29 Prokaryota lineages 46 | coriobacteriia_odb10 2020-03-06 94aea07bcda4b9f1d39ac2a65ed4e688 Prokaryota lineages 47 | corynebacteriales_odb10 2020-03-06 9a30bd54e30eaecf8637fd063916ed4f Prokaryota lineages 48 | cyanobacteria_odb10 2021-02-23 323d5fa15bc65cd0f16cccbcaa7a2f9f Prokaryota lineages 49 | cyprinodontiformes_odb10 2021-02-19 9a2490309802e45fe7476d4d9d46101f Eukaryota lineages 50 | cytophagales_odb10 2021-02-23 5e6376e82a5e19ca3016a341f43d049f Prokaryota lineages 51 | cytophagia_odb10 2021-02-23 2a7ad77bcd010f012c0ba74968253d20 Prokaryota lineages 52 | delta-epsilon-subdivisions_odb10 2021-02-23 1b1ea2795fe21f7987eaea59d81630ed Prokaryota lineages 53 | deltaproteobacteria_odb10 2021-02-23 fa17f4e1ba4b58846a3e0ec1cc31da2c Prokaryota lineages 54 | desulfobacterales_odb10 2020-03-06 9ab123e5a7c62f7afe5801838fb7c86c Prokaryota lineages 55 | desulfovibrionales_odb10 2021-02-23 75744e405f350155945a5f54c1ddacf2 Prokaryota lineages 56 | desulfurococcales_odb10 2021-02-23 1104b2ddf4175dce4209900e10557b24 Prokaryota lineages 57 | desulfuromonadales_odb10 2020-03-06 e17404d24b53f441d17f2de26a167be3 Prokaryota lineages 58 | diptera_odb10 2020-08-05 dbcbc5c66fd3ccb74dac42aba2ef9f54 Eukaryota lineages 59 | dothideomycetes_odb10 2020-08-05 8dd584d1a04eb730a9a36f6ded62f316 Eukaryota lineages 60 | embryophyta_odb10 2020-09-10 60ab0ab9ae5983dad157628cdb66aea4 Eukaryota lineages 61 | endopterygota_odb10 2020-09-10 4edcf89c3d56d11d3217ac78a55a9c8a Eukaryota lineages 62 | enterobacterales_odb10 2021-02-23 e3e79a8343682813068add13b9e42001 Prokaryota lineages 63 | entomoplasmatales_odb10 2020-03-06 275a32b658851b2fcb8ec0aaaf3dd456 Prokaryota lineages 64 | epsilonproteobacteria_odb10 2020-03-06 27dfe74e001490f54872182d2d3e0fc9 Prokaryota lineages 65 | euarchontoglires_odb10 2021-02-19 53ae07cb11ad8fbb7b6adcb60a21eb87 Eukaryota lineages 66 | eudicots_odb10 2020-09-10 08752dd1cbed6ae1dbcde78b49c8ea2f Eukaryota lineages 67 | euglenozoa_odb10 2020-08-05 e2bdaeda273ff3cf2befad32fd964834 Eukaryota lineages 68 | eukaryota_odb10 2020-09-10 01c475de80f458fc662595f7c52f2c7c Eukaryota lineages 69 | eurotiales_odb10 2020-08-05 1b8638d3f11083a05c13ac85d8d1c6cc Eukaryota lineages 70 | eurotiomycetes_odb10 2020-08-05 c8f5f61ec75edaa4f90cbc806b0a62fb Eukaryota lineages 71 | euryarchaeota_odb10 2021-02-23 dd5618970967837356ec01a26e345698 Prokaryota lineages 72 | eutheria_odb10 2021-02-19 1e06f18bb86ee888de68edc5b2488566 Eukaryota lineages 73 | fabales_odb10 2020-08-05 a6b23c521fa40bff31e01b92848414f9 Eukaryota lineages 74 | firmicutes_odb10 2021-02-23 95f62a68964c2ff85a3af0a8b8618b20 Prokaryota lineages 75 | flavobacteriales_odb10 2021-02-23 1b9e8a447d7d4ea997423c7e7f1d7907 Prokaryota lineages 76 | flavobacteriia_odb10 2021-02-23 134a9d7ba4a4b750785665ba6ba818ca Prokaryota lineages 77 | fungi_odb10 2021-06-28 343cfbc68df867970c1e3251b8c3886c Eukaryota lineages 78 | fusobacteriales_odb10 2020-03-06 7ef0baae4b567ffb9f19c5b1a42f8169 Prokaryota lineages 79 | fusobacteria_odb10 2020-03-06 2c4a243016fcf267f9e4fd7e2bc71500 Prokaryota lineages 80 | gammaproteobacteria_odb10 2021-02-23 5bb66bbbcc05168aaf28a2e02f4582d2 Prokaryota lineages 81 | glires_odb10 2021-02-19 39633c8425b2badc140da2504fc87cc6 Eukaryota lineages 82 | glomerellales_odb10 2020-08-05 4b30a551d675fcda7387c11578da7505 Eukaryota lineages 83 | halobacteriales_odb10 2021-02-23 7d595108e1e3e8a5a4dc3c71a853a933 Prokaryota lineages 84 | halobacteria_odb10 2021-02-23 67b9d5b545b50dd3e10ed41e2fbaf8e1 Prokaryota lineages 85 | haloferacales_odb10 2021-02-23 94480d89ec94075514bccd0805f71be4 Prokaryota lineages 86 | helotiales_odb10 2020-08-05 7b37f15f53446fb27799ca7bf57a255a Eukaryota lineages 87 | hemiptera_odb10 2020-08-05 5605964c582ff35a465bf2629f7f5bcb Eukaryota lineages 88 | hymenoptera_odb10 2020-08-05 d1fb8b5eab6222908188702c791f7e30 Eukaryota lineages 89 | hypocreales_odb10 2020-08-05 b3f1e90d1994e95930038951e8614a4e Eukaryota lineages 90 | insecta_odb10 2020-09-10 37811ebbdac556a9afa5b7b73ba80568 Eukaryota lineages 91 | lactobacillales_odb10 2020-03-06 750d7268082765b50f07ef3d14d3bff8 Prokaryota lineages 92 | laurasiatheria_odb10 2021-02-19 1af2473b8484afef051c6bd22599f6f4 Eukaryota lineages 93 | legionellales_odb10 2020-03-06 d4cf996e02d3386e752c2b5ab78990bc Prokaryota lineages 94 | leotiomycetes_odb10 2020-08-05 6c24724da1caf5200ab6fb09a1fa899c Eukaryota lineages 95 | lepidoptera_odb10 2020-08-05 50cd95f78717f7c9cb628b4c2cfdcf8c Eukaryota lineages 96 | liliopsida_odb10 2020-09-10 04fe88de47726ab0304a0d5d39b3a7c0 Eukaryota lineages 97 | lineages_list.txt 2021-12-14 ebe3d982a830281588e287cf72f3b5e1 NaN information 98 | list_of_reference_markers.archaea_odb10.txt 2019-12-16 0143477e3666a5bcdec26a826eb01ab4 Prokaryota placement_files 99 | list_of_reference_markers.bacteria_odb10.txt 2019-12-16 e82a39f3060123f4322057c632d7691c Prokaryota placement_files 100 | list_of_reference_markers.eukaryota_odb10.txt 2019-12-16 95a02d2bbe1659778bda594d59c90ee1 Eukaryota placement_files 101 | mammalia_odb10 2021-02-19 2ecac1cc6306e23aedc5554d21aa8cb9 Eukaryota lineages 102 | mapping_taxid-lineage.archaea_odb10.txt 2019-12-16 5a05ab8eb491f6d4ae9f7774fd778f7e Prokaryota placement_files 103 | mapping_taxid-lineage.bacteria_odb10.txt 2019-12-16 b25a5a807f4ff70f4343ef7500d26975 Prokaryota placement_files 104 | mapping_taxid-lineage.eukaryota_odb10.txt 2019-12-16 e5b047a09cee36f1116f7de1453276b4 Eukaryota placement_files 105 | mapping_taxids-busco_dataset_name.archaea_odb10.txt 2019-12-16 d8cc75b75e052e475e07d84cb4d8cd06 Prokaryota placement_files 106 | mapping_taxids-busco_dataset_name.bacteria_odb10.txt 2019-12-16 79514724345b6cae32bb05ecacc11386 Prokaryota placement_files 107 | mapping_taxids-busco_dataset_name.eukaryota_odb10.txt 2019-12-16 435492f974732bb2d562d5abe54bd494 Eukaryota placement_files 108 | metazoa_odb10 2021-02-24 908ac6e4843180b0da78008d8d13ad64 Eukaryota lineages 109 | methanobacteria_odb10 2021-02-23 ae5f8e89824ac8ce4cdce39def33075c Prokaryota lineages 110 | methanococcales_odb10 2021-02-23 63b2307d5bf99281d54a911ddae98c06 Prokaryota lineages 111 | methanomicrobiales_odb10 2021-02-23 e672048d3ce1482a2d5c9fb021939f16 Prokaryota lineages 112 | methanomicrobia_odb10 2021-02-23 342fb683a40299e1cf99c2e2a0d824e7 Prokaryota lineages 113 | micrococcales_odb10 2021-02-23 cc25897fe0037744d5c08611caf67f58 Prokaryota lineages 114 | microsporidia_odb10 2020-08-05 d4494748201b68681be04251797e2a29 Eukaryota lineages 115 | mollicutes_odb10 2020-03-06 da5b755e2e78520935d9497170dc722f Prokaryota lineages 116 | mollusca_odb10 2020-08-05 870b7d6ebc045f17b7701edd6c700283 Eukaryota lineages 117 | mucorales_odb10 2020-08-05 b5d8ca40517c336e97bd203ceb0285be Eukaryota lineages 118 | mucoromycota_odb10 2020-08-05 c8daca800b3c3beaa90984d043123bba Eukaryota lineages 119 | mycoplasmatales_odb10 2020-03-06 efb525bcfca4cd466a54d23c313e8cb5 Prokaryota lineages 120 | natrialbales_odb10 2021-02-23 b2e28169386b3867f8925eab47819901 Prokaryota lineages 121 | neisseriales_odb10 2021-02-23 6c6471483c294289c18e315f5c498a79 Prokaryota lineages 122 | nematoda_odb10 2020-08-05 837c874e300dacdaac1ea1c568f0a500 Eukaryota lineages 123 | nitrosomonadales_odb10 2020-03-06 b3faf24a30603b005d9fdd1b38b7a1ed Prokaryota lineages 124 | nostocales_odb10 2020-03-06 9169e406cbaf8e1979fdaaf82d9376c8 Prokaryota lineages 125 | oceanospirillales_odb10 2020-03-06 b86602828ec38ac083f6f021b3e2025f Prokaryota lineages 126 | onygenales_odb10 2020-08-05 5b737b0f6e4fdfd2c2bb8eef16312395 Eukaryota lineages 127 | oscillatoriales_odb10 2021-02-23 04ff36824a0333ecff06c19d3f696a78 Prokaryota lineages 128 | passeriformes_odb10 2021-02-19 0824ddc607ac23b81aba3b130c0ce3df Eukaryota lineages 129 | pasteurellales_odb10 2021-02-23 a58779d0fe992329b9c0dc1c1458f888 Prokaryota lineages 130 | planctomycetes_odb10 2020-03-06 0118b81b7df79cb1403b67f8183df555 Prokaryota lineages 131 | plasmodium_odb10 2020-08-05 c1b9af272f9d70cd87f2495ca02685f3 Eukaryota lineages 132 | pleosporales_odb10 2020-08-05 24b74bee7aba5bb7162e14cba16b4ae8 Eukaryota lineages 133 | poales_odb10 2020-08-05 f1370a401e061fbc481efc8d56a61299 Eukaryota lineages 134 | polyporales_odb10 2020-08-05 a8d1e8649af6305a646fa9560b8303a7 Eukaryota lineages 135 | primates_odb10 2021-02-19 70d4c8b9e252240c3361a8e9a36d7a63 Eukaryota lineages 136 | propionibacteriales_odb10 2020-03-06 abeabfab016e6dd8e174b8ff1f8cc3a6 Prokaryota lineages 137 | proteobacteria_odb10 2021-02-23 dfa99ad6fc3370619e36e0943f87e0ed Prokaryota lineages 138 | pseudomonadales_odb10 2020-03-06 2e75cacc10577e9ada225656b1d69698 Prokaryota lineages 139 | rhizobiales_odb10 2020-03-06 9d26dd132ea103cf12858572c1ccc30e Prokaryota lineages 140 | rhizobium-agrobacterium_group_odb10 2020-03-06 079b0f90df6ebcd4af5974e0f343751a Prokaryota lineages 141 | rhodobacterales_odb10 2021-02-23 1b5a9124171c07833f0e183112d0b797 Prokaryota lineages 142 | rhodospirillales_odb10 2020-03-06 15c0f83895891445119139d50e78a886 Prokaryota lineages 143 | rickettsiales_odb10 2020-03-06 786ed0abd6b46310e3801b23d5cb9ba5 Prokaryota lineages 144 | saccharomycetes_odb10 2020-08-05 9a6f694b70cb4371dafa07cbfefca21c Eukaryota lineages 145 | sauropsida_odb10 2021-02-19 0e96bcd14a25cd9f5a777dca8c486247 Eukaryota lineages 146 | selenomonadales_odb10 2020-03-06 182874011e09a85e223afcd10042dbfa Prokaryota lineages 147 | solanales_odb10 2020-08-05 372f8c2be80b3caeaedb53269d828452 Eukaryota lineages 148 | sordariomycetes_odb10 2020-08-05 7bb1a10b6e41280fa64c6662a63cee5e Eukaryota lineages 149 | sphingobacteriia_odb10 2020-03-06 d01c990601be499724e545e0154fa005 Prokaryota lineages 150 | sphingomonadales_odb10 2021-02-23 40f4f725e3348cab730a03b2fc02a3ba Prokaryota lineages 151 | spirochaetales_odb10 2020-03-06 42e9a4a32a4e9c61df42e8164c8a5a5a Prokaryota lineages 152 | spirochaetes_odb10 2021-02-23 967d4681c40a92556466a3d7e7ff8f4a Prokaryota lineages 153 | spirochaetia_odb10 2021-02-23 3bdc4751f645f9d86cd4adcec5c8a760 Prokaryota lineages 154 | stramenopiles_odb10 2020-08-05 dc910d58211329a234212d2c66789a79 Eukaryota lineages 155 | streptomycetales_odb10 2020-03-06 6f8019faf605c81e4d395c22cca0f20b Prokaryota lineages 156 | streptosporangiales_odb10 2020-03-06 1c91c03f877d928d14ff91efd683d5b4 Prokaryota lineages 157 | sulfolobales_odb10 2021-02-23 4007f40f3d0ed35185696d46697c8063 Prokaryota lineages 158 | supermatrix.aln.archaea_odb10.faa 2019-12-16 e9d2c8065a7779f9447b154b9030bcf8 Prokaryota placement_files 159 | supermatrix.aln.bacteria_odb10.faa 2019-12-16 eba6a090cbe21abe1db69d841e8231bc Prokaryota placement_files 160 | supermatrix.aln.eukaryota_odb10.faa 2019-12-16 3584e9fb12211b8c8c3ad63666444be1 Eukaryota placement_files 161 | synechococcales_odb10 2020-03-06 5ecdb97dc990e41102cab2684e69bb29 Prokaryota lineages 162 | synergistetes_odb10 2020-03-06 8f7dfc139094256c3db8072889a7f1d3 Prokaryota lineages 163 | tenericutes_odb10 2020-03-06 89c06a7b611cc7f62d8f19b5f3bc0dcd Prokaryota lineages 164 | tetrapoda_odb10 2021-02-19 694b8126182196ee9ae818aa481b02fa Eukaryota lineages 165 | thaumarchaeota_odb10 2021-02-23 4ab99951258ded64872c0190b71233de Prokaryota lineages 166 | thermoanaerobacterales_odb10 2020-03-06 0dc67d585bb9fcce1c02709336a04a76 Prokaryota lineages 167 | thermoplasmata_odb10 2021-02-23 a9296e8c5eb7a93593bdcb22e7b55744 Prokaryota lineages 168 | thermoproteales_odb10 2021-02-23 3ef9fe8a82657c27e72bb497dd1d8368 Prokaryota lineages 169 | thermoprotei_odb10 2021-02-23 77ec52b85096249d1c95c396a86e4ee9 Prokaryota lineages 170 | thermotogae_odb10 2020-03-06 fcf3316fda42cc5838095c3ef499f609 Prokaryota lineages 171 | thiotrichales_odb10 2020-03-06 d263640af89b51fa9dceac4cb37a2fdf Prokaryota lineages 172 | tissierellales_odb10 2020-03-06 0141ae72086d9f6e852a8e631bec2aea Prokaryota lineages 173 | tissierellia_odb10 2020-03-06 31903c27aaa16fc5c874985d572827d0 Prokaryota lineages 174 | tree.archaea_odb10.nwk 2019-12-16 23a9602460ca3ba12a4382964c40add4 Prokaryota placement_files 175 | tree.bacteria_odb10.nwk 2019-12-16 3cbe2d6292e02ee78b92b42ec7c13bf5 Prokaryota placement_files 176 | tree.eukaryota_odb10.nwk 2019-12-16 cb0d434ee9d87f5d6babd1832a3fa419 Eukaryota placement_files 177 | tree_metadata.archaea_odb10.txt 2019-12-16 1cc8a1284c883d9a5a8e33e70a81a743 Prokaryota placement_files 178 | tree_metadata.bacteria_odb10.txt 2019-12-16 04a34d1dbcc0814bd0f5acee1d2a9c3f Prokaryota placement_files 179 | tree_metadata.eukaryota_odb10.txt 2019-12-16 589ac2a41cb79863d4b0b061e472afea Eukaryota placement_files 180 | tremellomycetes_odb10 2020-08-05 349aef56d9ce5dc706e3be6d5d8df64c Eukaryota lineages 181 | verrucomicrobia_odb10 2020-03-06 d4fa7ba13a11fc30615d2c4b0e2c3c8a Prokaryota lineages 182 | vertebrata_odb10 2021-02-19 39319c8dbd4dde04dfdf3df8fde4e929 Eukaryota lineages 183 | vibrionales_odb10 2020-03-06 283699e0f1eb883e1eb98192327c6224 Prokaryota lineages 184 | viridiplantae_odb10 2020-09-10 6248976a39dc28727fcf7e91d4d7dca5 Eukaryota lineages 185 | xanthomonadales_odb10 2020-03-06 011a3a83c95bb2e065b7f9ba6d2174ef Prokaryota lineages 186 | alphaherpesvirinae_odb10 2020-11-26 30e2421312c2b2cad546e2c6bf06163c Virus lineages 187 | baculoviridae_odb10 2020-11-26 6db6c2f1f94bb760d216c1a837302323 Virus lineages 188 | rudiviridae_odb10 2020-11-26 3c6ac24c6cf4ea6a0b2a56e3cc7cb754 Virus lineages 189 | betaherpesvirinae_odb10 2020-11-26 b1ae785f353b911a6f6278ac159b4a4d Virus lineages 190 | herpesviridae_odb10 2020-11-26 9c2dd3ce19e1b0f90b21332fa7e7709b Virus lineages 191 | poxviridae_odb10 2020-11-26 4f79e3bb1c27bffb540bc9b5d8acbbcb Virus lineages 192 | tevenvirinae_odb10 2021-02-23 e839d5423faffc89f77612f3ebe54b7b Virus lineages 193 | aviadenovirus_odb10 2020-11-26 d24de19699c4030017035632b8ba0b01 Virus lineages 194 | enquatrovirus_odb10 2021-05-05 5e2425ba29d3bf1a0ed9db7aee4d2421 Virus lineages 195 | teseptimavirus_odb10 2020-11-26 7bb7dc9886e11502dde6fcb01a5376fa Virus lineages 196 | bclasvirinae_odb10 2020-11-26 01a6b43c49c9ca6fad86dc0405d7abc0 Virus lineages 197 | fromanvirus_odb10 2020-11-26 ea48d816aceabaa50b17e82aab9e19f9 Virus lineages 198 | skunavirus_odb10 2020-11-26 c977dd93312f7e31aaf13ba13597b8ad Virus lineages 199 | betabaculovirus_odb10 2020-11-26 ffb9d466040e6ed54f105d8a8a3193dd Virus lineages 200 | pahexavirus_odb10 2020-11-26 abd6e473974b5f74c2ee553eeeb81f6e Virus lineages 201 | alphabaculovirus_odb10 2020-11-26 e180f2b7ff5aebeac9f94dac74513bd0 Virus lineages 202 | tunavirinae_odb10 2020-11-26 08dc4693553f06940e7d4f6d4f73ba0d Virus lineages 203 | simplexvirus_odb10 2020-11-26 1f380eabeec787edc1617d5913b42d61 Virus lineages 204 | gammaherpesvirinae_odb10 2020-11-26 8abf118f3ba5554f621af737f9e4e053 Virus lineages 205 | varicellovirus_odb10 2020-11-26 0f8277c95dcd1cb472d632c4310fe34e Virus lineages 206 | cheoctovirus_odb10 2020-11-26 ff2403efea77d38716bca26d5591270d Virus lineages 207 | guernseyvirinae_odb10 2020-11-26 48d58eb0e99aea04a5506d5357c1bfbf Virus lineages 208 | tequatrovirus_odb10 2020-11-26 9f4c8e14aab52a505109bc460d4c1289 Virus lineages 209 | chordopoxvirinae_odb10 2020-11-26 e84a912c4fed4bdddacb12f36e1b3575 Virus lineages 210 | peduovirus_odb10 2021-02-23 372b89a33e2db3e090baacee2b14c6e5 Virus lineages 211 | iridoviridae_odb10 2020-11-26 2a1803e6b999005a2063f98b2ae8bc94 Virus lineages 212 | spounavirinae_odb10 2020-11-26 d549991c93c2271fbd5dcb734d5f5591 Virus lineages 213 | virus_datasets.txt 2020-11-26 a7d1e4d4bb6327d01efde3bcc8658cb3 NaN information 214 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | This repository is a tutorial for genome annotation. The scripts are set up to run on UConn's Xanadu cluster, including Xanadu-specific SLURM headers and software modules. To run it on Xanadu, simply clone this repository and start submitting the scripts by following along with this readme. If you are interested in running it elsewhere, you'll need to install the relevant software and alter or remove the SLURM headers, but otherwise, the tutorial is self-contained and pulls the necessary data from public databases. 2 | 3 | Commands should never be executed on the submit nodes of any HPC machine. If working on the Xanadu cluster, you should use `sbatch scriptname` after modifying the script for each stage. Basic editing of all scripts can be performed on the server with tools such as `nano`, `vim`, or `emacs`. If you are new to Linux, please use [this](https://bioinformatics.uconn.edu/unix-basics) handy guide for the operating system commands. The tutorial assumes basic familiarity with Linux. In this guide, you will be working with common bioinformatic file formats, such as [FASTA](https://en.wikipedia.org/wiki/FASTA_format), [FASTQ](https://en.wikipedia.org/wiki/FASTQ_format), [SAM/BAM](https://en.wikipedia.org/wiki/SAM_(file_format)), and [GFF3/GTF](https://en.wikipedia.org/wiki/General_feature_format). You can learn more about each file format [here](https://bioinformatics.uconn.edu/resources-and-events/tutorials/file-formats-tutorial/). If you do not have a Xanadu account and are an affiliate of UConn/UCHC, please apply for one **[here](https://bioinformatics.uconn.edu/contact-us/)**. 4 | 5 | Contents 6 | 1. [Overview](#1-overview) 7 | 2. [Downloading Data](#2-downloading-the-data) 8 | 3. [Identifying and Masking Repetitive Elements](#3-identifying-and-masking-repetitive-elements) 9 | 4. [Helixer](#4-helixer-annotation) 10 | 5. [Helixer Evaluation](#5-helixer-evaluation) 11 | 6. [Braker Annotation with Protein Evidence](#6-braker-annotation-with-protein-evidence) 12 | 7. [Braker Evaluation](#7-braker-evaluation) 13 | 8. [EASEL Pipeline](#8-easel-pipeline) 14 | 9. [Compare Evaluations with gffcompare](#9-compare-evaluations) 15 | 16 | ## 1. Overview 17 | In this tutorial, we'll annotate an *Arabidopsis thaliana* genome using a variety of methods. We'll use both RNA-seq data from leaf tissue and a database of green plant proteins as evidence in the annotation process. To get started, you can clone the tutorial repository to obtain all the scripts we're going to run. If you're working on UConn's Xanadu cluster, you can submit each script without modification. If you're working elsewhere, you will have to make sure the software is installed and modify or remove the SLURM headers, as appropriate to your system. To clone the repository: 18 | 19 | ``` 20 | git clone git@github.com:CBC-UCONN/Structural-Annotation.git 21 | ``` 22 | 23 | You'll notice the structure of the directory is initially pretty spare. Each script will create, as necessary, directories to hold data and results as you move along. 24 | 25 | ### `SLURM` 26 | UConn's Xanadu cluster uses `SLURM` to manage resources. Scripts to accomplish some task or requests for resources for interactive analysis, are routed through it. If you're working through the tutorial on Xanadu, you can submit each script using the command `sbatch` (e.g. `sbatch get_genome.sh`). `SLURM` will then queue your job, and when resources become available, run your code. Any output from your code that is written to the *standard output* or *standard error* channels is captured for later review. When submitting scripts via `sbatch`, we usually specify the resource request in a *header*. We'll review an example header here so we don't have to for every script going forward. 27 | 28 | ```bash 29 | #!/bin/bash 30 | #SBATCH --job-name=getgenome 31 | #SBATCH -o %x_%j.out 32 | #SBATCH -e %x_%j.err 33 | #SBATCH -N 1 34 | #SBATCH -n 1 35 | #SBATCH -c 1 36 | #SBATCH --mem=10G 37 | #SBATCH --partition=general 38 | #SBATCH --qos=general 39 | #SBATCH --mail-type=END 40 | #SBATCH --mail-user=your.email@uconn.edu 41 | ``` 42 | 43 | The first line is the *shebang*, indicating the script is `bash` code. The following lines, beginning with `#SBATCH`, pertain to `SLURM`. We specify a job name with `--job-name`, and the format of the names of the files that capture the standard output (`-o`) and the standard error (`-e`). In this case for `-o`, the format will be `JOBNAME_JOBID.out`. You should always check the `.out` and `.err` files after a job completes. These will usually contain any error messages produced by your code. 44 | 45 | The next six lines specify the resources requested. `-N 1` asks that the job be run on 1 node. `-n 1` tells SLURM we're only going to launch 1 task. `-c 1` requests 1 CPU for this task. `--mem=10G` requests 10 gigabytes of memory for this task. `--partition` asks that this job be run on the general partition (we also have high memory and GPU partitions) and `--qos=general` specifies the "general" quality of service. For most applications, we only vary `-c`, `--mem`, `--partition`, and `--qos=general`. 46 | 47 | It's important that you request adequate, but not excessive resources for each task. Also, generally you must tell the software you are running how many CPUs it can use (and sometimes how much memory) or it may not use all that you have requested. You can read more about resource allocations on Xanadu [here](https://github.com/CBC-UCONN/CBC_Docs/wiki/Requesting-resource-allocations-in-SLURM) and at the link above. 48 | 49 | After the SLURM header, we have the code to be run, as you will see in the sections below. 50 | 51 | ### Software modules 52 | 53 | You will note that in most of these scripts right after the SLURM header there is a `module load` command (e.g. `module load sratoolkit/2.11.3`). On Xanadu, we use a module system that allows us to have lots of software installed, often with conflicting dependencies, and to maintain functional older versions of software. In order to use one of these pieces of software, however, it must first be loaded. If you are working through this code on another system, you may need to install the software yourself, or modify the way it is made available (e.g. the version numbers). 54 | 55 | ##2 Downloading the Data 56 | 57 | The first step in the tutorial is to obtain the data we need. There are three main pieces of data: a reference genome, RNA-seq data, and protein data from green plants. We'll get the genome and RNA-seq data now, and download the protein data when we use it later. 58 | 59 | Scripts to obtain the data can be found in the directory [`scripts/01_raw_data`](). There are three. One downloads our reference genome, the other two are approaches for getting the RNA-seq data. 60 | 61 | ### Downloading the genome 62 | 63 | To get the genome, enter that directory and run the script `get_genome.sh`. By typing `sbatch get_genome.sh` on the command line. The body of the script (after the SLURM header) looks like this: 64 | 65 | ```bash 66 | OUTDIR=../../data/genome 67 | mkdir -p ${OUTDIR} 68 | 69 | cd ${OUTDIR} 70 | 71 | # Data: Arabidopsis thaliana TAIR10.1 assembly. 72 | # NCBI Accession: GCF_000001735.4 73 | 74 | # download genome 75 | wget https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/001/735/GCF_000001735.4_TAIR10.1/GCF_000001735.4_TAIR10.1_genomic.fna.gz 76 | 77 | # decompress 78 | gunzip GCF_000001735.4_TAIR10.1_genomic.fna.gz 79 | 80 | # strip off extra sequence name info after the space, e.g. 81 | # this: >NC_003070.9 Arabidopsis thaliana chromosome 1 sequence 82 | # becomes this: >NC_003070.9 83 | sed 's/ .*//' GCF_000001735.4_TAIR10.1_genomic.fna >tmp.fna 84 | mv tmp.fna GCF_000001735.4_TAIR10.1_genomic.fna 85 | ``` 86 | 87 | We first create a variable and assign it the name of our desired output directory. We then create that directory and `cd` into it. Most scripts in this tutorial will begin by specifying variables for input and output directories (and sometimes other relevant files and directories). Not all will move into the output directory for the analysis, but direct output there instead. 88 | 89 | The next steps are to download the genome from NCBI, decompress it and edit the sequence names, which have extraneous information (everything after the first space) that can trip up some software. You will note after this script completes, that there is now a new directory `data/genome` in the root of the tutorial repository containing our genome file (in this case with the suffix `.fna`). 90 | 91 | The reference genome is in *FASTA* format. This format is plain text. Each sequence in the file begins with a header line, followed by the sequence: 92 | 93 | ``` 94 | >NC_003070.9 95 | ccctaaaccctaaaccctaaaccctaaacctctGAATCCTTAATCCCTAAATCCCTAAATCTTTAAATCCTACATCCATG 96 | AATCCCTAAATACCTAAttccctaaacccgaaaccggTTTCTCTGGTTGAAAATCATTGTGtatataatgataattttat 97 | CGTTTTTATGTAATTGCTTATTGTTGTGtgtagattttttaaaaatatcatttgagGTCAATACAAATCCTATTTCTTGT 98 | GGTTTTCTTTCCTTCACTTAGCTATGGATGGTTTATCTTCATTTGTTATATTGGATACAAGCTTTGCTACGATCTACATT 99 | ... 100 | ``` 101 | 102 | ### The RNA-seq data 103 | 104 | There are two scripts that can be used to obtain the RNA-seq data. If you are working on Xanadu, you can use `symlink_data.sh`. That script will create a new directory, `data/rnaseq` and create *symlinks*, or pointers that point to copies of the data already residing on the cluster. If you would rather download the data from NCBI, you can run `sra_download.sh`. The body of the script looks like this: 105 | 106 | ```bash 107 | # RNA-seq from Arabidopsis leaf tissue 108 | # bioproject: PRJNA438701 109 | # biosample: SAMN08724106 110 | # SRA runs: 111 | # SRR6852085 112 | # SRR6852086 113 | 114 | # load software 115 | module load sratoolkit/2.11.3 116 | 117 | # output directory, create if it doesn't exist 118 | OUTDIR=../../data/rnaseq 119 | mkdir -p $OUTDIR 120 | 121 | cd ${OUTDIR} 122 | 123 | fasterq-dump SRR6852085 124 | fasterq-dump SRR6852086 125 | ``` 126 | 127 | Here we are using `sratoolkit` and the command `fasterq-dump` to download two different sets of RNA-seq data derived from Arabidopsis leaf tissue. 128 | 129 | The resulting data will be found in the newly created `data/rnaseq` directory. There will be four files of sequence data, each in *fastq* format: 130 | 131 | ``` 132 | SRR6852085_1.fastq 133 | SRR6852085_2.fastq 134 | SRR6852086_1.fastq 135 | SRR6852086_2.fastq 136 | ``` 137 | 138 | Our data are *paired-end*, which means that each molecule of cDNA in the sequence library is sequenced twice, once from each end. So our sequence data comes in pairs of files (`_1.fastq` and `_2.fastq`). The mate-pairs are kept in order, so if you do anything to disrupt the sequence ordering of these files that information will be scrambled. 139 | 140 | *fastq* format is a bit like *fasta*, except that it contains more information. Each sequence record has four lines: a header line giving the sequence name (beginning with `@`), a nucleotide sequence, a comment line beginning with `+`, and a line containing ASCII encoded, phred-scaled base qualities. For more on base qualities see [here](https://www.drive5.com/usearch/manual/quality_score.html). 141 | 142 | ``` 143 | @SRR6852085.1 1 length=150 144 | NGGCATGCAGACTTGTGAGGGGACGGAGACAATTCCGCTCANNGCNAGGTCACACACGTGTCTATTGTNAGGTGTGTACATNGGCAACGTGAAAGCGATAGTGAGGGNACAGTTTGGAATGTACAGCTGAACGGACATCACACGAAACCT 145 | +SRR6852085.1 1 length=150 146 | # CTGNNNNNNNNNNNGTTA ` 184 | 185 | (2) **Soft-masking** , this involves converting the sequence from Uppercase to lowercase as an examle the same repeat sequence is soft masked below 186 | 187 | `CTGTGCAAATCGCAGTTA -> CTGtgcaaatcgcagTTA ` 188 | 189 | For our downstream software, softmasking is preferable. It will let annotators know that a given sequence is repetitive, so that it will be ignored in initial gene-finding, but retaining the sequence allows nearby genic sequence to be extended into it if necessary. 190 | 191 | Getting good annotations of repetitive elements requires a fair bit of manual curation of repeat element models. 192 | 193 | Software used in identification of repeats can be categorised as extrensic and intrinsic tools. 194 | 195 | **Extrinsic tools**, e.g. [RepeatMasker](https://www.repeatmasker.org/), uses the repeat sequence (from a closely related species) listed in Repbase (a repeat database) and annotate there presence in our assembled genome. 196 | 197 | **Intrinsic tool**, e.g. [RepeatModeler](http://www.repeatmasker.org/RepeatModeler/), perform a de novo identification and modelling of TE families. 198 | 199 | ### RepeatModeler 200 | It relies on three de-novo repeat finding programs ( RECON, RepeatScout and LTRHarvest/LTR_retriever ) which employ complementary computational methods for identifying repeat element boundaries and family relationships from sequence data. A brief description each of the programme is given below 201 | 202 | **RECON** can carry out denovo identification and classification of repeat sequence families. In order to achieve that it first perform pairwise alignment between the genomic sequences and then it cluster the sequences using single linkage cluster (agglomerative clustering) approach. It then uses the multiple alignment information to define boundaries of repeats and also to distinguish homologous but distinct repeat element families. 203 | 204 | **RepeatScout** first create a frequency table of l-mers (l=ceil(log_4(L)+1), where L length of input sequence) then it creates a fasta file of all the repetitive elements. It then run 2 rounds of filtering on the repeat elements ("filter-stage-1.prl" and "filter-stage-2.prl"), in the first round it removes low complexity tandem repeats from the fasta file following that the frequency of repeats in fasta fasta file is estimated in the genome using RepeatMasker. In second round of filtering, repeats appearing less than certain number of times (default 10) are removed. 205 | 206 | **LTRHarvest/LTR_retriever** perform de novo detection of full length LTR retrotransposons in large sequence sets. LTRharvest efficiently delivers high quality annotations based on known LTR transposon features like length, distance, and sequence motifs. LTR_retriever carry out accurate identification of LTR retrotransposons (LTR-RTs) from output of LTRharvest and generates non-redundant LTR-RT library for genome annotations. 207 | 208 | Typically both Intrinsic and Extrinsic tools are used to annotate and mask the repaets of a genome and thats what we will be doing here. We will perform Repeatmodeller first to identify novel motifs and then using RepeatMasker we will softmask repeats in the genome using sequences of novel repeats and reference repeats (from Repbase). Before we identify our repeat regions, we must first compile our database using the "BuildDatabase" command of RepeatModeler. This will format the FASTA files for use with RepeatModeler. 209 | ``` 210 | module load RepeatModeler/2.0.4 211 | BuildDatabase -name "athaliana_db" Athaliana_167_TAIR9.fa 212 | ``` 213 | Command options: 214 | ``` 215 | BuildDatabase [-options] -name "mydb" 216 | -name The name of the database to create. 217 | ``` 218 | The complete slurm script called 01_create_db.sh can be found in `02_mask_repeats/` directory. 219 | This will create the following database files: 220 | ``` 221 | ├── athaliana_db.nhr 222 | ├── athaliana_db.nin 223 | ├── athaliana_db.nnd 224 | ├── athaliana_db.nni 225 | ├── athaliana_db.nog 226 | ├── athaliana_db.nsq 227 | ├── athaliana_db.translation 228 | ``` 229 | It is not important that you understand what each file represents. However, if you are interested in verifying that all of your chromosomes were compiled, you may view the .translation file. It should look like: 230 | ``` 231 | NC_003070.9 1 232 | NC_003071.7 2 233 | NC_003074.8 3 234 | NC_003075.7 4 235 | NC_003076.8 5 236 | NC_037304.1 6 237 | NC_000932.1 7 238 | ``` 239 | We see that all seven chromosomes were succesffuly compiled! We are now ready to run the RepeatModeler with the script `02_repeatmodeler.sh`. 240 | ``` 241 | module load RepeatModeler/2.0.4 242 | module load ninja/0.95 243 | 244 | # go to repeat directory 245 | REPDIR=../../results/02_mask_repeats 246 | 247 | cd ${REPDIR} 248 | 249 | # set repdb and run repeatmodeler 250 | REPDB=athaliana_db 251 | 252 | RepeatModeler -threads 30 -database ${REPDB} -LTRStruct 253 | ``` 254 | This process may run for over a day, so be patient and do not submit the job more than once! After completion of the run, there should be a directory called RM*. Let's have a look at its contents: 255 | ``` 256 | RM_150489.*/ 257 | ├── consensi.fa 258 | ├── consensi.fa.classified 259 | ├── round-1 260 | ├── round-2 261 | ├── round-3 262 | ├── round-4 263 | ├── round-5 264 | ``` 265 | Per the RepeatModeler [webpage](http://www.repeatmasker.org/RepeatModeler/), we see each file as: 266 | ``` 267 | round-1/ 268 | sampleDB-#.fa : The genomic sample used in this round 269 | sampleDB-#.fa.lfreq : The RepeatScout lmer table 270 | sampleDB-#.fa.rscons: The RepeatScout generated consensi 271 | sampleDB-#.fa.rscons.filtered : The simple repeat/low 272 | complexity filtered 273 | version of *.rscons 274 | consensi.fa : The final consensi db for this round 275 | family-#-cons.html : A visualization of the model 276 | refinement process. This can be opened 277 | in web browsers that support zooming. 278 | ( such as firefox ). 279 | This is used to track down problems 280 | with the Refiner.pl 281 | index.html : A HTML index to all the family-#-cons.html 282 | files. 283 | round-2/ 284 | sampleDB-#.fa : The genomic sample used in this round 285 | msps.out : The output of the sample all-vs-all 286 | comparison 287 | summary/ : The RECON output directory 288 | eles : The RECON family output 289 | consensi.fa : Same as above 290 | family-#-cons.html : Same as above 291 | index.html : Same as above 292 | round-3/ 293 | Same as round-2 294 | .. 295 | round-n/ 296 | ``` 297 | We see that we have information about the genomic sample used in each round, a consensus seqeuence frequency matrix for the genomic sample, the generated predicted consensus sequences, and visualizations. This format is repeated for various rounds with summaries of all rounds compiled in the summary directories. Our complete, predicted consensus sequences may be found in the various "consensi" fastas. Now that we have generated our consensus sequences, we are ready to mask our genome using the RepeatMasker. 298 | 299 | 300 | ### Masking Regions of Genomic Repetition with RepeatMasker 301 | Now that we have identified our consensus sequences, we are ready to mask them using the RepeatMasker. RepeatMasker requires two arguments, a library of repetitive regions for your organism and the genome fasta for your organism. RepeatMasker will align the repetitive regions to your genome followed by masking those repetitive regions within your genome appropriately. Let's have a look at the RepeatMasker options: 302 | ``` 303 | RepeatMasker 304 | ::small preview of options:: 305 | -lib 306 | Rather than use a database, use your own RepeatModeler consensus fasta to ammend your genome 307 | -small 308 | Returns complete .masked sequence in lower case 309 | -xsmall 310 | Returns repetitive regions in lowercase (rest capitals) rather than 311 | masked 312 | -x Returns repetitive regions masked with Xs rather than Ns 313 | ``` 314 | We want to softmask only repetitive regions, so we will be using the option "xsmall". The complete slurm script is called `03_repeatmasker.sh`: 315 | 316 | ```bash 317 | # load software 318 | module load RepeatMasker/4.1.2 319 | 320 | # set variables 321 | REPLIB=../../results/02_mask_repeats/athaliana_db-families.fa 322 | OUTDIR=../../results/02_mask_repeats/repeatmasker 323 | mkdir -p ${OUTDIR} 324 | 325 | GENOME=../../data/genome/GCF_000001735.4_TAIR10.1_genomic.fna 326 | 327 | RepeatMasker -dir repeatmasker_out -pa 8 -lib ${REPLIB} -gff -a -noisy -xsmall ${GENOME} 328 | ``` 329 | 330 | This will produce the following files: 331 | ``` 332 | /results/02_mask_repeats/repeatmasker/repeatmasker_out 333 | ├── GCF_000001735.4_TAIR10.1_genomic.fna.align 334 | ├── GCF_000001735.4_TAIR10.1_genomic.fna.cat.gz 335 | ├── GCF_000001735.4_TAIR10.1_genomic.fna.masked 336 | ├── GCF_000001735.4_TAIR10.1_genomic.fna.out 337 | ├── GCF_000001735.4_TAIR10.1_genomic.fna.out.gff 338 | └── GCF_000001735.4_TAIR10.1_genomic.fna.tbl 339 | ``` 340 | We are mainly interested in the masked fasta, let's give it a quick look on `GCF_000001735.4_TAIR10.1_genomic.fna.masked`, which shows the genome is soft-masked. 341 | ``` 342 | >less GCF_000001735.4_TAIR10.1_genomic.fna.masked 343 | >Chr1 344 | ccctaaaccctaaaccctaaaccctaaacctctgaatccttaatccctaa 345 | atccctaaatctttaaatcctacatccatgaatccctaaatacctaattc 346 | cctaaacccgaaaccGGTTTCTCTGGTTGAAAATCATTGTGTATATAATG 347 | ATAATTTTATCGTTTTTATGTAATTGCTTATTGTTGTGTGTAGATTTTTT 348 | AAAAATATCATTTGAGGTCAATACAAATCCTATTTCTTGTGGTTTTCTTT 349 | CCTTCACTTAGCTATGGATGGTTTATCTTCATTTGTTATATTGGATACAA 350 | GCTTTGCTACGATCTACATTTGGGAATGTGAGTCTCTTATTGTAACCTTA 351 | GGGTTGGTTTATCTCAAGAATCTTATTAATTGTTTGGACTGTTTATGTTT 352 | GGACATTTATTGTCATTCTTACTCCTTTGTGGAAATGTTTGTTCTATCAA 353 | ``` 354 | RepeatMasker produces masking stats and other relevant output files, lets have a look at few of them. 355 | 356 | **GCF_000001735.4_TAIR10.1_genomic.fna.tbl** have summary stats showing % of genome masked and the repeat elements that were used in the masking and there individual contribution in masking. 357 | ``` 358 | >head -12 GCF_000001735.4_TAIR10.1_genomic.fna.tbl 359 | ================================================== 360 | file name: GCF_000001735.4_TAIR10.1_genomic.fna 361 | sequences: 7 362 | total length: 119668634 bp (119483030 bp excl N/X-runs) 363 | GC level: 36.06 % 364 | bases masked: 20608830 bp ( 17.22 %) 365 | ================================================== 366 | number of length percentage 367 | elements* occupied of sequence 368 | -------------------------------------------------- 369 | Retroelements 9948 8538996 bp 7.14 % 370 | SINEs: 181 23261 bp 0.02 % 371 | 372 | ``` 373 | **GCF_000001735.4_TAIR10.1_genomic.fna.out** This file provide some details on each individual masking by providing SW (Smith-Waterman) scores, percent divergence, deletion, insertion, genomic location and repeat type. SW and divergence score cutoff can bet set while running RepeatMasker. 374 | ``` 375 | SW perc perc perc query position in query matching repeat position in repeat 376 | score div. del. ins. sequence begin end (left) repeat class/family begin end (left) ID 377 | 349 13.6 5.2 4.3 Chr1 1 115 (30427556) C rnd-1_family-2 Unspecified (1084) 286 171 1 378 | 21 2.9 5.7 0.0 Chr1 1064 1098 (30426573) + (CACCCCC)n Simple_repeat 1 37 (0) 2 * 379 | 22 10.0 0.0 0.0 Chr1 1066 1097 (30426574) + (C)n Simple_repeat 1 32 (0) 3 380 | 15 17.1 0.0 0.0 Chr1 1155 1187 (30426484) + (TTTCTT)n Simple_repeat 1 33 (0) 4 381 | 28 8.4 0.0 0.0 Chr1 4291 4328 (30423343) + (AT)n Simple_repeat 1 38 (0) 5 382 | 16 9.3 0.0 0.0 Chr1 5680 5702 (30421969) + (T)n Simple_repeat 1 23 (0) 6 383 | 36 0.0 0.0 0.0 Chr1 8669 8699 (30418972) + (CT)n Simple_repeat 1 31 (0) 7 384 | ``` 385 | ***GCF_000001735.4_TAIR10.1_genomic.fna.out.gff** provides the masking information in a gff format. The columns are , chromosome, software used to annotate the feature (here RepeatMaasker), Feature type, Start and End of feature, Score, Strand and last column shows additional attributes assosiated with the feature. 386 | ``` 387 | ##gff-version 2 388 | ##date 2021-07-27 389 | ##sequence-region Athaliana_167_TAIR9.fa 390 | Chr1 RepeatMasker similarity 1 115 13.6 - . Target "Motif:rnd-1_family-2" 171 286 391 | Chr1 RepeatMasker similarity 1064 1098 2.9 + . Target "Motif:(CACCCCC)n" 1 37 392 | Chr1 RepeatMasker similarity 1066 1097 10.0 + . Target "Motif:(C)n" 1 32 393 | Chr1 RepeatMasker similarity 1155 1187 17.1 + . Target "Motif:(TTTCTT)n" 1 33 394 | Chr1 RepeatMasker similarity 4291 4328 8.4 + . Target "Motif:(AT)n" 1 38 395 | Chr1 RepeatMasker similarity 5680 5702 9.3 + . Target "Motif:(T)n" 1 23 396 | Chr1 RepeatMasker similarity 8669 8699 0.0 + . Target "Motif:(CT)n" 1 31 397 | ``` 398 | **GCF_000001735.4_TAIR10.1_genomic.fna.align** shows the alignment of repeat to the genomic sequence. 399 | ``` 400 | 349 13.63 5.17 4.35 Chr1 1 115 (30427556) C rnd-1_family-2#Unspecified (1084) 286 171 m_b1s001i0 1 401 | Chr1 1 CCCTAAACCCTAAACCCTAAACCCTAAACCTCTGAATCCTTAATCCCTAA 50 402 | - i ii - 403 | C rnd-1_family- 286 CCCTAAACCCTAAACCCTAAACCCTAAACC-CTAAACTCTTAA-CCCTAA 239 404 | Chr1 51 ATCCCTAAATC-TTTAAATCCTACATCCATGAATCCCTAAAT-----ACC 94 405 | - i -ii i v i - i - ?----- 406 | C rnd-1_family- 238 A-CCCTAAACCGCCTAAACCCTAAACCC-TAAA-CCCTAAANCCTAAACC 192 407 | Chr1 95 TAATTCCCTAAACCCGAAACC 115 408 | i vv v 409 | C rnd-1_family- 191 CAAAACCCTAAACCCTAAACC 171 410 | Matrix = 20p35g.matrix 411 | Kimura (with divCpGMod) = 14.34 412 | Transitions / transversions = 2.50 (10/4) 413 | Gap_init rate = 0.06 (7 / 114), avg. gap size = 1.57 (11 / 7) 414 | 21 2.94 5.71 0.00 Chr1 1064 1098 (30426573) (CACCCCC)n#Simple_repeat 1 37 (0) m_b1s252i0 2 415 | Chr1 1064 CACCCCCCACCTCCC-CCCCCC-CCCCCCACCCCCCA 1098 416 | i - - 417 | (CACCCCC)n#Si 1 CACCCCCCACCCCCCACCCCCCACCCCCCACCCCCCA 37 418 | Matrix = Unknown 419 | Transitions / transversions = 1.00 (1/0) 420 | Gap_init rate = 0.06 (2 / 34), avg. gap size = 1.00 (2 / 2) 421 | ``` 422 | 423 | ### Evaluating using BUSCO 424 | In here we will evalute the assemblies using BUSCO. 425 | ```bash 426 | module load busco/5.4.5 427 | 428 | # set output directory for busco, cd into parent directory 429 | BUSCODIR=../../results/02_mask_repeats/busco/ 430 | mkdir -p ${BUSCODIR} 431 | cd ${BUSCODIR} 432 | 433 | OUTDIR=masked_genome 434 | 435 | # input masked genome 436 | MASKED_GENOME=../../02_mask_repeats/repeatmasker/repeatmasker_out/GCF_000001735.4_TAIR10.1_genomic.fna.masked 437 | 438 | # green plant busco database, already downloaded on xanadu. see busco documentation for how to obtain 439 | BUSCODB=/isg/shared/databases/BUSCO/odb10/lineages/viridiplantae_odb10 440 | 441 | # run busco 442 | busco -i ${MASKED_GENOME} \ 443 | -o ${OUTDIR} \ 444 | -c 8 \ 445 | -f \ 446 | -l ${BUSCODB} \ 447 | -m genome 448 | ``` 449 | 450 | General useage of the command: 451 | ``` 452 | usage: busco -i [SEQUENCE_FILE] -l [LINEAGE] -o [OUTPUT_NAME] -m [MODE] [OTHER OPTIONS] 453 | ``` 454 | 455 | The command options we will be using: 456 | ``` 457 | -i FASTA FILE Input sequence file in FASTA format 458 | -l LINEAGE Specify the name of the BUSCO lineage 459 | -o OUTPUT Output folders and files will be labelled with this name 460 | -m MODE BUSCO analysis mode 461 | - geno or genome, for genome assemblies (DNA) 462 | - tran or transcriptome, for transcriptome assemblies (DNA) 463 | - prot or proteins, for annotated gene sets (protein) 464 | ``` 465 | The complete BUSCO scrip is called 04_busco.sh 466 | 467 | In here the busco metrics proposed to describe genome/gene-set/transcriptome completeness used the the following notation: 468 | Where recovered genes are marked as complete (C), and complete genes found with more than one copy is depected as duplicate (D), and complete single copy genes as single-copy (S) genes. The partial recovered genes are named as fragmented (F) and the genes which could not be found is named as missing (M) genes. 469 | So the following notation is used in busco notation: 470 | C: Complete, D: duplicated, F: fragmented, M: missing, n: number of genes used. 471 | 472 | Summary (found at `/results/02_mask_repeats/busco/masked_genome/short_summary.specific.viridiplantae_odb10.masked_genome.txt`) of the inital assembly assesment using BUSCO: 473 | ``` 474 | -------------------------------------------------- 475 | |Results from dataset viridiplantae_odb10 | 476 | -------------------------------------------------- 477 | |C:99.3%[S:98.6%,D:0.7%],F:0.0%,M:0.7%,n:425 | 478 | |422 Complete BUSCOs (C) | 479 | |419 Complete and single-copy BUSCOs (S) | 480 | |3 Complete and duplicated BUSCOs (D) | 481 | |0 Fragmented BUSCOs (F) | 482 | |3 Missing BUSCOs (M) | 483 | |425 Total BUSCO groups searched | 484 | -------------------------------------------------- 485 | ``` 486 | 487 | ## 4. Helixer 488 | 489 | ### Annotation Method 1 (no evidence): Helixer 490 | In this section we will annotate our genome with [Helixer](https://github.com/weberlab-hhu/Helixer), a program that takes only a genome as input and then performs gene calling with Deep Neural Networks. 491 | 492 | We have pulled helixer in a container on our Xanadu, which lives in a public directory. If you are working on a different cluster you will want to pull the image yourself from docker. On Xanadu we have this container in a global location: `/isg/shared/databases/nfx_singularity_cache/helixer-docker_helixer_v0.3.0a0_cuda_11.2.0-cudnn8.sif` 493 | 494 | Navigate the the 03_helixer directory in `/scripts`. Helixer can be run with the following script `01_helixer.sh` 495 | 496 | ```bash 497 | #!/bin/bash 498 | #SBATCH --job-name=helixer 499 | #SBATCH -N 1 500 | #SBATCH -n 1 501 | #SBATCH -c 12 502 | #SBATCH --partition=gpu 503 | #SBATCH --qos=general 504 | #SBATCH --constraint="AVX2&FMA3" 505 | #SBATCH --mail-type=ALL 506 | #SBATCH --mem=20G 507 | #SBATCH --mail-user= 508 | #SBATCH -o %x_%j.out 509 | #SBATCH -e %x_%j.err 510 | 511 | hostname 512 | date 513 | 514 | module load singularity/3.9.2 515 | mkdir tmp 516 | 517 | export TMPDIR=$PWD/tmp 518 | OUTDIR=../../results/03_helixer/ 519 | mkdir -p ${OUTDIR} 520 | 521 | GENOME=../../results/02_mask_repeats/repeatmasker/repeatmasker_out/GCF_000001735.4_TAIR10.1_genomic.fna.masked 522 | 523 | singularity exec /isg/shared/databases/nfx_singularity_cache/helixer-docker_helixer_v0.3.0a0_cuda_11.2.0-cudnn8.sif Helixer.py --lineage land_plant --fasta-path ${GENOME} --gff-output-path ${OUTDIR}/Arabidopsis_thaliana.gff3 524 | ``` 525 | In this script we execute the container with `singularity exec`, run Helixer with command `Helixer.py`, specify the taxonomic lineage with `--lineage land_plant`, the genome with `--fasta-path`, and the output path/file with `--gff-output-path` 526 | 527 | 528 | ## 5. Helixer Evaluation 529 | 530 | Next we moved on to evaluating the annotation with AGAT, BUSCO, and EnTAP. 531 | 532 | [AGAT](https://agat.readthedocs.io/en/latest/) is a toolkit that has scripts to check, fix, and evaluate GFF/GTF files. On Xanadu we have agat as a container in a global location: `/isg/shared/databases/nfx_singularity_cache/depot.galaxyproject.org-singularity-agat-1.0.0--pl5321hdfd78af_0.img`, however if you are working on a different cluster you will need to download the software or pull the container. 533 | 534 | Still in the 03_helixer directory, the first evaluation script is `02_agat_stats.sh`: 535 | 536 | ```bash 537 | module load singularity 538 | 539 | OUTDIR="../../results/03_helixer/agat/statistics" 540 | mkdir -p ${OUTDIR} 541 | 542 | cd ${OUTDIR} 543 | 544 | singularity exec /isg/shared/databases/nfx_singularity_cache/depot.galaxyproject.org-singularity-agat-1.0.0--pl5321hdfd78af_0.img agat_sp_statistics.pl \ 545 | --gff ../../Arabidopsis_thaliana.gff3 \ 546 | -g ../../../../data/genome/GCF_000001735.4_TAIR10.1_genomic.fna \ 547 | -o stats.txt 548 | 549 | 550 | singularity exec /isg/shared/databases/nfx_singularity_cache/depot.galaxyproject.org-singularity-agat-1.0.0--pl5321hdfd78af_0.img agat_sq_stat_basic.pl \ 551 | --gff ../../Arabidopsis_thaliana.gff3 \ 552 | -g ../../../../data/genome/GCF_000001735.4_TAIR10.1_genomic.fna \ 553 | -o basic_stats.txt 554 | 555 | singularity exec /isg/shared/databases/nfx_singularity_cache/depot.galaxyproject.org-singularity-agat-1.0.0--pl5321hdfd78af_0.img agat_sp_functional_statistics.pl \ 556 | --gff ../../Arabidopsis_thaliana.gff3 \ 557 | -g ../../../../data/genome/GCF_000001735.4_TAIR10.1_genomic.fna \ 558 | -o functional_statistics 559 | ``` 560 | We utilize three of AGAT's evaluation scripts here: `agat_sp_statistics.pl`, `agat_sq_stat_basic.pl`, `agat_sp_functional_statistics.pl` 561 | 562 | Results from these runs can be found in the directory `/results/03_helixer/agat/statisticsls`. 563 | 564 | Let's look at the `stats.txt` (`less stats.txt`) 565 | 566 | Here we can look at the mono/multi exonic gene ratio: 567 | 568 | "Number of single exon gene" / "Number of genes" 569 | 570 | 5067/27082=0.187 571 | 572 | 573 | Next we extract a single transcript per gene model and then extract the coding sequences in the script `03_agat_codingsequences.sh` in order to evaluate them with BUSCO and EnTAP. This can be done with `agat_sp_keep_longest_isoform.pl` and `agat_sp_extract_sequences.pl`. 574 | 575 | 576 | ```bash 577 | module load singularity 578 | 579 | OUTDIR="../../results/03_helixer/agat/codingsequences" 580 | mkdir -p ${OUTDIR} 581 | 582 | cd ${OUTDIR} 583 | 584 | singularity exec /isg/shared/databases/nfx_singularity_cache/depot.galaxyproject.org-singularity-agat-1.0.0--pl5321hdfd78af_0.img agat_sp_keep_longest_isoform.pl \ 585 | --gff ../../Arabidopsis_thaliana.gff3 \ 586 | -o helixer_longest_isoform.gtf 587 | 588 | 589 | singularity exec /isg/shared/databases/nfx_singularity_cache/depot.galaxyproject.org-singularity-agat-1.0.0--pl5321hdfd78af_0.img agat_sp_extract_sequences.pl \ 590 | -g helixer_longest_isoform.gtf \ 591 | -f ../../../../data/genome/GCF_000001735.4_TAIR10.1_genomic.fna \ 592 | -p \ 593 | -o helixer_proteins.fasta.faa 594 | ``` 595 | The protein file (`helixer_proteins.fasta.faa`) can then be used in further evaluations. Next we'll run the script `04_busco.sh`: 596 | 597 | ```bash 598 | module load busco/5.4.5 599 | 600 | # green plant busco database. already on the xanadu cluster. see documentation for how to obtain 601 | BUSCODB=/isg/shared/databases/BUSCO/odb10/lineages/viridiplantae_odb10 602 | 603 | #Output directory 604 | OUTDIR=../../results/03_helixer/busco 605 | mkdir -p ${OUTDIR} 606 | 607 | 608 | 609 | # predicted proteins 610 | PROTEINS=../../results/03_helixer/agat/codingsequences/helixer_proteins.fasta.faa 611 | 612 | 613 | # run busco 614 | busco -i ${PROTEINS} \ 615 | -o ${OUTDIR} \ 616 | -c 8 \ 617 | -l ${BUSCODB} \ 618 | -m prot \ 619 | -f 620 | ``` 621 | Let's take a look at the output (`/results/03_helixer/busco/short_summary.specific.viridiplantae_odb10.busco.txt`): 622 | 623 | ```bash 624 | ***** Results: ***** 625 | 626 | C:99.1%[S:98.4%,D:0.7%],F:0.2%,M:0.7%,n:425 627 | 421 Complete BUSCOs (C) 628 | 418 Complete and single-copy BUSCOs (S) 629 | 3 Complete and duplicated BUSCOs (D) 630 | 1 Fragmented BUSCOs (F) 631 | 3 Missing BUSCOs (M) 632 | 425 Total BUSCO groups searched 633 | 634 | 635 | ``` 636 | 637 | Finally, we will run `05_EnTAP.sh` to generate a funtional annotation: 638 | 639 | ```bash 640 | module load EnTAP/0.9.0-beta 641 | module load diamond/0.9.36 642 | 643 | OUTDIR=../../results/03_helixer/EnTAP 644 | mkdir -p ${OUTDIR} 645 | 646 | cp entap_config.txt ${OUTDIR} 647 | cd ${OUTDIR} 648 | 649 | PROTEINS=../agat/codingsequences/helixer_proteins.fasta.faa 650 | 651 | EnTAP --runP \ 652 | -i ${PROTEINS} \ 653 | -d /isg/shared/databases/Diamond/RefSeq/complete.protein.faa.216.dmnd \ 654 | -d /isg/shared/databases/Diamond/Uniprot/uniprot_sprot.dmnd \ 655 | --ontology 0 \ 656 | --threads 16 657 | 658 | date 659 | ``` 660 | The output will be writen in the the entap_outfiles/ folder. 661 | 662 | ```bash 663 | entap_outfiles 664 | ├── final_results 665 | ├── ontology 666 | ├── similarity_search 667 | └── transcriptomes 668 | ``` 669 | 670 | Similarity search directory will contain the results from the [diamond](https://github.com/bbuchfink/diamond) database(s) you included in your search. Inside the processed/ folder you will find the information based on the hits returned from similarity searching against each database. This information contains the best hits (discussed previously) from each database based on e-value, coverage, informativeness, phylogenetic closeness, and contaminant status. 671 | 672 | - In the database you selected under the processed directory it will contain best_hits.faa and .fnn and .tsv files: 673 | - best_hits_contam.faa/.fnn/.tsv will contain contaminants (protein/nucleotide) separated from the best hits file. 674 | - best_hits_no_contam.faa/.fnn/.tsv will contain sequences (protein/nucleotide) that were selected as best hits and not flagged as contaminants 675 | - no_hits.faa/.fnn/.tsv contain sequences (protein/nucleotide) from the transcriptome that did not hit against this particular database 676 | - unselected.tsv will contain result in several hits for each query sequence. With only one best hit being selected, the rest are unselected and end up here 677 | 678 | In the Ontology search folder we will find the ortholog groups / ontolgoy results agains EggNOG pipeline. Inside the processed folder it will contain the annotations. 679 | 680 | In the final_results folder, it will contain the final EnTAP annotations. These files are the summation of each stage of the pipeline and contain the combined information. So these can be considered the most important files! Gene ontology terms are normalized to levels based on the input flag from the user (or the default of 0,3,4). A level of 0 within the filename indicates that ALL GO terms will be printed to the annotation file. Normalization of GO terms to levels is generally done before enrichment analysis and is based upon the hierarchical setup of the Gene Ontology database. 681 | 682 | - final_annotations_lvlX.tsv : X represents the normalized GO terms for the annotation 683 | - final_annotated.faa / .fnn : Nucleotide and protein fasta files containing all sequences that either hit databases through similarity searching or through the ontology stage 684 | - final_unannotated.aa / .fnn : Nucleotide and protein fasta files containing all sequences that did not hit either through similarity searching nor through the ontology stage 685 | 686 | In the following example it shows the results agains the complete diamond database we selected, and EggNOG pipeline and the final results. 687 | 688 | More information on EnTAP can be found in [EnTAP](https://entap.readthedocs.io/) documentation, which has a very comprehensive description. 689 | 690 | Let's take a look at some results (`/results/03_helixer/EnTAP/entap_outfiles/log_file_2023Y6M6D-13h14m4s.txt`) 691 | 692 | ```bash 693 | Final Annotation Statistics 694 | ------------------------------------------------------ 695 | Total Sequences: 27082 696 | Similarity Search 697 | Total unique sequences with an alignment: 25555 698 | Total unique sequences without an alignment: 1527 699 | Gene Families 700 | Total unique sequences with family assignment: 25834 701 | Total unique sequences without family assignment: 1248 702 | Total unique sequences with at least one GO term: 20969 703 | Total unique sequences with at least one pathway (KEGG) assignment: 6038 704 | Totals 705 | Total unique sequences annotated (similarity search alignments only): 411 706 | Total unique sequences annotated (gene family assignment only): 690 707 | Total unique sequences annotated (gene family and/or similarity search): 26245 708 | Total unique sequences unannotated (gene family and/or similarity search): 837 709 | ``` 710 | 711 | ## 6. Braker Annotation with Protein Evidence 712 | 713 | Our second method of annotation in this tutorial is using protein evidence as input with Braker. First we must obtain the protein data and concatenate it into a single fasta file. Navigate to the `scripts/04_braker` directory, and run the `/01_get_proteins.sh` script: 714 | 715 | ```bash 716 | OUTDIR=../../results/04_braker/proteins 717 | mkdir -p ${OUTDIR} 718 | cd ${OUTDIR} 719 | 720 | # per the braker documentation, a protein database can be obtained as below 721 | # this code downloads protein sequences in fasta format for all genes in orthoDB for the Viridiplantae 722 | # then it concatenates them into a single fasta file 723 | # https://github.com/gatech-genemark/ProtHint#protein-database-preparation 724 | wget --no-check-certificate https://v100.orthodb.org/download/odb10_plants_fasta.tar.gz 725 | tar -xvzf odb10_plants_fasta.tar.gz 726 | cat plants/Rawdata/*fs >proteins.fa 727 | ``` 728 | Now we can run Braker with the proteins and masked genome as input (`02_braker_proteins.sh`): 729 | 730 | ```bash 731 | # load software 732 | module load BRAKER/2.1.6 733 | module load ProtHint/2.6.0 734 | 735 | # for testing new perl version: 736 | module unload perl 737 | module load perl/5.36.0 738 | 739 | # set braker output directory and cd there 740 | OUTDIR=../../results/04_braker/proteins 741 | mkdir -p ${OUTDIR} 742 | cd ${OUTDIR} 743 | 744 | # copy augustus config, set variable 745 | export AUGUSTUS_CONFIG_PATH=$(pwd)/config 746 | cp -r /isg/shared/apps/augustus/3.6.0/config/ config 747 | 748 | # create a temp directory for braker temp files 749 | export TMPDIR=$(pwd)/braker_tmp 750 | mkdir -p ${TMPDIR} 751 | 752 | # set variables for input/output files 753 | GENOME="../../02_mask_repeats/repeatmasker/repeatmasker_out/GCF_000001735.4_TAIR10.1_genomic.fna.masked" 754 | PROTEINDB=proteins.fa 755 | 756 | # run braker 757 | braker.pl \ 758 | --genome=${GENOME} \ 759 | --prot_seq=${PROTEINDB} \ 760 | --softmasking \ 761 | --cores 16 \ 762 | --gff3 763 | ``` 764 | To evaluate this genome we will follow the same steps we did with Helixer (agat, busco, and EnTAP). Let's have a look at some of the results: 765 | 766 | AGAT: 767 | 768 | Mono/Multi Ratio 769 | "Number of single exon gene" / "Number of genes" 770 | 7132/30246=0.236 771 | 772 | Busco: 773 | ```bash 774 | ***** Results: ***** 775 | 776 | C:99.5%[S:98.8%,D:0.7%],F:0.2%,M:0.3%,n:425 777 | 423 Complete BUSCOs (C) 778 | 420 Complete and single-copy BUSCOs (S) 779 | 3 Complete and duplicated BUSCOs (D) 780 | 1 Fragmented BUSCOs (F) 781 | 1 Missing BUSCOs (M) 782 | 425 Total BUSCO groups searched 783 | 784 | ``` 785 | 786 | EnTAP: 787 | ```bash 788 | Final Annotation Statistics 789 | ------------------------------------------------------ 790 | Total Sequences: 30246 791 | Similarity Search 792 | Total unique sequences with an alignment: 27785 793 | Total unique sequences without an alignment: 2461 794 | Gene Families 795 | Total unique sequences with family assignment: 28008 796 | Total unique sequences without family assignment: 2238 797 | Total unique sequences with at least one GO term: 22365 798 | Total unique sequences with at least one pathway (KEGG) assignment: 6223 799 | Totals 800 | Total unique sequences annotated (similarity search alignments only): 925 801 | Total unique sequences annotated (gene family assignment only): 1148 802 | Total unique sequences annotated (gene family and/or similarity search): 28933 803 | Total unique sequences unannotated (gene family and/or similarity search): 1313 804 | 805 | ``` 806 | 807 | ## 8. EASEL Pipeline 808 | 809 | EASEL (Efficient, Accurate, Scalable Eukaryotic modeLs) is a genome annotation pipeline that utilizes machine learning, RNA folding, and functional annotations to enhance gene prediction accuracy. is a nextflow pipeline that annotates a genome with RNA-seq reads as evidence. 810 | 811 | More about methods and the workflows may be found on the EASEL website[https://gitlab.com/PlantGenomicsLab/easel]. 812 | 813 | To run EASEL on xanadu we must first pull the container, and then provide it with a set of paramaters. The params.yaml file: 814 | 815 | Then we ran the following script: 816 | 817 | 818 | ## 9. Compare Evaluations 819 | GFFCompare can be used to compare, merge, annotate and estimate accuracy of one or more GFF files when compared with a reference annotation. Here we run GFFCompare with the three annotation files we generated through this tutorial, and the "truth" that we pulled in the beginning in the 01_raw_data directory. 820 | 821 | Navigate to `06_gffcompare` and `sbatch 01_gffcompare.sh`: 822 | 823 | ```bash 824 | module load gffcompare/0.10.4 825 | 826 | # output directory 827 | OUTDIR=../../results/06_gffcompare_2 828 | mkdir -p ${OUTDIR} 829 | 830 | # files 831 | NCBIANNO=../../data/genome/GCF_000001735.4_TAIR10.1_genomic.gff 832 | HELIXER=../../results/03_helixer/Arabidopsis_thaliana.gff3 833 | BRAKERPROTEIN=../../results/04_braker/proteins/braker/augustus.hints.gtf 834 | EASEL=../../results/05_EASEL/arabidopsis/final_predictions/arabidopsis_filtered.gtf 835 | 836 | # run gffcompare 837 | gffcompare -R -r <(awk '$7 !~ /?/' ${NCBIANNO}) -o ${OUTDIR}/helixer ${HELIXER} 838 | 839 | gffcompare -R -r <(awk '$7 !~ /?/' ${NCBIANNO}) -o ${OUTDIR}/braker ${BRAKERPROTEIN} 840 | 841 | gffcompare -R -r <(awk '$7 !~ /?/' ${NCBIANNO}) -o ${OUTDIR}/easel ${EASEL} 842 | ``` 843 | 844 | Let's take a look at the results: 845 | 846 | EASEL: 847 | ```bash 848 | #= Summary for dataset: ../../results/05_EASEL/arabidopsis/final_predictions/arabidopsis_filtered.gtf 849 | # Query mRNAs : 29381 in 19324 loci (25053 multi-exon transcripts) 850 | # (6209 multi-transcript loci, ~1.5 transcripts per locus) 851 | # Reference mRNAs : 38462 in 20342 loci (34229 multi-exon) 852 | # Super-loci w/ reference transcripts: 18924 853 | #-----------------| Sensitivity | Precision | 854 | Base level: 68.0 | 99.2 | 855 | Exon level: 52.9 | 65.9 | 856 | Intron level: 77.9 | 83.3 | 857 | Intron chain level: 29.9 | 40.8 | 858 | Transcript level: 29.6 | 38.7 | 859 | Locus level: 54.6 | 57.1 | 860 | 861 | Matching intron chains: 10225 862 | Matching transcripts: 11366 863 | Matching loci: 11112 864 | 865 | Missed exons: 18649/159922 ( 11.7%) 866 | Novel exons: 942/126977 ( 0.7%) 867 | Missed introns: 11149/115825 ( 9.6%) 868 | Novel introns: 1786/108273 ( 1.6%) 869 | Missed loci: 0/20342 ( 0.0%) 870 | Novel loci: 15/19324 ( 0.1%) 871 | 872 | Total union super-loci across all input datasets: 18946 873 | 29381 out of 29381 consensus transcripts written in ../../results/06_gffcompare_2/easel.annotated.gtf (0 discarded as redundant) 874 | ``` 875 | BRAKER: 876 | ```bash 877 | #= Summary for dataset: ../../results/04_braker/proteins/braker/augustus.hints.gtf 878 | # Query mRNAs : 32230 in 30204 loci (24877 multi-exon transcripts) 879 | # (1655 multi-transcript loci, ~1.1 transcripts per locus) 880 | # Reference mRNAs : 48521 in 27544 loci (41185 multi-exon) 881 | # Super-loci w/ reference transcripts: 27253 882 | #-----------------| Sensitivity | Precision | 883 | Base level: 68.8 | 98.0 | 884 | Exon level: 56.1 | 68.0 | 885 | Intron level: 84.7 | 92.3 | 886 | Intron chain level: 39.8 | 65.9 | 887 | Transcript level: 37.8 | 56.9 | 888 | Locus level: 64.8 | 59.7 | 889 | 890 | Matching intron chains: 16405 891 | Matching transcripts: 18327 892 | Matching loci: 17838 893 | 894 | Missed exons: 15214/186166 ( 8.2%) 895 | Novel exons: 3582/151634 ( 2.4%) 896 | Missed introns: 12310/131340 ( 9.4%) 897 | Novel introns: 6505/120548 ( 5.4%) 898 | Missed loci: 0/27544 ( 0.0%) 899 | Novel loci: 926/30204 ( 3.1%) 900 | 901 | Total union super-loci across all input datasets: 28276 902 | 32230 out of 32230 consensus transcripts written in ../../results/06_gffcompare_2/braker.annotated.gtf (0 discarded as redundant) 903 | ``` 904 | HELIXER: 905 | ```bash 906 | #= Summary for dataset: ../../results/03_helixer/Arabidopsis_thaliana.gff3 907 | # Query mRNAs : 27082 in 27082 loci (22015 multi-exon transcripts) 908 | # (0 multi-transcript loci, ~1.0 transcripts per locus) 909 | # Reference mRNAs : 46994 in 26068 loci (40657 multi-exon) 910 | # Super-loci w/ reference transcripts: 24454 911 | #-----------------| Sensitivity | Precision | 912 | Base level: 86.7 | 95.7 | 913 | Exon level: 70.4 | 80.3 | 914 | Intron level: 84.5 | 89.0 | 915 | Intron chain level: 34.4 | 63.6 | 916 | Transcript level: 36.5 | 63.3 | 917 | Locus level: 65.1 | 63.3 | 918 | 919 | Matching intron chains: 13999 920 | Matching transcripts: 17147 921 | Matching loci: 16981 922 | 923 | Missed exons: 5266/183080 ( 2.9%) 924 | Novel exons: 5195/150243 ( 3.5%) 925 | Missed introns: 5319/129769 ( 4.1%) 926 | Novel introns: 5691/123161 ( 4.6%) 927 | Missed loci: 0/26068 ( 0.0%) 928 | Novel loci: 514/27082 ( 1.9%) 929 | 930 | Total union super-loci across all input datasets: 25087 931 | 27082 out of 27082 consensus transcripts written in ../../results/06_gffcompare_2/helixer.annotated.gtf (0 discarded as redundant) 932 | ``` 933 | 934 | 935 | --------------------------------------------------------------------------------