├── LICENSE.md ├── README.md ├── User tutorial.pdf ├── doc ├── HGT_pipeline.png └── YOL164W_circle_tree.png ├── example ├── HGT_workflow.py ├── blastp_files │ └── AAT92670.txt ├── input.fasta └── output.tsv ├── main ├── HGT_workflow_close.py ├── HGT_workflow_distant.py └── blastp.py ├── phylogenetics ├── input │ ├── YOL164W.fasta │ ├── YOL164W.txt │ └── YOL164W_homologs.fasta ├── intermediate │ ├── YOL164W.bionj │ ├── YOL164W.ckp.gz │ ├── YOL164W.contree │ ├── YOL164W.iqtree │ ├── YOL164W.log │ ├── YOL164W.mldist │ ├── YOL164W.model.gz │ ├── YOL164W.splits.nex │ ├── YOL164W.treefile │ ├── YOL164W_aln.fasta │ ├── YOL164W_aln_trimmed.fasta │ ├── YOL164W_midpoint-color.txt │ ├── YOL164W_midpoint-font.txt │ └── YOL164W_midpoint.tree ├── output │ ├── YOL164W_circle_tree.pdf │ ├── YOL164W_midpoint-color.txt │ ├── YOL164W_midpoint-font.txt │ ├── YOL164W_midpoint.tree │ └── YOL164W_rectangular_tree.pdf └── scripts │ ├── HGT_homologs_sequence.py │ ├── create_iTOL_config.pl │ ├── generate_tree.sh │ └── midpoint_tree.R └── requirements.txt /LICENSE.md: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Systems and Synthetic Biology at Chalmers University of Technology 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # HGTphyloDetect 2 | Horizontal gene transfer (HGT) refers to the exchange of genetic material 3 | between disparate groups of organisms other than from parent to offspring, 4 | which has been confirmed as a very significant factor in adaptive 5 | evolution, disease emergence and metabolic shift that can act across various 6 | species. However, current methods for HGT detection are usually not automatic, 7 | narrow applicable or unavailable to use. In this work, we developed a versatile computational 8 | toolbox named HGTphyloDetect by combining a high-throughput pipeline together with phylogenetic analysis 9 | to facilitate comprehensive investigation of the potential mechanism for HGT events. Tests on two case 10 | studies suggest that this approach could effectively 11 | identify horizontally acquired genes with high accuracy. In-depth phylogenetic analysis further facilitates 12 | the navigation of the potential donors and detailed gene transmission process. The HGTphyloDetect toolbox 13 | is designed for ease of use and could accurately find HGT events with a very low false discovery rate 14 | in a high-throughput manner. 15 | 16 | ## HGT identification pipeline 17 | ![image](https://github.com/SysBioChalmers/HGTphyloDetect/blob/master/doc/HGT_pipeline.png) 18 | 19 | ## Citation 20 | Please cite this paper: 21 | Yuan, Le, et al. _HGTphyloDetect: facilitating the identification and phylogenetic analysis of horizontal gene transfer._ Briefings in Bioinformatics (2023). https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbad035/7031155. 22 | 23 | ## Installation 24 | Install the latest version with: 25 | ```bash 26 | $ git clone https://github.com/SysBioChalmers/HGTphyloDetect.git 27 | $ cd HGTphyloDetect 28 | $ pip install -r requirements.txt 29 | ``` 30 | Please note: this is sufficient to run the HGT detection functionality of HGTphyloDetect. Additional software dependencies and installation instructions are specified in the [User tutorial](https://github.com/SysBioChalmers/HGTphyloDetect/blob/master/User%20tutorial.pdf). 31 | 32 | ## Example 33 | We provide a user-friendly example for small test, users just need to prepare a FASTA file including protein id and protein sequence, note that protein id should be from the GenBank protein database.

34 | (1) If you are now in the HGTphyloDetect directory, just enter into the folder [example](https://github.com/SysBioChalmers/HGTphyloDetect/tree/master/example) via the command line: 35 | ```linux 36 | cd example 37 | ``` 38 | 39 | (2.1) Then users can run the script for the input file (default AI value = 45, out_pct = 0.90): 40 | ```linux 41 | python HGT_workflow.py input.fasta 42 | ``` 43 | 44 | (2.2) If users want to change the default values for the parameters used in the pipeline, e.g., AI value = 40, out_pct = 0.80, just reset the constant values and run the following: 45 | ```linux 46 | python HGT_workflow.py input.fasta AI=40 out_pct=0.80 47 | ``` 48 | 49 | (3) Finally, our software could generate the output results as a file under the folder [example](https://github.com/SysBioChalmers/HGTphyloDetect/tree/master/example) for this gene/protein. The output file includes some important information, i.e., Alien index, E value and donor information. For example: 50 | |Gene/Protein | Alien index | E value | Donor id | Donor taxonomy | 51 | |:---------------:|:---------------:|:---------------:|:---------------:|:---------------:| 52 | | AAT92670 | 199.18| 3.15e-87| WP_208929673| Bacteria/Firmicutes| 53 | 54 | ## User Manual 55 | Please check the documentation [User tutorial](https://github.com/SysBioChalmers/HGTphyloDetect/blob/master/User%20tutorial.pdf) for the user manual. Here you can download this file for your case studies. 56 | 57 | ## Contact 58 | * [Le Yuan](https://www.chalmers.se/en/Staff/Pages/leyu.aspx) ([@leyuan](https://github.com/le-yuan)), Chalmers University of Technology, Sweden 59 | -------------------------------------------------------------------------------- /User tutorial.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SysBioChalmers/HGTphyloDetect/d4ff8c5dbdaf3b200e45dd57962dfb11776f43d7/User tutorial.pdf -------------------------------------------------------------------------------- /doc/HGT_pipeline.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SysBioChalmers/HGTphyloDetect/d4ff8c5dbdaf3b200e45dd57962dfb11776f43d7/doc/HGT_pipeline.png -------------------------------------------------------------------------------- /doc/YOL164W_circle_tree.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SysBioChalmers/HGTphyloDetect/d4ff8c5dbdaf3b200e45dd57962dfb11776f43d7/doc/YOL164W_circle_tree.png -------------------------------------------------------------------------------- /example/HGT_workflow.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | 4 | # Step 1 : BLAST hits were parsed to retrieve associated taxonomic information 5 | # using the NCBI's taxonomy database. 6 | 7 | # Step 2: Calculate Alien Index (AI) values and out_pct based on the above information. 8 | 9 | # Step 3: Output the horizontal gene transfer (HGT) high-throughput identification results. 10 | 11 | import sys 12 | import os 13 | import math 14 | import csv 15 | import warnings 16 | from Bio import BiopythonWarning 17 | from Bio import SeqIO 18 | from Bio import Entrez 19 | from ete3 import NCBITaxa 20 | 21 | 22 | def parse_NCBI(gene): 23 | with open("./blastp_files/%s.txt" % gene, "r") as filename : 24 | data = filename.readlines() 25 | for line in data : 26 | if line.strip("\n").endswith("found") : 27 | index = data.index(line) 28 | 29 | blast_results = data[index+1:-1] 30 | 31 | accession_number = list() 32 | evalue = dict() 33 | for blast in blast_results : 34 | accession = blast.strip("\n").split("\t")[1] 35 | accession_number.append(accession) 36 | evalue[accession] = float(blast.strip('\n').split("\t")[2]) 37 | return accession_number, evalue 38 | 39 | def getTaxid(accession): 40 | # Retrieving data in the GenBank using only the GenBank code accession in biopython 41 | # https://www.ncbi.nlm.nih.gov/books/NBK25497/table/chapter2.T._entrez_unique_identifiers_ui/?report=objectonly 42 | # https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.EFetch 43 | # https://biopython.org/DIST/docs/api/Bio.Entrez-module.html 44 | Entrez.email = "abcd@ncbi.org" 45 | 46 | # https://www.biostars.org/p/304175/ 47 | # get tax id using only the GenBank code accession in biopython 48 | # handle = Entrez.efetch(db='protein', id="NP_012706.1", rettype='gb') 49 | handle = Entrez.efetch(db='protein', id=accession, rettype='gb') 50 | record = SeqIO.read(handle,'genbank') 51 | # print(record.features[0].qualifiers) 52 | if record.features[0].qualifiers['db_xref'][0].split(":")[0] == 'taxon': 53 | taxid = record.features[0].qualifiers['db_xref'][0].split(":")[1] # the type is a string 54 | organism = record.features[0].qualifiers['organism'][0] 55 | # seq = record.seq 56 | # print(taxid,organism) 57 | 58 | return taxid 59 | 60 | def main(AI=45, out_pct=0.90) : 61 | name = sys.argv[1:][0] 62 | if len(sys.argv) == 2 : 63 | pass 64 | else : 65 | AI = float(sys.argv[2].split("=")[1]) 66 | out_pct = float(sys.argv[3].split("=")[1]) 67 | genes = list() 68 | HGT = list() 69 | warnings.simplefilter('ignore', BiopythonWarning) 70 | 71 | with open(name, 'r') as handleGene : 72 | for record in SeqIO.parse(handleGene, "fasta") : 73 | gene = str(record.id) 74 | genes.append(gene) 75 | 76 | n=0 77 | for gene in genes : 78 | n += 1 79 | print("This is gene %d------------------" %(n)) 80 | print(gene) 81 | 82 | if os.path.exists("./blastp_files/%s.txt" % gene) : 83 | accession_number,evalue = parse_NCBI(gene) 84 | print('Yes, blast file already exists, nice!') 85 | else : 86 | # Need to install blast! 87 | myCmd = "blastp -db nr -remote -query ./%s.fasta -max_target_seqs 250 -task 'blastp-fast' -outfmt '7 qacc sacc evalue bitscore length pident' -out ./blastp_files/%s.txt" %('input', gene) 88 | os.system(myCmd) 89 | 90 | ncbi = NCBITaxa() 91 | outgroup = list() 92 | ingroup = list() 93 | outgroup_species = list() 94 | ingroup_species = list() 95 | outgroup_dict = dict() 96 | ingroup_dict = dict() 97 | e_minus = 1e-200 98 | 99 | try : 100 | gene_taxid = getTaxid(gene) 101 | gene_lineage = ncbi.get_lineage(gene_taxid) 102 | gene_lineage2ranks = ncbi.get_rank(gene_lineage) 103 | gene_ranks2lineage = dict((rank, taxid) for (taxid, rank) in gene_lineage2ranks.items()) 104 | gene_taxonomy_alignment = gene_ranks2lineage 105 | gene_kingdom = gene_taxonomy_alignment['kingdom'] 106 | gene_subphylum = gene_taxonomy_alignment['subphylum'] 107 | # print(gene_kingdom) 108 | # print(type(gene_kingdom)) # 109 | # print(gene_subphylum) 110 | except : 111 | print('Attention: please check the gene accession id!') 112 | break 113 | 114 | for accession in accession_number[:200] : 115 | try : 116 | # print(accession) 117 | taxid = getTaxid(accession) 118 | print('Taxid by BLAST:', taxid) 119 | except : 120 | continue 121 | 122 | try : 123 | lineage = ncbi.get_lineage(taxid) 124 | lineage2ranks = ncbi.get_rank(lineage) 125 | ranks2lineage = dict((rank, taxid) for (taxid, rank) in lineage2ranks.items()) 126 | 127 | taxonomy_alignment = ranks2lineage 128 | LINNAEUS_FILTER = ["subphylum","kingdom","superkingdom"] 129 | except : 130 | print('Warning: %s taxid not found!' % str(taxid)) 131 | continue 132 | 133 | try : 134 | if taxonomy_alignment['kingdom'] != gene_kingdom : 135 | outgroup.append(accession) 136 | outgroup_species.append(taxonomy_alignment['species']) 137 | 138 | if taxonomy_alignment['kingdom'] == gene_kingdom and taxonomy_alignment['subphylum'] != gene_subphylum : 139 | ingroup.append(accession) 140 | ingroup_species.append(taxonomy_alignment['species']) 141 | 142 | except : 143 | outgroup.append(accession) 144 | try : 145 | outgroup_species.append(taxonomy_alignment['species']) 146 | except : 147 | continue 148 | 149 | for accession_id in outgroup : 150 | outgroup_dict[accession_id] = evalue[accession_id] 151 | 152 | for accession_id in ingroup : 153 | ingroup_dict[accession_id] = evalue[accession_id] 154 | 155 | if outgroup_dict : 156 | min_outgroup_key = min(outgroup_dict,key=outgroup_dict.get) 157 | min_outgroup_evalue = outgroup_dict[min_outgroup_key] 158 | else : 159 | min_outgroup_evalue = 1 160 | 161 | if ingroup_dict : 162 | min_ingroup_key = min(ingroup_dict,key=ingroup_dict.get) 163 | min_ingroup_evalue = ingroup_dict[min_ingroup_key] 164 | else : 165 | min_ingroup_evalue = 1 166 | 167 | alienindex = format(math.log(min_ingroup_evalue+e_minus, math.e)-math.log(min_outgroup_evalue+e_minus, math.e), '.2f') 168 | try : 169 | outg_pct = format(len(set(outgroup_species))/(len(set(outgroup_species))+len(set(ingroup_species))), '.4f') 170 | except : 171 | continue 172 | 173 | print('Alien index: %s' % str(alienindex)) 174 | 175 | if float(alienindex) >= AI and float(outg_pct) >= out_pct : 176 | print('This is a HGT event') 177 | print('Accession_id: %s' % min_outgroup_key) 178 | print('Evalue: %s' % evalue[min_outgroup_key]) 179 | taxid= getTaxid(min_outgroup_key) 180 | Evalue = evalue[min_outgroup_key] 181 | lineage = ncbi.get_lineage(taxid) 182 | lineage2ranks = ncbi.get_rank(lineage) 183 | ranks2lineage = dict((rank, taxid) for (taxid, rank) in lineage2ranks.items()) 184 | # print(lineage) 185 | # print(ranks2lineage) 186 | taxid2name = ncbi.get_taxid_translator([ranks2lineage['phylum'], ranks2lineage['class'], ranks2lineage['superkingdom']]) 187 | print(taxid2name[ranks2lineage['phylum']]) 188 | 189 | superkingdom = taxid2name[ranks2lineage['superkingdom']] 190 | phylum = taxid2name[ranks2lineage['phylum']] 191 | hgt = 'Yes' 192 | taxonomy = superkingdom + '/' + phylum 193 | 194 | item = [gene, alienindex, Evalue, min_outgroup_key, taxonomy] 195 | HGT.append(item) 196 | 197 | else: 198 | # print('This is not a HGT event') 199 | hgt = 'No' 200 | HGT.append([gene,alienindex,'No', 'No', 'No']) 201 | 202 | outfile = open("./output.tsv", "wt") 203 | tsv_writer = csv.writer(outfile, delimiter="\t") 204 | column = ['Gene/Protein', 'Alien index', 'E value', 'Donor id', 'Donor taxonomy'] 205 | tsv_writer.writerow(column) 206 | for HGT_info in HGT : 207 | tsv_writer.writerow(HGT_info) 208 | outfile.close() 209 | 210 | 211 | if __name__== "__main__": 212 | main() 213 | -------------------------------------------------------------------------------- /example/blastp_files/AAT92670.txt: -------------------------------------------------------------------------------- 1 | # BLASTP 2.10.1+ 2 | # Query: AAT92670 3 | # RID: 8MMNC23C016 4 | # Database: nr 5 | # Fields: query acc., subject acc., evalue, bit score, alignment length, % identity 6 | # 250 hits found 7 | AAT92670 5COG_A 1.11e-126 364 179 100.000 8 | AAT92670 NP_010829 4.83e-126 362 179 100.000 9 | AAT92670 AJU85562 1.31e-125 360 179 99.441 10 | AAT92670 AJV04521 2.15e-125 360 179 99.441 11 | AAT92670 CAD6614768 2.15e-125 360 179 99.441 12 | AAT92670 ONH79032 2.29e-125 360 179 99.441 13 | AAT92670 AJU96086 3.02e-125 360 179 99.441 14 | AAT92670 AHY75482 3.88e-125 359 179 99.441 15 | AAT92670 PTN18717 4.29e-125 359 179 99.441 16 | AAT92670 CAD6614350 6.57e-125 359 179 99.441 17 | AAT92670 CAD6614837 7.84e-125 358 179 98.883 18 | AAT92670 AJU59728 1.16e-124 358 179 98.883 19 | AAT92670 CAD6614250 2.96e-124 357 179 98.883 20 | AAT92670 AJU63907 5.60e-123 354 179 97.765 21 | AAT92670 GHM89243 4.22e-122 352 179 97.207 22 | AAT92670 AJV01730 1.00e-121 351 179 97.207 23 | AAT92670 AJU98194 1.33e-121 350 179 97.207 24 | AAT92670 EGA83280 2.88e-111 324 159 99.371 25 | AAT92670 EGA75337 2.44e-99 295 145 97.931 26 | AAT92670 ONH78076 8.85e-90 270 136 95.588 27 | AAT92670 WP_208929673 3.15e-87 263 168 73.214 28 | AAT92670 WP_206843940 8.25e-86 260 168 73.214 29 | AAT92670 WP_086277901 4.52e-85 258 168 72.024 30 | AAT92670 WP_069646943 1.07e-84 257 168 72.024 31 | AAT92670 WP_086330545 7.61e-84 254 168 72.619 32 | AAT92670 WP_086315029 1.39e-83 254 168 72.024 33 | AAT92670 WP_071877120 2.00e-83 254 168 70.833 34 | AAT92670 WP_069653266 3.13e-82 251 168 68.452 35 | AAT92670 WP_010761928 1.01e-81 249 168 69.643 36 | AAT92670 WP_206838637 1.58e-81 249 168 67.857 37 | AAT92670 WP_207113218 2.40e-81 248 168 70.238 38 | AAT92670 WP_069635085 3.97e-81 248 168 70.238 39 | AAT92670 WP_207695964 4.33e-81 248 168 69.643 40 | AAT92670 WP_206849624 5.23e-81 248 168 67.262 41 | AAT92670 WP_010766489 8.47e-81 247 167 69.461 42 | AAT92670 WP_111248972 1.24e-80 247 168 67.857 43 | AAT92670 WP_010770404 2.61e-80 246 168 69.048 44 | AAT92670 WP_025870318 4.42e-80 245 168 69.048 45 | AAT92670 WP_086345820 4.42e-80 245 168 69.048 46 | AAT92670 WP_088270311 1.03e-79 244 168 66.667 47 | AAT92670 WP_209551020 5.93e-75 232 168 64.881 48 | AAT92670 WP_035019983 9.81e-75 232 168 65.476 49 | AAT92670 WP_228159366 4.20e-73 228 168 62.500 50 | AAT92670 WP_074403517 5.97e-73 228 174 60.920 51 | AAT92670 WP_135025830 6.66e-73 227 174 60.920 52 | AAT92670 WP_135018024 6.74e-73 227 174 60.920 53 | AAT92670 WP_034573059 8.48e-73 227 174 60.920 54 | AAT92670 WP_124006429 1.63e-72 226 168 63.095 55 | AAT92670 WP_086319184 2.37e-72 226 168 64.881 56 | AAT92670 WP_004559864 3.11e-72 225 168 63.690 57 | AAT92670 WP_195495722 4.14e-72 225 168 61.905 58 | AAT92670 WP_119907274 4.38e-72 225 174 60.345 59 | AAT92670 WP_096711087 4.62e-72 225 168 61.905 60 | AAT92670 WP_005227560 5.38e-72 225 166 61.446 61 | AAT92670 WP_115231784 6.41e-72 224 166 61.446 62 | AAT92670 WP_104688309 6.92e-72 224 168 64.881 63 | AAT92670 WP_194194192 7.38e-72 224 166 61.446 64 | AAT92670 WP_012492203 7.64e-72 224 168 63.095 65 | AAT92670 WP_003128966 8.16e-72 224 168 61.905 66 | AAT92670 WP_220022871 8.62e-72 224 168 65.476 67 | AAT92670 WP_087913410 9.83e-72 224 168 62.500 68 | AAT92670 HCO71425 1.04e-71 224 166 62.048 69 | AAT92670 WP_194853683 1.06e-71 224 168 63.095 70 | AAT92670 WP_121260015 1.12e-71 224 166 61.446 71 | AAT92670 WP_086294562 1.17e-71 224 166 60.843 72 | AAT92670 WP_128488310 1.32e-71 224 166 60.843 73 | AAT92670 WP_036076428 1.51e-71 224 167 62.275 74 | AAT92670 WP_074934572 1.64e-71 224 166 61.446 75 | AAT92670 WP_167822701 1.64e-71 224 166 60.843 76 | AAT92670 WP_016994205 2.16e-71 223 167 62.275 77 | AAT92670 WP_014918942 2.21e-71 223 168 64.881 78 | AAT92670 WP_054607410 2.33e-71 223 168 64.881 79 | AAT92670 CCK23958 2.34e-71 223 168 62.500 80 | AAT92670 WP_159161509 2.39e-71 223 166 61.446 81 | AAT92670 WP_147587199 2.78e-71 223 168 61.310 82 | AAT92670 WP_016609715 2.81e-71 223 166 60.843 83 | AAT92670 WP_155853236 2.90e-71 223 168 61.310 84 | AAT92670 WP_221680393 3.14e-71 223 167 62.275 85 | AAT92670 ADX70393 3.43e-71 223 168 64.881 86 | AAT92670 WP_023061843 3.95e-71 223 168 64.881 87 | AAT92670 WP_054550367 4.35e-71 223 167 62.275 88 | AAT92670 WP_066037277 4.65e-71 222 167 62.275 89 | AAT92670 WP_123032107 4.70e-71 222 168 63.095 90 | AAT92670 WP_151493937 5.13e-71 222 168 62.500 91 | AAT92670 WP_003577112 5.42e-71 222 168 62.500 92 | AAT92670 WP_081116974 5.42e-71 222 166 60.843 93 | AAT92670 WP_061053753 5.48e-71 222 166 60.843 94 | AAT92670 WP_004225105 6.18e-71 222 167 62.275 95 | AAT92670 WP_016369059 6.32e-71 222 168 62.500 96 | AAT92670 WP_195265238 6.53e-71 222 166 61.446 97 | AAT92670 WP_172998771 6.90e-71 222 168 64.881 98 | AAT92670 WP_142973918 7.21e-71 222 166 61.446 99 | AAT92670 WP_086316885 7.29e-71 222 168 61.310 100 | AAT92670 WP_003586053 7.69e-71 222 168 62.500 101 | AAT92670 WP_167822118 8.97e-71 222 166 60.843 102 | AAT92670 WP_005232023 8.97e-71 222 166 61.446 103 | AAT92670 WP_054612910 9.79e-71 221 167 62.275 104 | AAT92670 WP_142963124 1.00e-70 221 166 61.446 105 | AAT92670 WP_115673276 1.06e-70 221 167 62.275 106 | AAT92670 WP_213355573 1.14e-70 221 166 60.843 107 | AAT92670 WP_077453653 1.18e-70 221 166 61.446 108 | AAT92670 WP_131520065 1.21e-70 221 167 61.677 109 | AAT92670 WP_015508975 1.27e-70 221 166 60.843 110 | AAT92670 WP_205840691 1.29e-70 221 168 64.286 111 | AAT92670 WP_003600237 1.47e-70 221 168 62.500 112 | AAT92670 WP_081119017 1.50e-70 221 166 61.446 113 | AAT92670 WP_060792753 1.50e-70 221 166 60.843 114 | AAT92670 KRO14293 1.50e-70 221 168 64.881 115 | AAT92670 WP_218193225 1.75e-70 221 166 61.446 116 | AAT92670 WP_057730345 1.83e-70 221 168 64.881 117 | AAT92670 WP_194957952 1.85e-70 221 168 61.905 118 | AAT92670 WP_194186201 1.95e-70 221 166 61.446 119 | AAT92670 NTJ98307 2.06e-70 221 168 64.286 120 | AAT92670 WP_076651954 2.11e-70 221 168 61.905 121 | AAT92670 WP_048392561 2.15e-70 221 167 61.677 122 | AAT92670 WP_196498648 2.18e-70 221 168 62.500 123 | AAT92670 WP_016388093 2.20e-70 221 168 61.905 124 | AAT92670 WP_086293992 2.83e-70 220 166 60.843 125 | AAT92670 WP_036117070 2.89e-70 220 167 61.677 126 | AAT92670 WP_142965075 2.93e-70 220 166 60.843 127 | AAT92670 WP_173009066 3.12e-70 220 168 64.286 128 | AAT92670 WP_003568315 3.45e-70 220 168 61.905 129 | AAT92670 WP_167827335 3.76e-70 220 166 61.446 130 | AAT92670 WP_074536584 4.48e-70 220 166 60.843 131 | AAT92670 WP_142973074 4.59e-70 220 166 60.843 132 | AAT92670 WP_165846893 5.64e-70 219 168 61.905 133 | AAT92670 WP_123022120 6.29e-70 219 168 61.905 134 | AAT92670 WP_058205773 7.18e-70 219 168 63.690 135 | AAT92670 WP_011675062 8.10e-70 219 168 61.905 136 | AAT92670 WP_195961081 9.44e-70 219 166 60.843 137 | AAT92670 WP_008377943 1.54e-69 219 166 60.843 138 | AAT92670 WP_017152268 1.63e-69 218 168 59.524 139 | AAT92670 WP_221685466 1.70e-69 218 166 60.241 140 | AAT92670 WP_035450618 1.80e-69 218 167 60.479 141 | AAT92670 WP_168784600 2.03e-69 218 167 64.072 142 | AAT92670 WP_142972095 2.12e-69 218 166 60.843 143 | AAT92670 WP_058218936 2.29e-69 218 167 64.072 144 | AAT92670 WP_196043740 2.64e-69 218 166 60.843 145 | AAT92670 WP_087664892 3.22e-69 218 166 60.843 146 | AAT92670 WP_010748807 3.25e-69 218 166 60.843 147 | AAT92670 WP_086333006 3.25e-69 218 166 60.843 148 | AAT92670 EPD07047 4.42e-69 217 166 62.048 149 | AAT92670 EPC89042 4.87e-69 217 168 61.310 150 | AAT92670 WP_201705749 5.32e-69 217 166 60.241 151 | AAT92670 EKP98078 6.77e-69 217 168 61.310 152 | AAT92670 WP_172982252 7.54e-69 216 163 65.644 153 | AAT92670 WP_114820579 7.89e-69 217 166 61.446 154 | AAT92670 WP_002191562 1.00e-68 216 168 58.333 155 | AAT92670 WP_063226548 1.18e-68 216 168 58.333 156 | AAT92670 WP_068709110 1.42e-68 216 168 60.119 157 | AAT92670 WP_003596614 1.49e-68 216 168 61.310 158 | AAT92670 WP_061674712 1.62e-68 216 168 58.333 159 | AAT92670 WP_229443398 1.85e-68 216 166 62.651 160 | AAT92670 WP_202067617 1.89e-68 216 166 62.651 161 | AAT92670 WP_215589296 2.00e-68 216 168 58.333 162 | AAT92670 WP_241139205 2.09e-68 216 168 57.738 163 | AAT92670 WP_215588168 2.09e-68 216 168 58.333 164 | AAT92670 WP_081143378 2.46e-68 216 168 58.929 165 | AAT92670 WP_079241112 3.13e-68 215 166 60.241 166 | AAT92670 WP_076597054 4.30e-68 215 169 59.172 167 | AAT92670 WP_120230529 4.35e-68 215 166 60.241 168 | AAT92670 WP_071865667 4.69e-68 214 156 64.744 169 | AAT92670 WP_215559710 5.07e-68 215 168 57.738 170 | AAT92670 WP_002203044 5.07e-68 215 168 58.333 171 | AAT92670 WP_002144233 5.35e-68 214 168 57.738 172 | AAT92670 WP_098287108 8.67e-68 214 168 58.333 173 | AAT92670 WP_213278628 8.76e-68 214 166 60.241 174 | AAT92670 WP_002187330 1.03e-67 214 168 57.738 175 | AAT92670 WP_152873926 1.04e-67 214 167 60.479 176 | AAT92670 WP_016093939 1.24e-67 214 168 57.738 177 | AAT92670 WP_013709724 1.33e-67 214 168 59.524 178 | AAT92670 WP_201053994 1.48e-67 213 168 57.143 179 | AAT92670 WP_216483989 1.69e-67 213 168 57.738 180 | AAT92670 WP_078422372 1.80e-67 213 168 57.738 181 | AAT92670 NUF97537 1.91e-67 213 168 58.929 182 | AAT92670 WP_163120899 1.97e-67 213 167 60.479 183 | AAT92670 WP_244012899 2.10e-67 213 167 60.479 184 | AAT92670 WP_227943935 2.67e-67 213 166 59.639 185 | AAT92670 QBA21333 2.76e-67 213 166 60.241 186 | AAT92670 WP_174788971 3.33e-67 213 168 58.929 187 | AAT92670 WP_130131624 3.40e-67 213 167 59.880 188 | AAT92670 WP_239045432 3.44e-67 213 167 60.479 189 | AAT92670 WP_223600316 3.76e-67 213 166 59.036 190 | AAT92670 WP_234624222 3.80e-67 213 167 59.880 191 | AAT92670 WP_130112315 4.01e-67 213 167 59.880 192 | AAT92670 WP_101668531 4.05e-67 213 167 57.485 193 | AAT92670 WP_130116989 4.62e-67 212 167 59.880 194 | AAT92670 WP_174791913 4.73e-67 212 168 58.333 195 | AAT92670 WP_096886786 5.57e-67 212 167 57.485 196 | AAT92670 WP_003340693 5.95e-67 212 167 57.485 197 | AAT92670 WP_087552928 6.28e-67 212 167 59.880 198 | AAT92670 WP_129036932 7.08e-67 212 166 59.036 199 | AAT92670 WP_068974833 7.48e-67 212 167 59.880 200 | AAT92670 WP_071680068 7.90e-67 212 168 57.143 201 | AAT92670 WP_201033505 8.35e-67 212 168 57.143 202 | AAT92670 WP_077319935 8.82e-67 211 168 55.952 203 | AAT92670 WP_202844057 9.11e-67 211 166 59.639 204 | AAT92670 WP_105703154 1.02e-66 211 166 59.036 205 | AAT92670 WP_070129311 1.05e-66 211 168 57.738 206 | AAT92670 WP_096734381 1.07e-66 212 167 59.880 207 | AAT92670 WP_115964082 1.09e-66 211 166 59.036 208 | AAT92670 WP_088030746 1.12e-66 211 168 57.143 209 | AAT92670 WP_130168913 1.17e-66 211 167 59.880 210 | AAT92670 WP_227900732 1.41e-66 211 168 57.143 211 | AAT92670 WP_105713699 1.59e-66 211 167 59.880 212 | AAT92670 WP_057876868 1.64e-66 211 168 56.548 213 | AAT92670 WP_181749346 1.70e-66 211 167 57.485 214 | AAT92670 WP_110009419 2.16e-66 211 166 59.036 215 | AAT92670 WP_124536318 2.19e-66 211 166 59.036 216 | AAT92670 WP_236850724 2.33e-66 211 166 59.036 217 | AAT92670 WP_004822309 2.36e-66 211 167 59.880 218 | AAT92670 WP_184552522 2.47e-66 211 166 61.446 219 | AAT92670 WP_209785239 2.72e-66 210 166 59.036 220 | AAT92670 WP_088308704 3.95e-66 210 168 57.143 221 | AAT92670 WP_089977464 4.12e-66 210 168 58.333 222 | AAT92670 WP_198175763 4.17e-66 210 168 57.738 223 | AAT92670 WP_152289620 4.91e-66 210 166 58.434 224 | AAT92670 WP_034615226 5.07e-66 210 167 59.880 225 | AAT92670 WP_126320462 5.30e-66 210 172 56.395 226 | AAT92670 WP_187948837 6.11e-66 209 168 59.524 227 | AAT92670 WP_045922684 6.18e-66 209 168 58.333 228 | AAT92670 WP_174790729 6.46e-66 209 168 58.333 229 | AAT92670 WP_185287214 7.28e-66 209 166 58.434 230 | AAT92670 WP_165006160 8.13e-66 209 168 60.119 231 | AAT92670 WP_002169656 8.22e-66 209 168 56.548 232 | AAT92670 WP_185507149 8.77e-66 209 172 55.814 233 | AAT92670 WP_220746435 9.68e-66 209 172 58.140 234 | AAT92670 WP_105683644 9.79e-66 209 166 58.434 235 | AAT92670 WP_003755718 1.07e-65 209 172 55.814 236 | AAT92670 WP_167029757 1.29e-65 209 166 59.036 237 | AAT92670 WP_010744365 1.33e-65 209 168 65.476 238 | AAT92670 WP_056512763 1.34e-65 209 167 58.683 239 | AAT92670 WP_003335899 1.44e-65 208 167 56.886 240 | AAT92670 EHB6449604 1.47e-65 208 168 57.738 241 | AAT92670 WP_142014514 1.57e-65 208 166 59.036 242 | AAT92670 WP_149216920 1.62e-65 208 168 58.333 243 | AAT92670 WP_022586250 1.73e-65 208 167 56.886 244 | AAT92670 NST56843 1.73e-65 208 166 58.434 245 | AAT92670 WP_010818106 1.85e-65 208 168 57.738 246 | AAT92670 WP_137904171 1.97e-65 208 166 59.639 247 | AAT92670 WP_042087352 2.43e-65 208 167 59.281 248 | AAT92670 WP_156248593 2.62e-65 208 168 57.143 249 | AAT92670 WP_223607470 2.86e-65 208 166 59.639 250 | AAT92670 WP_016627294 2.86e-65 207 166 58.434 251 | AAT92670 WP_163245235 2.89e-65 207 167 56.886 252 | AAT92670 WP_151957428 3.02e-65 208 167 59.880 253 | AAT92670 HCN47554 3.26e-65 207 166 57.831 254 | AAT92670 WP_086319892 3.37e-65 207 168 65.476 255 | AAT92670 WP_070786995 3.84e-65 207 168 57.143 256 | AAT92670 WP_000816114 3.93e-65 207 168 57.143 257 | # BLAST processed 1 queries 258 | -------------------------------------------------------------------------------- /example/input.fasta: -------------------------------------------------------------------------------- 1 | >AAT92670 2 | MREYTSKKELKEEIEKKYEKYDAEFETISESQKDEKVETVDRTPSENLSYQLGWVNLLLEWEAKEIAGYNVETPAPGYKWNNLGGLYQSFYKKYGIYSIKEQRAKLREAVNEVYKWISTLSDDELFQAGNRKWATTKAMWPVYKWIHINTVAPFTNFRGKIRKWKRLVPEEQRIKRRKI -------------------------------------------------------------------------------- /example/output.tsv: -------------------------------------------------------------------------------- 1 | Gene/Protein Alien index E value Donor id Donor taxonomy 2 | AAT92670 199.18 3.15e-87 WP_208929673 Bacteria/Firmicutes 3 | -------------------------------------------------------------------------------- /main/HGT_workflow_close.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | 4 | import sys 5 | import os 6 | import math 7 | import csv 8 | import warnings 9 | from Bio import BiopythonWarning 10 | from Bio import SeqIO 11 | from Bio import Entrez 12 | from ete3 import NCBITaxa 13 | 14 | 15 | def parse_NCBI(gene): 16 | with open("./blastp_files/%s.txt" % gene, "r") as filename : 17 | data = filename.readlines() 18 | for line in data : 19 | if line.strip("\n").endswith("found") : 20 | index = data.index(line) 21 | 22 | blast_results = data[index+1:-1] 23 | 24 | accession_number = list() 25 | accession_bitscore = dict() 26 | for blast in blast_results : 27 | accession = blast.strip("\n").split("\t")[1] 28 | accession_number.append(accession) 29 | accession_bitscore[accession] = float(blast.strip('\n').split("\t")[3]) 30 | return accession_number, accession_bitscore 31 | 32 | def getTaxid(accession): 33 | # Retrieving data in the GenBank using only the GenBank code accession in biopython 34 | # https://www.ncbi.nlm.nih.gov/books/NBK25497/table/chapter2.T._entrez_unique_identifiers_ui/?report=objectonly 35 | # https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.EFetch 36 | # https://biopython.org/DIST/docs/api/Bio.Entrez-module.html 37 | Entrez.email = "abcd@ncbi.org" 38 | 39 | # https://www.biostars.org/p/304175/ 40 | # get tax id using only the GenBank code accession in biopython 41 | # handle = Entrez.efetch(db='protein', id="NP_012706.1", rettype='gb') 42 | handle = Entrez.efetch(db='protein', id=accession, rettype='gb') 43 | record = SeqIO.read(handle,'genbank') 44 | # print(record.features[0].qualifiers) 45 | if record.features[0].qualifiers['db_xref'][0].split(":")[0] == 'taxon': 46 | taxid = record.features[0].qualifiers['db_xref'][0].split(":")[1] # the type is a string 47 | organism = record.features[0].qualifiers['organism'][0] 48 | # seq = record.seq 49 | # print(taxid,organism) 50 | 51 | return taxid 52 | 53 | def main(bitscore_parameter=100, HGTIndex=0.5, out_pct=0.8) : 54 | name = sys.argv[1:][0] 55 | if len(sys.argv) == 2 : 56 | pass 57 | else : 58 | bitscore_parameter = float(sys.argv[2].split("=")[1]) 59 | HGTIndex = float(sys.argv[3].split("=")[1]) 60 | out_pct = float(sys.argv[4].split("=")[1]) 61 | genes = list() 62 | geneSeq = dict() 63 | HGT = list() 64 | warnings.simplefilter('ignore', BiopythonWarning) 65 | 66 | with open(name, 'r') as handleGene : 67 | for record in SeqIO.parse(handleGene, "fasta") : 68 | gene = str(record.id) 69 | sequence = str(record.seq) 70 | geneSeq[gene] = sequence 71 | genes.append(gene) 72 | 73 | n=0 74 | for gene in genes : 75 | n += 1 76 | print("This is gene %d------------------" %(n)) 77 | print(gene) 78 | 79 | if os.path.exists("./blastp_files/%s.txt" % gene) : 80 | accession_number,accession_bitscore = parse_NCBI(gene) 81 | print('Yes, blast file already exists, nice!') 82 | else : 83 | # Need to install blast! 84 | with open('./%s.fasta' % gene, 'w') as outfile : 85 | outfile.write('>'+gene+'\n') 86 | outfile.write(geneSeq[gene]+'\n') 87 | if not os.path.exists("./blastp_files/") : 88 | os.system('mkdir ./blastp_files') 89 | myCmd = "blastp -db nr -remote -query ./%s.fasta -max_target_seqs 250 -task 'blastp-fast' -outfmt '7 qacc sacc evalue bitscore length pident' -out ./blastp_files/%s.txt" %(gene, gene) 90 | os.system(myCmd) 91 | os.remove('./%s.fasta' % gene) 92 | accession_number,accession_bitscore = parse_NCBI(gene) 93 | 94 | ncbi = NCBITaxa() 95 | recipient_accession = list() 96 | outgroup_accession = list() 97 | recipient_accession_bitscore = dict() 98 | outgroup_accession_bitscore = dict() 99 | recipient_species = list() 100 | outgroup_species = list() 101 | 102 | try : 103 | gene_taxid = getTaxid(gene) 104 | gene_lineage = ncbi.get_lineage(gene_taxid) 105 | gene_lineage2ranks = ncbi.get_rank(gene_lineage) 106 | gene_ranks2lineage = dict((rank, taxid) for (taxid, rank) in gene_lineage2ranks.items()) 107 | gene_taxonomy_alignment = gene_ranks2lineage 108 | gene_kingdom = gene_taxonomy_alignment['kingdom'] 109 | gene_subphylum = gene_taxonomy_alignment['subphylum'] 110 | # print(gene_kingdom) 111 | # print(type(gene_kingdom)) # 112 | # print(gene_subphylum) 113 | except : 114 | print('Attention: please check the gene accession id!') 115 | break 116 | 117 | for accession in accession_number[:200] : 118 | try : 119 | taxid = getTaxid(accession) 120 | print('Taxid by BLAST:', taxid) 121 | except : 122 | continue 123 | 124 | try : 125 | lineage = ncbi.get_lineage(taxid) 126 | lineage2ranks = ncbi.get_rank(lineage) 127 | ranks2lineage = dict((rank, taxid) for (taxid, rank) in lineage2ranks.items()) 128 | # print(ranks2lineage) 129 | 130 | taxid2name = ncbi.get_taxid_translator(lineage) 131 | taxonomy_alignment = ranks2lineage 132 | except : 133 | print('Warning: %s taxid not found!' % str(taxid)) 134 | continue 135 | 136 | try : 137 | if taxonomy_alignment['subphylum'] == gene_subphylum : 138 | recipient_accession.append(accession) 139 | recipient_species.append(taxonomy_alignment['species']) 140 | 141 | if taxonomy_alignment['kingdom'] == gene_kingdom and taxonomy_alignment['subphylum'] != gene_subphylum : 142 | outgroup_accession.append(accession) 143 | outgroup_species.append(taxonomy_alignment['species']) 144 | except : 145 | continue 146 | 147 | for accession_id in recipient_accession : 148 | recipient_accession_bitscore[accession_id] = accession_bitscore[accession_id] 149 | 150 | for accession_id in outgroup_accession : 151 | outgroup_accession_bitscore[accession_id] = accession_bitscore[accession_id] 152 | 153 | if recipient_accession_bitscore : 154 | max_recipient_organism_accession_key = max(recipient_accession_bitscore,key=recipient_accession_bitscore.get) 155 | max_recipient_organism_bitscore = recipient_accession_bitscore[max_recipient_organism_accession_key] 156 | 157 | if outgroup_accession_bitscore : 158 | max_outgroup_accession_key = max(outgroup_accession_bitscore,key=outgroup_accession_bitscore.get) 159 | max_outgroup_bitscore = outgroup_accession_bitscore[max_outgroup_accession_key] 160 | if max_outgroup_accession_key : 161 | max_taxid = getTaxid(max_outgroup_accession_key) 162 | max_lineage = ncbi.get_lineage(max_taxid) 163 | max_lineage2ranks = ncbi.get_rank(max_lineage) 164 | max_ranks2lineage = dict((rank, taxid) for (taxid, rank) in max_lineage2ranks.items()) 165 | try : 166 | max_taxid2name = ncbi.get_taxid_translator([max_ranks2lineage['kingdom'], max_ranks2lineage['phylum'], max_ranks2lineage['subphylum'], max_ranks2lineage['species']]) 167 | except : 168 | continue 169 | 170 | # print(gene) 171 | # print(max_recipient_organism_bitscore) 172 | # print(max_outgroup_bitscore) 173 | # print(max_taxid2name) 174 | 175 | if recipient_species : 176 | recipient_species_number = len(set(recipient_species)) 177 | if outgroup_species : 178 | outgroup_species_number = len(set(outgroup_species)) 179 | 180 | HGT_index = format(max_outgroup_bitscore/max_recipient_organism_bitscore, '.4f') 181 | Outg_pct = format(outgroup_species_number/(outgroup_species_number+recipient_species_number), '.4f') 182 | 183 | print('HGT index: %s' % str(HGT_index)) 184 | print('Out_pct: %s' % str(Outg_pct)) 185 | if max_outgroup_bitscore>=bitscore_parameter and float(HGT_index)>=HGTIndex and float(Outg_pct)>=out_pct : 186 | print('This is a HGT event') 187 | taxonomy = max_taxid2name[max_ranks2lineage['kingdom']] + '/' + max_taxid2name[max_ranks2lineage['subphylum']] 188 | item = [gene, max_outgroup_bitscore, Outg_pct, HGT_index, taxonomy] 189 | HGT.append(item) 190 | else : 191 | print('This is not a HGT event') 192 | item = [gene, max_outgroup_bitscore, Outg_pct, HGT_index, 'No'] 193 | HGT.append(item) 194 | 195 | outfile = open("./output_close_HGT.tsv", "wt") 196 | tsv_writer = csv.writer(outfile, delimiter="\t") 197 | column = ['Gene/Protein', 'Bitscore', 'Out_pct', 'HGT index', 'Donor taxonomy'] 198 | tsv_writer.writerow(column) 199 | for HGT_info in HGT : 200 | tsv_writer.writerow(HGT_info) 201 | outfile.close() 202 | 203 | 204 | if __name__== "__main__": 205 | main() 206 | -------------------------------------------------------------------------------- /main/HGT_workflow_distant.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | 4 | # Step 1 : BLAST hits were parsed to retrieve associated taxonomic information 5 | # using the NCBI's taxonomy database. 6 | 7 | # Step 2: Calculate Alien Index (AI) values and out_pct based on the above information. 8 | 9 | # Step 3: Output the horizontal gene transfer (HGT) high-throughput identification results. 10 | 11 | import sys 12 | import os 13 | import math 14 | import csv 15 | import warnings 16 | from Bio import BiopythonWarning 17 | from Bio import SeqIO 18 | from Bio import Entrez 19 | from ete3 import NCBITaxa 20 | 21 | 22 | def parse_NCBI(gene): 23 | with open("./blastp_files/%s.txt" % gene, "r") as filename : 24 | data = filename.readlines() 25 | for line in data : 26 | if line.strip("\n").endswith("found") : 27 | index = data.index(line) 28 | 29 | blast_results = data[index+1:-1] 30 | 31 | accession_number = list() 32 | evalue = dict() 33 | for blast in blast_results : 34 | accession = blast.strip("\n").split("\t")[1] 35 | accession_number.append(accession) 36 | evalue[accession] = float(blast.strip('\n').split("\t")[2]) 37 | return accession_number, evalue 38 | 39 | def getTaxid(accession): 40 | # Retrieving data in the GenBank using only the GenBank code accession in biopython 41 | # https://www.ncbi.nlm.nih.gov/books/NBK25497/table/chapter2.T._entrez_unique_identifiers_ui/?report=objectonly 42 | # https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.EFetch 43 | # https://biopython.org/DIST/docs/api/Bio.Entrez-module.html 44 | Entrez.email = "abcd@ncbi.org" 45 | 46 | # https://www.biostars.org/p/304175/ 47 | # get tax id using only the GenBank code accession in biopython 48 | # handle = Entrez.efetch(db='protein', id="NP_012706.1", rettype='gb') 49 | handle = Entrez.efetch(db='protein', id=accession, rettype='gb') 50 | record = SeqIO.read(handle,'genbank') 51 | # print(record.features[0].qualifiers) 52 | if record.features[0].qualifiers['db_xref'][0].split(":")[0] == 'taxon': 53 | taxid = record.features[0].qualifiers['db_xref'][0].split(":")[1] # the type is a string 54 | organism = record.features[0].qualifiers['organism'][0] 55 | # seq = record.seq 56 | # print(taxid,organism) 57 | 58 | return taxid 59 | 60 | def main(AI=45, out_pct=0.90) : 61 | name = sys.argv[1:][0] 62 | if len(sys.argv) == 2 : 63 | pass 64 | else : 65 | AI = float(sys.argv[2].split("=")[1]) 66 | out_pct = float(sys.argv[3].split("=")[1]) 67 | genes = list() 68 | geneSeq = dict() 69 | HGT = list() 70 | warnings.simplefilter('ignore', BiopythonWarning) 71 | 72 | with open(name, 'r') as handleGene : 73 | for record in SeqIO.parse(handleGene, "fasta") : 74 | gene = str(record.id) 75 | sequence = str(record.seq) 76 | geneSeq[gene] = sequence 77 | genes.append(gene) 78 | 79 | n=0 80 | for gene in genes : 81 | n += 1 82 | print("This is gene %d------------------" %(n)) 83 | print(gene) 84 | 85 | if os.path.exists("./blastp_files/%s.txt" % gene) : 86 | accession_number,evalue = parse_NCBI(gene) 87 | print('Yes, blast file already exists, nice!') 88 | else : 89 | # Need to install blast! 90 | with open('./%s.fasta' % gene, 'w') as outfile : 91 | outfile.write('>'+gene+'\n') 92 | outfile.write(geneSeq[gene]+'\n') 93 | if not os.path.exists("./blastp_files/") : 94 | os.system('mkdir ./blastp_files') 95 | myCmd = "blastp -db nr -remote -query ./%s.fasta -max_target_seqs 250 -task 'blastp-fast' -outfmt '7 qacc sacc evalue bitscore length pident' -out ./blastp_files/%s.txt" %(gene, gene) 96 | os.system(myCmd) 97 | os.remove('./%s.fasta' % gene) 98 | accession_number,evalue = parse_NCBI(gene) 99 | 100 | ncbi = NCBITaxa() 101 | outgroup = list() 102 | ingroup = list() 103 | outgroup_species = list() 104 | ingroup_species = list() 105 | outgroup_dict = dict() 106 | ingroup_dict = dict() 107 | e_minus = 1e-200 108 | 109 | try : 110 | gene_taxid = getTaxid(gene) 111 | gene_lineage = ncbi.get_lineage(gene_taxid) 112 | gene_lineage2ranks = ncbi.get_rank(gene_lineage) 113 | gene_ranks2lineage = dict((rank, taxid) for (taxid, rank) in gene_lineage2ranks.items()) 114 | gene_taxonomy_alignment = gene_ranks2lineage 115 | gene_kingdom = gene_taxonomy_alignment['kingdom'] 116 | gene_subphylum = gene_taxonomy_alignment['subphylum'] 117 | # print(gene_kingdom) 118 | # print(type(gene_kingdom)) # 119 | # print(gene_subphylum) 120 | except : 121 | print('Attention: please check the gene accession id!') 122 | break 123 | 124 | for accession in accession_number[:200] : 125 | try : 126 | # print(accession) 127 | taxid = getTaxid(accession) 128 | print('Taxid by BLAST:', taxid) 129 | except : 130 | continue 131 | 132 | try : 133 | lineage = ncbi.get_lineage(taxid) 134 | lineage2ranks = ncbi.get_rank(lineage) 135 | ranks2lineage = dict((rank, taxid) for (taxid, rank) in lineage2ranks.items()) 136 | 137 | taxonomy_alignment = ranks2lineage 138 | LINNAEUS_FILTER = ["subphylum","kingdom","superkingdom"] 139 | except : 140 | print('Warning: %s taxid not found!' % str(taxid)) 141 | continue 142 | 143 | try : 144 | if taxonomy_alignment['kingdom'] != gene_kingdom : 145 | outgroup.append(accession) 146 | outgroup_species.append(taxonomy_alignment['species']) 147 | 148 | if taxonomy_alignment['kingdom'] == gene_kingdom and taxonomy_alignment['subphylum'] != gene_subphylum : 149 | ingroup.append(accession) 150 | ingroup_species.append(taxonomy_alignment['species']) 151 | 152 | except : 153 | outgroup.append(accession) 154 | try : 155 | outgroup_species.append(taxonomy_alignment['species']) 156 | except : 157 | continue 158 | 159 | for accession_id in outgroup : 160 | outgroup_dict[accession_id] = evalue[accession_id] 161 | 162 | for accession_id in ingroup : 163 | ingroup_dict[accession_id] = evalue[accession_id] 164 | 165 | if outgroup_dict : 166 | min_outgroup_key = min(outgroup_dict,key=outgroup_dict.get) 167 | min_outgroup_evalue = outgroup_dict[min_outgroup_key] 168 | else : 169 | min_outgroup_evalue = 1 170 | 171 | if ingroup_dict : 172 | min_ingroup_key = min(ingroup_dict,key=ingroup_dict.get) 173 | min_ingroup_evalue = ingroup_dict[min_ingroup_key] 174 | else : 175 | min_ingroup_evalue = 1 176 | 177 | alienindex = format(math.log(min_ingroup_evalue+e_minus, math.e)-math.log(min_outgroup_evalue+e_minus, math.e), '.2f') 178 | try : 179 | outg_pct = format(len(set(outgroup_species))/(len(set(outgroup_species))+len(set(ingroup_species))), '.4f') 180 | except : 181 | continue 182 | 183 | print('Alien index: %s' % str(alienindex)) 184 | 185 | if float(alienindex) >= AI and float(outg_pct) >= out_pct : 186 | print('This is a HGT event') 187 | print('Accession_id: %s' % min_outgroup_key) 188 | print('Evalue: %s' % evalue[min_outgroup_key]) 189 | taxid= getTaxid(min_outgroup_key) 190 | Evalue = evalue[min_outgroup_key] 191 | lineage = ncbi.get_lineage(taxid) 192 | lineage2ranks = ncbi.get_rank(lineage) 193 | ranks2lineage = dict((rank, taxid) for (taxid, rank) in lineage2ranks.items()) 194 | # print(lineage) 195 | # print(ranks2lineage) 196 | taxid2name = ncbi.get_taxid_translator([ranks2lineage['phylum'], ranks2lineage['class'], ranks2lineage['superkingdom']]) 197 | print(taxid2name[ranks2lineage['phylum']]) 198 | 199 | superkingdom = taxid2name[ranks2lineage['superkingdom']] 200 | phylum = taxid2name[ranks2lineage['phylum']] 201 | hgt = 'Yes' 202 | taxonomy = superkingdom + '/' + phylum 203 | 204 | item = [gene, alienindex, Evalue, min_outgroup_key, taxonomy] 205 | HGT.append(item) 206 | 207 | else: 208 | # print('This is not a HGT event') 209 | hgt = 'No' 210 | HGT.append([gene,alienindex,'No', 'No', 'No']) 211 | 212 | outfile = open("./output_distant_HGT.tsv", "wt") 213 | tsv_writer = csv.writer(outfile, delimiter="\t") 214 | column = ['Gene/Protein', 'Alien index', 'E value', 'Donor id', 'Donor taxonomy'] 215 | tsv_writer.writerow(column) 216 | for HGT_info in HGT : 217 | tsv_writer.writerow(HGT_info) 218 | outfile.close() 219 | 220 | 221 | if __name__== "__main__": 222 | main() 223 | -------------------------------------------------------------------------------- /main/blastp.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | # coding: utf-8 3 | 4 | ### Usage: python blastp.py input.fasta 5 | 6 | import os 7 | import sys 8 | import time 9 | from Bio import SeqIO 10 | 11 | 12 | def main() : 13 | start_time = time.time() 14 | name = sys.argv[1:][0] 15 | genes = list() 16 | geneSeq = dict() 17 | with open(name, 'r') as handleGene : 18 | for record in SeqIO.parse(handleGene, "fasta") : 19 | gene = str(record.id) 20 | sequence = str(record.seq) 21 | geneSeq[gene] = sequence 22 | genes.append(gene) 23 | 24 | for gene in genes : 25 | print('This is gene:', gene) 26 | with open('./%s.fasta' % gene, 'w') as outfile : 27 | outfile.write('>'+gene+'\n') 28 | outfile.write(geneSeq[gene]+'\n') 29 | if not os.path.exists("./blastp_files/") : 30 | os.system('mkdir ./blastp_files') 31 | # Execute shell command line in Python using the os.system function 32 | myCmd = "blastp -db nr -remote -query ./%s.fasta -max_target_seqs 250 -task 'blastp-fast' -outfmt '7 qacc sacc evalue bitscore length pident' -out ./blastp_files/%s.txt" % (gene, gene) 33 | os.system(myCmd) 34 | os.remove('./%s.fasta' % gene) 35 | 36 | elapsed_time = (time.time() - start_time) 37 | print("Finished------------------") 38 | print("Running the Blast process: %.4fs" %(elapsed_time)) 39 | 40 | 41 | if __name__ == "__main__" : 42 | main() -------------------------------------------------------------------------------- /phylogenetics/input/YOL164W.fasta: -------------------------------------------------------------------------------- 1 | >YOL164W 2 | MIGAFKRNRGSSQSFAKECQPSTLKANLEVAKELPFSDRRDFEDATQGYIGSLSDEQIIGPDGGVVWCMKSYGFLEPETPANTVNPSLWRQAQLNAIHGLFKITDNVYQVRGLDISNMTIIEGNTSLIIIDTLFTTETAQESLKLYYRHRPQKPVRTVIYTHSHSDHYGGVKGIVKEADVKSGEVQIIAPVGFMESVVAENILAGNAMHRRSQYQFGMLLSPSVKGHVDCGIGKAASHGTVTLIAPTIIIEEPVEERTIDGVDFVFQLAPGSEAPSEMLIYMPQQRVLNMAEDVTHHMHNLYALRGVEVRDGNQWAKYIDAARVAFGSKTDVLIAQHHWPTTGQMRINELLKKQRDMYKFIHDQTLRLLNQGYTSRDIAETLRMPSSLEQEWSTRGYYGTLSHNVKAVYQKYLGWYDANPANLNPLPPVAYAKKAVEYMGGADAVLARAYKDFQKGEFRWVASVVNQLVFADPNNHQARELCADALEQLGYQAEASTWRNAYLVGAMELRQGVPKRRSTGKRNNIAVLNNEMFFDFLAVRLNATKAEGKIIVSNWCFINSNERFVITLENCALTYIQGWQTDADATITLKRTTFEALLANEITMVDFLRSKEVEIEGNRLRIEELLKLFDDFDQSFPVVEPMGGST -------------------------------------------------------------------------------- /phylogenetics/input/YOL164W.txt: 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2 | -------------------------------------------------------------------------------- /phylogenetics/intermediate/YOL164W.model.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SysBioChalmers/HGTphyloDetect/d4ff8c5dbdaf3b200e45dd57962dfb11776f43d7/phylogenetics/intermediate/YOL164W.model.gz -------------------------------------------------------------------------------- /phylogenetics/intermediate/YOL164W.treefile: -------------------------------------------------------------------------------- 1 | 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2 | -------------------------------------------------------------------------------- /phylogenetics/intermediate/YOL164W_midpoint.tree: -------------------------------------------------------------------------------- 1 | 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299.1-Pseudomonas_yangmingensis_Proteobacteria:0.2420063289)100:0.0524687662,(WP_108104178.1-Pseudomonas_mangrovi_Proteobacteria:0.1439069358,WP_017937932.1-Pseudomonas_thermotolerans_Proteobacteria:0.146369063)100:0.1327273239)100:0.1002360458,(((MAB24835.1-Pseudomonadales_bacterium_Proteobacteria:0.0190952035,WP_182248855.1-Pseudomonas_sp._5Ae-yellow_Proteobacteria:0.0166167239)100:0.1055173487,WP_083726556.1-Pseudomonas_pachastrellae_Proteobacteria:0.1003605968)100:0.1239403048,PKM29133.1-Gammaproteobacteria_bacterium_HGW-Gammaproteobacteria-11_Proteobacteria:0.1694559589)100:0.2031845527)100:0.1243949737,(WP_057659955.1-Pseudoxanthomonas_dokdonensis_Proteobacteria:0.2504443031,WP_077034059.1-Pelomonas_sp._KK5_Proteobacteria:0.290739419)100:0.089768806)99:0.0853947488,WP_151072822.1-Cupriavidus_oxalaticus_Proteobacteria:0.4488612335)77:0.038526075)94:0.0539909156,(WP_163259863.1-Caulobacter_sp._17J65-9_Proteobacteria:0.0359889329,WP_187448402.1-Caulobacter_sp._17J80-11_Proteobacteria:0.0560016928)100:0.4106137649)88:0.0483121127,WP_119419155.1-Rhodospirillaceae_bacterium_SYSU_D60015_Proteobacteria:0.5054381725)86:0.0421557676,((((((((WP_071072555.1-Cupriavidus_malaysiensis_Proteobacteria:2.4404e-06,WP_071038742.1-Cupriavidus_sp._USMAA2-4_Proteobacteria:0.0018049159)100:0.001807578,WP_071018820.1-Cupriavidus_sp._USMAHM13_Proteobacteria:2.614e-06)100:0.1086010247,WP_066731373.1-Cupriavidus_sp._D384_Proteobacteria:0.1244621502)99:0.0424841488,WP_116319445.1-Cupriavidus_sp._P-10_Proteobacteria:0.1027967862)100:0.0837115318,((((WP_025586346.1-Cupriavidus_taiwanensis_Proteobacteria:2.5529e-06,WP_116388232.1-Cupriavidus_taiwanensis_Proteobacteria:0.0036557088)100:0.0316799251,WP_116377884.1-Cupriavidus_taiwanensis_Proteobacteria:0.0302374199)99:0.0150639375,WP_062795892.1-Cupriavidus_nantongensis_Proteobacteria:0.0148732888)100:0.0624658383,WP_116320039.1-Cupriavidus_sp._P-10_Proteobacteria:0.0839847297)100:0.1462076246)100:0.1899307557,((WP_061999991.1-Burkholderia_sp._PAMC_28687_Proteobacteria:0.008725495,WP_062170184.1-Burkholderia_sp._PAMC_26561_Proteobacteria:0.0025040922)100:0.0260404307,WP_081889718.1-Caballeronia_sordidicola_Proteobacteria:0.0277042159)100:0.4391644299)99:0.0726843554,(PZW26129.1-Thermosporothrix_hazakensis_Chloroflexi:2.5529e-06,WP_111324427.1-Thermosporothrix_hazakensis_Chloroflexi:2.5529e-06)100:0.4278181864)100:0.0982174834,(((WP_183035411.1-Cupriavidus_sp._UME77_Proteobacteria:2.5529e-06,MBB1634382.1-Cupriavidus_sp._UME77_Proteobacteria:2.4494e-06)100:0.0121357022,WP_150987011.1-Cupriavidus_basilensis_Proteobacteria:0.006166255)100:0.1044846317,WP_092594144.1-Ralstonia_sp._25mfcol4.1_Proteobacteria:0.1855041654)100:0.3460007168)98:0.0688633008)83:0.0191555351); 2 | -------------------------------------------------------------------------------- /phylogenetics/output/YOL164W_circle_tree.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SysBioChalmers/HGTphyloDetect/d4ff8c5dbdaf3b200e45dd57962dfb11776f43d7/phylogenetics/output/YOL164W_circle_tree.pdf -------------------------------------------------------------------------------- /phylogenetics/output/YOL164W_midpoint-color.txt: -------------------------------------------------------------------------------- 1 | DATASET_COLORSTRIP 2 | #In colored strip datasets, each ID is associated to a color box/strip and can have an optional label. Color can be specified in hexadecimal, RGB or RGBA notation. When using RGB or RGBA notation, you cannot use COMMA as the dataset separator 3 | 4 | #lines starting with a hash are comments and ignored during parsing 5 | 6 | #=================================================================# 7 | # MANDATORY SETTINGS # 8 | #=================================================================# 9 | #select the separator which is used to delimit the data below (TAB,SPACE or COMMA).This separator must be used throughout this file (except in the SEPARATOR line, which uses space). 10 | 11 | #SEPARATOR TAB 12 | #SEPARATOR COMMA 13 | SEPARATOR SPACE 14 | 15 | #label is used in the legend table (can be changed later) 16 | DATASET_LABEL Taxonomy 17 | 18 | #dataset color (can be changed later) 19 | COLOR #000000 20 | 21 | #=================================================================# 22 | # OPTIONAL SETTINGS # 23 | #=================================================================# 24 | 25 | #If COLOR_BRANCHES is set to 1, branches of the tree will be colored according to the colors of the strips above the leaves. 26 | #When all children of a node have the same color, it will be colored the same, ie. the color will propagate inwards towards the root. 27 | COLOR_BRANCHES 1 28 | 29 | #each dataset can have a legend, which is defined below 30 | #for each row in the legend, there should be one shape, color and label 31 | #shape should be a number between 1 and 5: 32 | #1: square 33 | #2: circle 34 | #3: star 35 | #4: right pointing triangle 36 | #5: left pointing triangle 37 | 38 | LEGEND_TITLE Taxonomy 39 | LEGEND_SHAPES 1 1 1 1 40 | LEGEND_COLORS #e31a1c #28b1aa #2D33E7 #969696 41 | LEGEND_LABELS Saccharomycotina Fungi Bacteria Other 42 | # LEGEND_SHAPES 1 1 1 1 1 1 1 1 1 1 1 1 43 | # LEGEND_COLORS #e31a1c #fb9a99 #ff7f00 #28b1aa #b15928 #6a3d9a #cab2d6 #1f78b4 #a6cee3 #33a02c #ffff99 #969696 44 | # LEGEND_LABELS Leotiomycetes Sordariomycetes Eurotiomycetes Dothideomycetes other_Pezizomycotina other_Ascomycota other_Fungi other_Opisthokonta other_Eukaryota Bacteria Archaea Viruses 45 | 46 | #=================================================================# 47 | # all other optional settings can be set or changed later # 48 | # in the web interface (under 'Datasets' tab) # 49 | #=================================================================# 50 | 51 | #width of the colored strip 52 | STRIP_WIDTH 35 53 | 54 | #left margin, used to increase/decrease the spacing to the next dataset. Can be negative, causing datasets to overlap. 55 | #MARGIN 5 56 | 57 | #border width; if set above 0, a border of specified width (in pixels) will be drawn around the color strip 58 | #BORDER_WIDTH 2 59 | 60 | #border color; used when BORDER_WIDTH is above 0 61 | #BORDER_COLOR #969696 62 | 63 | #always show internal values; if set, values associated to internal nodes will be displayed even if these nodes are not collapsed. It could cause overlapping in the dataset display. 64 | #SHOW_INTERNAL 1 65 | 66 | #Internal tree nodes can be specified using IDs directly, or using the 'last common ancestor' method described in iTOL help pages 67 | 68 | #=================================================================# 69 | # Actual data follows after the DATA keyword # 70 | #=================================================================# 71 | DATA 72 | 73 | #Examples: 74 | #assign a red colored strip to leaf 9606, with label 'Human' (label is displayed in the mouseover popups) 75 | #9606 #ff0000 Human 76 | 77 | #assign a green, semi-transparent (alpha 0.5) strip to an internal node, without any label. If 'Show internal values' is set to 'No', this will only be displayed if the node is collapsed. 78 | #9606|5664 rgba(0,255,0,0.5) 79 | WP_061021165.1-Bradyrhizobium_sp._CCH5-F6_Proteobacteria #2D33E7 Bacteria 80 | GAJ33771.1-Bradyrhizobium_sp._DOA9_Proteobacteria #2D33E7 Bacteria 81 | WP_035674808.1-Bradyrhizobium_liaoningense_Proteobacteria #2D33E7 Bacteria 82 | WP_028179375.1-Bradyrhizobium_japonicum_Proteobacteria #2D33E7 Bacteria 83 | WP_061878281.1-Bradyrhizobium_liaoningense_Proteobacteria #2D33E7 Bacteria 84 | WP_158669522.1-Bradyrhizobium_guangdongense_Proteobacteria #2D33E7 Bacteria 85 | WP_035715237.1-Bradyrhizobium_sp._CCBAU_43298_Proteobacteria #2D33E7 Bacteria 86 | WP_100175831.1-Bradyrhizobium_nitroreducens_Proteobacteria #2D33E7 Bacteria 87 | WP_142250055.1-Bradyrhizobium_sp._UNPF46_Proteobacteria #2D33E7 Bacteria 88 | RTM13090.1-Bradyrhizobiaceae_bacterium_Proteobacteria #2D33E7 Bacteria 89 | WP_128939625.1-Bradyrhizobium_zhanjiangense_Proteobacteria #2D33E7 Bacteria 90 | WP_128955323.1-Bradyrhizobium_zhanjiangense_Proteobacteria #2D33E7 Bacteria 91 | WP_183258594.1-Bradyrhizobium_sp._CIR48_Proteobacteria #2D33E7 Bacteria 92 | WP_183231495.1-Bradyrhizobium_sp._SBR1B_Proteobacteria #2D33E7 Bacteria 93 | WP_092259760.1-Bradyrhizobium_sp._Rc3b_Proteobacteria #2D33E7 Bacteria 94 | WP_018322728.1-Bradyrhizobium_sp._WSM2793_Proteobacteria #2D33E7 Bacteria 95 | WP_183237909.1-Bradyrhizobium_sp._CIR18_Proteobacteria #2D33E7 Bacteria 96 | WP_152103320.1-Bradyrhizobium_sp._TM102_Proteobacteria #2D33E7 Bacteria 97 | WP_091965654.1-Bradyrhizobium_shewense_Proteobacteria #2D33E7 Bacteria 98 | WP_104462928.1-Bradyrhizobium_sp._AC87j1_Proteobacteria #2D33E7 Bacteria 99 | WP_135167913.1-Bradyrhizobium_frederickii_Proteobacteria #2D33E7 Bacteria 100 | WP_126256569.1-Bradyrhizobium_sp._LVM_105_Proteobacteria #2D33E7 Bacteria 101 | WP_135162274.1-Bradyrhizobium_frederickii_Proteobacteria #2D33E7 Bacteria 102 | WP_028135100.1-Bradyrhizobium_japonicum_Proteobacteria #2D33E7 Bacteria 103 | WP_151642895.1-Bradyrhizobium_betae_Proteobacteria #2D33E7 Bacteria 104 | WP_015687718.1-Bradyrhizobium_cosmicum_Proteobacteria #2D33E7 Bacteria 105 | WP_145664020.1-Bradyrhizobium_stylosanthis_Proteobacteria #2D33E7 Bacteria 106 | WP_063691485.1-Bradyrhizobium_stylosanthis_Proteobacteria #2D33E7 Bacteria 107 | WP_007594037.1-Bradyrhizobium_sp._WSM1253_Proteobacteria #2D33E7 Bacteria 108 | WP_027516639.1-Bradyrhizobium_sp._WSM1417_Proteobacteria #2D33E7 Bacteria 109 | WP_027568791.1-Bradyrhizobium_sp._URHA0013_Proteobacteria #2D33E7 Bacteria 110 | WP_085395770.1-Bradyrhizobium_canariense_Proteobacteria #2D33E7 Bacteria 111 | WP_085352090.1-Bradyrhizobium_canariense_Proteobacteria #2D33E7 Bacteria 112 | WP_063993269.1-Bradyrhizobium_sp._Proteobacteria #2D33E7 Bacteria 113 | WP_018457146.1-Bradyrhizobium_sp._WSM4349_Proteobacteria #2D33E7 Bacteria 114 | WP_106942729.1-Bradyrhizobium_sp._MOS002_Proteobacteria #2D33E7 Bacteria 115 | WP_011085536.1-Bradyrhizobium_diazoefficiens_Proteobacteria #2D33E7 Bacteria 116 | WP_038965832.1-Bradyrhizobium_diazoefficiens_Proteobacteria #2D33E7 Bacteria 117 | WP_182869822.1-Bradyrhizobium_diazoefficiens_Proteobacteria #2D33E7 Bacteria 118 | QHP72204.1-Bradyrhizobium_sp._LCT2_Proteobacteria #2D33E7 Bacteria 119 | WP_161535945.1-Bradyrhizobium_sp._LCT2_Proteobacteria #2D33E7 Bacteria 120 | WP_060912981.1-Bradyrhizobium_diazoefficiens_Proteobacteria #2D33E7 Bacteria 121 | BAR59885.1-Bradyrhizobium_diazoefficiens_Proteobacteria #2D33E7 Bacteria 122 | KGJ68294.1-Bradyrhizobium_diazoefficiens_Proteobacteria #2D33E7 Bacteria 123 | WP_028174753.1-Bradyrhizobium_diazoefficiens_Proteobacteria #2D33E7 Bacteria 124 | WP_148744709.1-Bradyrhizobium_hipponense_Proteobacteria #2D33E7 Bacteria 125 | WP_128961978.1-Bradyrhizobium_guangzhouense_Proteobacteria #2D33E7 Bacteria 126 | WP_128953098.1-Bradyrhizobium_guangzhouense_Proteobacteria #2D33E7 Bacteria 127 | WP_057753835.1-Bradyrhizobium_manausense_Proteobacteria #2D33E7 Bacteria 128 | MBC9878718.1-Bradyrhizobium_campsiandrae_Proteobacteria #2D33E7 Bacteria 129 | WP_092295676.1-Bradyrhizobium_sp._Ghvi_Proteobacteria #2D33E7 Bacteria 130 | WP_128967591.1-Bradyrhizobium_guangdongense_Proteobacteria #2D33E7 Bacteria 131 | WP_027528936.1-Bradyrhizobium_sp._WSM3983_Proteobacteria #2D33E7 Bacteria 132 | WP_148775460.1-Bradyrhizobium_rifense_Proteobacteria #2D33E7 Bacteria 133 | WP_148749019.1-Bradyrhizobium_cytisi_Proteobacteria #2D33E7 Bacteria 134 | WP_008138438.1-Bradyrhizobium_sp._YR681_Proteobacteria #2D33E7 Bacteria 135 | WP_129271949.1-Bradyrhizobium_betae_Proteobacteria #2D33E7 Bacteria 136 | WP_092029312.1-Bradyrhizobium_sp._OK095_Proteobacteria #2D33E7 Bacteria 137 | WP_092214970.1-Bradyrhizobium_arachidis_Proteobacteria #2D33E7 Bacteria 138 | WP_084803943.1-Bradyrhizobium_sp._NAS80.1_Proteobacteria #2D33E7 Bacteria 139 | WP_074117602.1-Bradyrhizobium_sp._AS23.2_Proteobacteria #2D33E7 Bacteria 140 | WP_071915975.1-Bradyrhizobium_japonicum_Proteobacteria #2D33E7 Bacteria 141 | WP_063984832.1-Bradyrhizobium_sp._Proteobacteria #2D33E7 Bacteria 142 | WP_166089364.1-Bradyrhizobium_sp._1S5_Proteobacteria #2D33E7 Bacteria 143 | WP_166350707.1-Bradyrhizobium_sp._323S2_Proteobacteria #2D33E7 Bacteria 144 | WP_038931390.1-Bradyrhizobium_japonicum_Proteobacteria #2D33E7 Bacteria 145 | WP_039157276.1-Bradyrhizobium_japonicum_Proteobacteria #2D33E7 Bacteria 146 | WP_028156991.1-Bradyrhizobium_japonicum_Proteobacteria #2D33E7 Bacteria 147 | WP_041955910.1-Bradyrhizobium_japonicum_Proteobacteria #2D33E7 Bacteria 148 | WP_063680948.1-Bradyrhizobium_neotropicale_Proteobacteria #2D33E7 Bacteria 149 | WP_027550185.1-Bradyrhizobium_sp._Cp5.3_Proteobacteria #2D33E7 Bacteria 150 | WP_063705824.1-Bradyrhizobium_centrolobii_Proteobacteria #2D33E7 Bacteria 151 | WP_027526029.1-Bradyrhizobium_sp._Ec3.3_Proteobacteria #2D33E7 Bacteria 152 | WP_140980779.1-Bradyrhizobium_guangdongense_Proteobacteria #2D33E7 Bacteria 153 | WP_028347371.1-Bradyrhizobium_murdochi_Proteobacteria #2D33E7 Bacteria 154 | WP_057843198.1-Bradyrhizobium_retamae_Proteobacteria #2D33E7 Bacteria 155 | WP_065748757.1-Bradyrhizobium_sp._LMTR_3_Proteobacteria #2D33E7 Bacteria 156 | WP_171577827.1-Bradyrhizobium_australiense_Proteobacteria #2D33E7 Bacteria 157 | WP_065730813.1-Bradyrhizobium_icense_Proteobacteria #2D33E7 Bacteria 158 | WP_065756637.1-Bradyrhizobium_paxllaeri_Proteobacteria #2D33E7 Bacteria 159 | WP_057859318.1-Bradyrhizobium_lablabi_Proteobacteria #2D33E7 Bacteria 160 | WP_161852777.1-Bradyrhizobium_sp._CCBAU_051011_Proteobacteria #2D33E7 Bacteria 161 | WP_057833547.1-Bradyrhizobium_jicamae_Proteobacteria #2D33E7 Bacteria 162 | WP_108514566.1-Bradyrhizobium_algeriense_Proteobacteria #2D33E7 Bacteria 163 | WP_057900995.1-Bradyrhizobium_valentinum_Proteobacteria #2D33E7 Bacteria 164 | WP_057851642.1-Bradyrhizobium_valentinum_Proteobacteria #2D33E7 Bacteria 165 | WP_092515866.1-Afipia_sp._GAS231_Proteobacteria #2D33E7 Bacteria 166 | SDP08864.1-Afipia_sp._GAS231_Proteobacteria #2D33E7 Bacteria 167 | WP_155805222.1-Bradyrhizobium_sp._URHA0002_Proteobacteria #2D33E7 Bacteria 168 | WP_079586579.1-Bradyrhizobium_lablabi_Proteobacteria #2D33E7 Bacteria 169 | TKW79521.1-Bradyrhizobium_icense_Proteobacteria #2D33E7 Bacteria 170 | WP_100419024.1-Bradyrhizobium_lablabi_Proteobacteria #2D33E7 Bacteria 171 | WP_143206068.1-Bradyrhizobium_erythrophlei_Proteobacteria #2D33E7 Bacteria 172 | MBA2399293.1-Bradyrhizobium_sp._Proteobacteria #2D33E7 Bacteria 173 | MBA4036164.1-Bradyrhizobium_sp._Proteobacteria #2D33E7 Bacteria 174 | WP_069276965.1-Bradyrhizobium_elkanii_Proteobacteria #2D33E7 Bacteria 175 | WP_130580234.1-Bradyrhizobium_sp._Leo170_Proteobacteria #2D33E7 Bacteria 176 | WP_130222300.1-Bradyrhizobium_sp._Leo121_Proteobacteria #2D33E7 Bacteria 177 | WP_100380578.1-Afipia_broomeae_Proteobacteria #2D33E7 Bacteria 178 | WP_146687675.1-Bradyrhizobium_canariense_Proteobacteria #2D33E7 Bacteria 179 | TMK06221.1-Alphaproteobacteria_bacterium_Proteobacteria #2D33E7 Bacteria 180 | THD60673.1-Bradyrhizobium_sp._Proteobacteria #2D33E7 Bacteria 181 | WP_079601315.1-Bradyrhizobium_erythrophlei_Proteobacteria #2D33E7 Bacteria 182 | WP_079544782.1-Bradyrhizobium_lablabi_Proteobacteria #2D33E7 Bacteria 183 | WP_024518537.1-Bradyrhizobium_sp._Tv2a-2_Proteobacteria #2D33E7 Bacteria 184 | WP_024511517.1-Bradyrhizobium_sp._ARR65_Proteobacteria #2D33E7 Bacteria 185 | TMJ47098.1-Alphaproteobacteria_bacterium_Proteobacteria #2D33E7 Bacteria 186 | TMK39387.1-Alphaproteobacteria_bacterium_Proteobacteria #2D33E7 Bacteria 187 | WP_172134110.1-Bradyrhizobium_sp._81013_Proteobacteria #2D33E7 Bacteria 188 | WP_172111906.1-Bradyrhizobium_sp._83012_Proteobacteria #2D33E7 Bacteria 189 | WP_172180802.1-Bradyrhizobium_sp._83002_Proteobacteria #2D33E7 Bacteria 190 | WP_083842450.1-Bradyrhizobium_sp._ORS_375_Proteobacteria #2D33E7 Bacteria 191 | WP_035655465.1-Bradyrhizobium_sp._STM_3809_Proteobacteria #2D33E7 Bacteria 192 | CCD98599.1-Bradyrhizobium_sp._STM_3809_Proteobacteria #2D33E7 Bacteria 193 | WP_168167890.1-Bradyrhizobium_sp._BTAi1_Proteobacteria #2D33E7 Bacteria 194 | NVN85223.1-Rhodopseudomonas_sp._Proteobacteria #2D33E7 Bacteria 195 | WP_011439962.1-Rhodopseudomonas_palustris_Proteobacteria #2D33E7 Bacteria 196 | EGP08750.1-Bradyrhizobiaceae_bacterium_SG-6C_Proteobacteria #2D33E7 Bacteria 197 | WP_043924226.1-Bradyrhizobiaceae_bacterium_SG-6C_Proteobacteria #2D33E7 Bacteria 198 | WP_002714592.1-Afipia_clevelandensis_Proteobacteria #2D33E7 Bacteria 199 | WP_046828790.1-Afipia_massiliensis_Proteobacteria #2D33E7 Bacteria 200 | WP_076861029.1-Bradyrhizobium_mercantei_Proteobacteria #2D33E7 Bacteria 201 | OJY09878.1-Rhizobiales_bacterium_62-47_Proteobacteria #2D33E7 Bacteria 202 | PYN74367.1-Candidatus_Rokubacteria_bacterium_Candidatus #969696 Other 203 | PYM96198.1-Candidatus_Rokubacteria_bacterium_Candidatus #969696 Other 204 | OGL20369.1-Candidatus_Rokubacteria_bacterium_RIFCSPLOWO2_12_FULL_71_22_Candidatus #969696 Other 205 | PYN46404.1-Candidatus_Rokubacteria_bacterium_Candidatus #969696 Other 206 | PYN32128.1-Candidatus_Rokubacteria_bacterium_Candidatus #969696 Other 207 | WP_089945766.1-Candidatus_Entotheonella_palauensis_Candidatus #969696 Other 208 | WP_089717141.1-Candidatus_Entotheonella_palauensis_Candidatus #969696 Other 209 | KAB2911669.1-Hyphomicrobiaceae_bacterium_Proteobacteria #2D33E7 Bacteria 210 | WP_027475679.1-Curvibacter_gracilis_Proteobacteria #2D33E7 Bacteria 211 | WP_157443435.1-Curvibacter_lanceolatus_Proteobacteria #2D33E7 Bacteria 212 | RUP23521.1-Curvibacter_sp._Proteobacteria #2D33E7 Bacteria 213 | GBU15266.1-Polaromonas_sp._Proteobacteria #2D33E7 Bacteria 214 | WP_155709830.1-Massilia_dura_Proteobacteria #2D33E7 Bacteria 215 | RZL87373.1-Variovorax_sp._Proteobacteria #2D33E7 Bacteria 216 | WP_145759386.1-Variovorax_sp._1180_Proteobacteria #2D33E7 Bacteria 217 | WP_126183471.1-Variovorax_sp._369_Proteobacteria #2D33E7 Bacteria 218 | WP_020722993.1-Variovorax_paradoxus_Proteobacteria #2D33E7 Bacteria 219 | WP_184605932.1-Variovorax_sp._BK613_Proteobacteria #2D33E7 Bacteria 220 | WP_132801781.1-Variovorax_sp._BK370_Proteobacteria #2D33E7 Bacteria 221 | WP_132755990.1-Variovorax_sp._BK151_Proteobacteria #2D33E7 Bacteria 222 | WP_176665552.1-Variovorax_sp._SG517_Proteobacteria #2D33E7 Bacteria 223 | WP_184638849.1-Variovorax_guangxiensis_Proteobacteria #2D33E7 Bacteria 224 | WP_126020505.1-Variovorax_guangxiensis_Proteobacteria #2D33E7 Bacteria 225 | WP_093195641.1-Variovorax_sp._YR750_Proteobacteria #2D33E7 Bacteria 226 | WP_126472589.1-Variovorax_gossypii_Proteobacteria #2D33E7 Bacteria 227 | WP_097197469.1-Variovorax_sp._YR752_Proteobacteria #2D33E7 Bacteria 228 | WP_093174861.1-Variovorax_sp._YR266_Proteobacteria #2D33E7 Bacteria 229 | WP_093076613.1-Variovorax_sp._OV084_Proteobacteria #2D33E7 Bacteria 230 | WP_093057006.1-Variovorax_sp._YR634_Proteobacteria #2D33E7 Bacteria 231 | WP_130421160.1-Variovorax_sp._BK460_Proteobacteria #2D33E7 Bacteria 232 | WP_093131647.1-Variovorax_sp._OK605_Proteobacteria #2D33E7 Bacteria 233 | WP_007837899.1-Variovorax_sp._CF313_Proteobacteria #2D33E7 Bacteria 234 | WP_081267376.1-Variovorax_paradoxus_Proteobacteria #2D33E7 Bacteria 235 | ODU15846.1-Variovorax_sp._SCN_67-85_Proteobacteria #2D33E7 Bacteria 236 | WP_172705422.1-Variovorax_paradoxus_Proteobacteria #2D33E7 Bacteria 237 | KPU98577.1-Variovorax_paradoxus_Proteobacteria #2D33E7 Bacteria 238 | MBS76598.1-Variovorax_sp._Proteobacteria #2D33E7 Bacteria 239 | WP_093343818.1-Variovorax_sp._PDC80_Proteobacteria #2D33E7 Bacteria 240 | KAF1061273.1-Variovorax_sp._Proteobacteria #2D33E7 Bacteria 241 | TAJ61793.1-Variovorax_sp._Proteobacteria #2D33E7 Bacteria 242 | WP_093435402.1-Variovorax_sp._770b2_Proteobacteria #2D33E7 Bacteria 243 | WP_052810747.1-Variovorax_paradoxus_Proteobacteria #2D33E7 Bacteria 244 | WP_013538622.1-Variovorax_paradoxus_Proteobacteria #2D33E7 Bacteria 245 | WP_176660984.1-Variovorax_sp._SG533_Proteobacteria #2D33E7 Bacteria 246 | WP_145738683.1-Variovorax_beijingensis_Proteobacteria #2D33E7 Bacteria 247 | WP_062470459.1-Variovorax_boronicumulans_Proteobacteria #2D33E7 Bacteria 248 | WP_095948515.1-Variovorax_boronicumulans_Proteobacteria #2D33E7 Bacteria 249 | WP_159278964.1-Variovorax_boronicumulans_Proteobacteria #2D33E7 Bacteria 250 | WP_159274326.1-Variovorax_boronicumulans_Proteobacteria #2D33E7 Bacteria 251 | WP_070061350.1-Variovorax_boronicumulans_Proteobacteria #2D33E7 Bacteria 252 | WP_095743122.1-Variovorax_boronicumulans_Proteobacteria #2D33E7 Bacteria 253 | WP_139703542.1-Variovorax_sp._KBS0712_Proteobacteria #2D33E7 Bacteria 254 | WP_099794476.1-Variovorax_sp._54_Proteobacteria #2D33E7 Bacteria 255 | WP_101490554.1-Variovorax_sp._RO1_Proteobacteria #2D33E7 Bacteria 256 | WP_153281245.1-Variovorax_paradoxus_Proteobacteria #2D33E7 Bacteria 257 | WP_056574498.1-Variovorax_sp._Root473_Proteobacteria #2D33E7 Bacteria 258 | WP_119556153.1-Acidovorax_cavernicola_Proteobacteria #2D33E7 Bacteria 259 | WP_093241869.1-Variovorax_sp._EL159_Proteobacteria #2D33E7 Bacteria 260 | WP_187113215.1-Variovorax_sp._PAMC26660_Proteobacteria #2D33E7 Bacteria 261 | WP_124458460.1-Variovorax_sp._KBW07_Proteobacteria #2D33E7 Bacteria 262 | QLG72306.1-Zygotorulaspora_mrakii_Saccharomycotina #e31a1c Saccharomycotina 263 | QUERY_Saccharomyces_cerevisiae_YOL164W #e31a1c Saccharomycotina 264 | XP_002555886.1-Lachancea_thermotolerans_Saccharomycotina #e31a1c Saccharomycotina 265 | SCW03834.1-Lachancea_fermentati_Saccharomycotina #e31a1c Saccharomycotina 266 | XP_018221989.1-Saccharomyces_eubayanus_Saccharomycotina #e31a1c Saccharomycotina 267 | QID88192.1-Saccharomyces_pastorianus_Saccharomycotina #e31a1c Saccharomycotina 268 | WP_092831606.1-Rhodospirillales_bacterium_URHD0017_Proteobacteria #2D33E7 Bacteria 269 | MBB5054515.1-Afipia_massiliensis_Proteobacteria #2D33E7 Bacteria 270 | WP_184089169.1-Afipia_massiliensis_Proteobacteria #2D33E7 Bacteria 271 | WP_046827511.1-Afipia_massiliensis_Proteobacteria #2D33E7 Bacteria 272 | WP_062313220.1-Bradyrhizobium_sp._CCH10-C7_Proteobacteria #2D33E7 Bacteria 273 | HBR43714.1-Afipia_sp._Proteobacteria #2D33E7 Bacteria 274 | HAQ93654.1-Afipia_sp._Proteobacteria #2D33E7 Bacteria 275 | WP_006021976.1-Afipia_broomeae_Proteobacteria #2D33E7 Bacteria 276 | RTL81632.1-Bradyrhizobiaceae_bacterium_Proteobacteria #2D33E7 Bacteria 277 | TXJ10197.1-Afipia_sp._Proteobacteria #2D33E7 Bacteria 278 | WP_002713184.1-Afipia_clevelandensis_Proteobacteria #2D33E7 Bacteria 279 | WP_009734135.1-Bradyrhizobiaceae_bacterium_SG-6C_Proteobacteria #2D33E7 Bacteria 280 | NGX94573.1-Candidatus_Afipia_apatlaquensis_Proteobacteria #2D33E7 Bacteria 281 | WP_175367589.1-Tardiphaga_robiniae_Proteobacteria #2D33E7 Bacteria 282 | WP_120288614.1-Tardiphaga_sp._YR296_Proteobacteria #2D33E7 Bacteria 283 | WP_184513838.1-Tardiphaga_robiniae_Proteobacteria #2D33E7 Bacteria 284 | WP_089261759.1-Tardiphaga_sp._OK246_Proteobacteria #2D33E7 Bacteria 285 | WP_092138970.1-Bradyrhizobium_sp._NFR13_Proteobacteria #2D33E7 Bacteria 286 | WP_115033941.1-Tardiphaga_sp._CF115_Proteobacteria #2D33E7 Bacteria 287 | WP_149531854.1-Tardiphaga_sp._P9-11_Proteobacteria #2D33E7 Bacteria 288 | OJY11205.1-Rhizobiales_bacterium_62-47_Proteobacteria #2D33E7 Bacteria 289 | WP_146690165.1-Bradyrhizobium_canariense_Proteobacteria #2D33E7 Bacteria 290 | WP_100386102.1-Afipia_broomeae_Proteobacteria #2D33E7 Bacteria 291 | WP_008968903.1-Bradyrhizobium_sp._STM_3843_Proteobacteria #2D33E7 Bacteria 292 | WP_164571151.1-Rhodopseudomonas_sp._BR0C11_Proteobacteria #2D33E7 Bacteria 293 | WP_107345917.1-Rhodopseudomonas_palustris_Proteobacteria #2D33E7 Bacteria 294 | WP_164638651.1-Rhodopseudomonas_sp._BR0G17_Proteobacteria #2D33E7 Bacteria 295 | WP_107356882.1-Rhodopseudomonas_palustris_Proteobacteria #2D33E7 Bacteria 296 | WP_011159875.1-Rhodopseudomonas_palustris_Proteobacteria #2D33E7 Bacteria 297 | WP_012497548.1-Rhodopseudomonas_palustris_Proteobacteria #2D33E7 Bacteria 298 | WP_119019080.1-Rhodopseudomonas_palustris_Proteobacteria #2D33E7 Bacteria 299 | WP_013504207.1-Rhodopseudomonas_palustris_Proteobacteria #2D33E7 Bacteria 300 | WP_047308756.1-Rhodopseudomonas_palustris_Proteobacteria #2D33E7 Bacteria 301 | WP_164630935.1-Rhodopseudomonas_sp._WA056_Proteobacteria #2D33E7 Bacteria 302 | WP_119858943.1-Rhodopseudomonas_palustris_Proteobacteria #2D33E7 Bacteria 303 | WP_142883484.1-Rhodopseudomonas_palustris_Proteobacteria #2D33E7 Bacteria 304 | WP_022722993.1-Rhodopseudomonas_sp._B29_Proteobacteria #2D33E7 Bacteria 305 | WP_054162298.1-Rhodopseudomonas_sp._AAP120_Proteobacteria #2D33E7 Bacteria 306 | WP_110782500.1-Rhodopseudomonas_faecalis_Proteobacteria #2D33E7 Bacteria 307 | WP_027276359.1-Rhodopseudomonas_palustris_Proteobacteria #2D33E7 Bacteria 308 | TAH64925.1-Rhodopseudomonas_palustris_Proteobacteria #2D33E7 Bacteria 309 | WP_011665574.1-Rhodopseudomonas_palustris_Proteobacteria #2D33E7 Bacteria 310 | ABD08837.1-Rhodopseudomonas_palustris_Proteobacteria #2D33E7 Bacteria 311 | WP_041798938.1-Rhodopseudomonas_palustris_Proteobacteria #2D33E7 Bacteria 312 | NVN87899.1-Rhodopseudomonas_sp._Proteobacteria #2D33E7 Bacteria 313 | OQW56345.1-Proteobacteria_bacterium_SG_bin9_Proteobacteria #2D33E7 Bacteria 314 | WP_136963313.1-Phreatobacter_stygius_Proteobacteria #2D33E7 Bacteria 315 | WP_136868902.1-Pseudomonas_bauzanensis_Proteobacteria #2D33E7 Bacteria 316 | EZQ18937.1-Pseudomonas_bauzanensis_Proteobacteria #2D33E7 Bacteria 317 | WP_185267007.1-Pseudomonas_xiamenensis_Proteobacteria #2D33E7 Bacteria 318 | WP_093476299.1-Pseudomonas_yangmingensis_Proteobacteria #2D33E7 Bacteria 319 | WP_108104178.1-Pseudomonas_mangrovi_Proteobacteria #2D33E7 Bacteria 320 | WP_017937932.1-Pseudomonas_thermotolerans_Proteobacteria #2D33E7 Bacteria 321 | MAB24835.1-Pseudomonadales_bacterium_Proteobacteria #2D33E7 Bacteria 322 | WP_182248855.1-Pseudomonas_sp._5Ae-yellow_Proteobacteria #2D33E7 Bacteria 323 | WP_083726556.1-Pseudomonas_pachastrellae_Proteobacteria #2D33E7 Bacteria 324 | PKM29133.1-Gammaproteobacteria_bacterium_HGW-Gammaproteobacteria-11_Proteobacteria #2D33E7 Bacteria 325 | WP_057659955.1-Pseudoxanthomonas_dokdonensis_Proteobacteria #2D33E7 Bacteria 326 | WP_077034059.1-Pelomonas_sp._KK5_Proteobacteria #2D33E7 Bacteria 327 | WP_151072822.1-Cupriavidus_oxalaticus_Proteobacteria #2D33E7 Bacteria 328 | WP_163259863.1-Caulobacter_sp._17J65-9_Proteobacteria #2D33E7 Bacteria 329 | WP_187448402.1-Caulobacter_sp._17J80-11_Proteobacteria #2D33E7 Bacteria 330 | WP_119419155.1-Rhodospirillaceae_bacterium_SYSU_D60015_Proteobacteria #2D33E7 Bacteria 331 | WP_071072555.1-Cupriavidus_malaysiensis_Proteobacteria #2D33E7 Bacteria 332 | WP_071038742.1-Cupriavidus_sp._USMAA2-4_Proteobacteria #2D33E7 Bacteria 333 | WP_071018820.1-Cupriavidus_sp._USMAHM13_Proteobacteria #2D33E7 Bacteria 334 | WP_066731373.1-Cupriavidus_sp._D384_Proteobacteria #2D33E7 Bacteria 335 | WP_116319445.1-Cupriavidus_sp._P-10_Proteobacteria #2D33E7 Bacteria 336 | WP_025586346.1-Cupriavidus_taiwanensis_Proteobacteria #2D33E7 Bacteria 337 | WP_116388232.1-Cupriavidus_taiwanensis_Proteobacteria #2D33E7 Bacteria 338 | WP_116377884.1-Cupriavidus_taiwanensis_Proteobacteria #2D33E7 Bacteria 339 | WP_062795892.1-Cupriavidus_nantongensis_Proteobacteria #2D33E7 Bacteria 340 | WP_116320039.1-Cupriavidus_sp._P-10_Proteobacteria #2D33E7 Bacteria 341 | WP_061999991.1-Burkholderia_sp._PAMC_28687_Proteobacteria #2D33E7 Bacteria 342 | WP_062170184.1-Burkholderia_sp._PAMC_26561_Proteobacteria #2D33E7 Bacteria 343 | WP_081889718.1-Caballeronia_sordidicola_Proteobacteria #2D33E7 Bacteria 344 | PZW26129.1-Thermosporothrix_hazakensis_Chloroflexi #2D33E7 Bacteria 345 | WP_111324427.1-Thermosporothrix_hazakensis_Chloroflexi #2D33E7 Bacteria 346 | WP_183035411.1-Cupriavidus_sp._UME77_Proteobacteria #2D33E7 Bacteria 347 | MBB1634382.1-Cupriavidus_sp._UME77_Proteobacteria #2D33E7 Bacteria 348 | WP_150987011.1-Cupriavidus_basilensis_Proteobacteria #2D33E7 Bacteria 349 | WP_092594144.1-Ralstonia_sp._25mfcol4.1_Proteobacteria #2D33E7 Bacteria 350 | -------------------------------------------------------------------------------- /phylogenetics/output/YOL164W_midpoint.tree: -------------------------------------------------------------------------------- 1 | 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299.1-Pseudomonas_yangmingensis_Proteobacteria:0.2420063289)100:0.0524687662,(WP_108104178.1-Pseudomonas_mangrovi_Proteobacteria:0.1439069358,WP_017937932.1-Pseudomonas_thermotolerans_Proteobacteria:0.146369063)100:0.1327273239)100:0.1002360458,(((MAB24835.1-Pseudomonadales_bacterium_Proteobacteria:0.0190952035,WP_182248855.1-Pseudomonas_sp._5Ae-yellow_Proteobacteria:0.0166167239)100:0.1055173487,WP_083726556.1-Pseudomonas_pachastrellae_Proteobacteria:0.1003605968)100:0.1239403048,PKM29133.1-Gammaproteobacteria_bacterium_HGW-Gammaproteobacteria-11_Proteobacteria:0.1694559589)100:0.2031845527)100:0.1243949737,(WP_057659955.1-Pseudoxanthomonas_dokdonensis_Proteobacteria:0.2504443031,WP_077034059.1-Pelomonas_sp._KK5_Proteobacteria:0.290739419)100:0.089768806)99:0.0853947488,WP_151072822.1-Cupriavidus_oxalaticus_Proteobacteria:0.4488612335)77:0.038526075)94:0.0539909156,(WP_163259863.1-Caulobacter_sp._17J65-9_Proteobacteria:0.0359889329,WP_187448402.1-Caulobacter_sp._17J80-11_Proteobacteria:0.0560016928)100:0.4106137649)88:0.0483121127,WP_119419155.1-Rhodospirillaceae_bacterium_SYSU_D60015_Proteobacteria:0.5054381725)86:0.0421557676,((((((((WP_071072555.1-Cupriavidus_malaysiensis_Proteobacteria:2.4404e-06,WP_071038742.1-Cupriavidus_sp._USMAA2-4_Proteobacteria:0.0018049159)100:0.001807578,WP_071018820.1-Cupriavidus_sp._USMAHM13_Proteobacteria:2.614e-06)100:0.1086010247,WP_066731373.1-Cupriavidus_sp._D384_Proteobacteria:0.1244621502)99:0.0424841488,WP_116319445.1-Cupriavidus_sp._P-10_Proteobacteria:0.1027967862)100:0.0837115318,((((WP_025586346.1-Cupriavidus_taiwanensis_Proteobacteria:2.5529e-06,WP_116388232.1-Cupriavidus_taiwanensis_Proteobacteria:0.0036557088)100:0.0316799251,WP_116377884.1-Cupriavidus_taiwanensis_Proteobacteria:0.0302374199)99:0.0150639375,WP_062795892.1-Cupriavidus_nantongensis_Proteobacteria:0.0148732888)100:0.0624658383,WP_116320039.1-Cupriavidus_sp._P-10_Proteobacteria:0.0839847297)100:0.1462076246)100:0.1899307557,((WP_061999991.1-Burkholderia_sp._PAMC_28687_Proteobacteria:0.008725495,WP_062170184.1-Burkholderia_sp._PAMC_26561_Proteobacteria:0.0025040922)100:0.0260404307,WP_081889718.1-Caballeronia_sordidicola_Proteobacteria:0.0277042159)100:0.4391644299)99:0.0726843554,(PZW26129.1-Thermosporothrix_hazakensis_Chloroflexi:2.5529e-06,WP_111324427.1-Thermosporothrix_hazakensis_Chloroflexi:2.5529e-06)100:0.4278181864)100:0.0982174834,(((WP_183035411.1-Cupriavidus_sp._UME77_Proteobacteria:2.5529e-06,MBB1634382.1-Cupriavidus_sp._UME77_Proteobacteria:2.4494e-06)100:0.0121357022,WP_150987011.1-Cupriavidus_basilensis_Proteobacteria:0.006166255)100:0.1044846317,WP_092594144.1-Ralstonia_sp._25mfcol4.1_Proteobacteria:0.1855041654)100:0.3460007168)98:0.0688633008)83:0.0191555351); 2 | -------------------------------------------------------------------------------- /phylogenetics/output/YOL164W_rectangular_tree.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SysBioChalmers/HGTphyloDetect/d4ff8c5dbdaf3b200e45dd57962dfb11776f43d7/phylogenetics/output/YOL164W_rectangular_tree.pdf -------------------------------------------------------------------------------- /phylogenetics/scripts/HGT_homologs_sequence.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | 4 | # This file is to get the homolog accession identifiers and sequences for the following multiple sequence alignment 5 | 6 | 7 | import re 8 | import sys 9 | import os 10 | from Bio import SeqIO 11 | from Bio import Entrez 12 | from ete3 import NCBITaxa 13 | 14 | 15 | def parse_NCBI(filename): 16 | with open(filename, "r") as infile : 17 | lines = infile.readlines() 18 | 19 | accession_number = list() 20 | accession_similarity = dict() 21 | for line in lines : 22 | accession = line.strip("\n").split("\t")[2] 23 | similarity = line.strip("\n").split("\t")[3] 24 | accession_number.append(accession) 25 | accession_similarity[accession] = float(similarity) 26 | 27 | return accession_number, accession_similarity 28 | 29 | def get_refSeq(gene) : 30 | # get the related protein sequence accoding to protein identifier 31 | with open("./input/%s.fasta" % gene, "r") as handleGene : 32 | proteinSeq = dict() 33 | for record in SeqIO.parse(handleGene, "fasta") : 34 | # ['__add__', '__bool__', '__class__', '__contains__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', 35 | # '__getattribute__', '__getitem__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__iter__', '__le__', '__le___', '__len__', '__lt__', 36 | # '__module__', '__ne__', '__new__', '__nonzero__', '__radd__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', 37 | # '__subclasshook__', '__weakref__', '_per_letter_annotations', '_seq', '_set_per_letter_annotations', '_set_seq', 'annotations', 'dbxrefs', 'description', 38 | # 'features', 'format', 'id', 'letter_annotations', 'lower', 'name', 'reverse_complement', 'seq', 'translate', 'upper'] 39 | proteinSeq[record.id] = str(record.seq) 40 | 41 | return proteinSeq[gene] 42 | 43 | def getTaxid(accession): 44 | # Retrieving data in the GenBank using only the GenBank code accession in biopython 45 | # https://www.ncbi.nlm.nih.gov/books/NBK25497/table/chapter2.T._entrez_unique_identifiers_ui/?report=objectonly 46 | # https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.EFetch 47 | # https://biopython.org/DIST/docs/api/Bio.Entrez-module.html 48 | Entrez.email = "abcd@ncbi.org" 49 | 50 | # https://www.biostars.org/p/304175/ 51 | # get tax id using only the GenBank code accession in biopython 52 | # handle = Entrez.efetch(db='protein', id="NP_012706.1", rettype='gb') 53 | handle = Entrez.efetch(db='protein', id=accession, rettype='gb') 54 | record = SeqIO.read(handle,'genbank') 55 | # print(record.features[0].qualifiers) 56 | if record.features[0].qualifiers['db_xref'][0].split(":")[0] == 'taxon': 57 | taxid = record.features[0].qualifiers['db_xref'][0].split(":")[1] # the type is a string 58 | organism = record.features[0].qualifiers['organism'][0] 59 | # seq = record.seq 60 | # print(taxid,organism) 61 | 62 | return taxid 63 | 64 | def getSeq(accession): 65 | # Retrieving data in the GenBank using only the GenBank code accession in biopython 66 | # https://www.ncbi.nlm.nih.gov/books/NBK25497/table/chapter2.T._entrez_unique_identifiers_ui/?report=objectonly 67 | # https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.EFetch 68 | # https://biopython.org/DIST/docs/api/Bio.Entrez-module.html 69 | Entrez.email = "abcd@ncbi.org" 70 | 71 | # https://www.biostars.org/p/304175/ 72 | # get tax id using only the GenBank code accession in biopython 73 | # handle = Entrez.efetch(db='protein', id="NP_012706.1", rettype='gb') 74 | handle = Entrez.efetch(db='protein', id=accession, rettype='gb') 75 | record = SeqIO.read(handle,'genbank') 76 | seq = record.seq 77 | # print(seq) 78 | 79 | return seq 80 | 81 | def main() : 82 | name = sys.argv[1:][0] 83 | gene = name[6:-6] 84 | 85 | print('This is gene', gene + ' ---------------') 86 | if os.path.exists("./input/%s.txt" % gene) : 87 | accession_number, accession_similarity = parse_NCBI("./input/%s.txt" % gene) 88 | # print(len(accession_number)) 89 | else : 90 | print('Warning: please run BLASTP first!') 91 | 92 | ncbi = NCBITaxa() 93 | id_seq = dict() 94 | for accession in accession_number : 95 | # print(accession) 96 | # print(accession_similarity[accession]) 97 | try : 98 | seq = str(getSeq(accession)) 99 | except : 100 | continue 101 | 102 | taxid = getTaxid(accession) 103 | lineage = ncbi.get_lineage(taxid) 104 | lineage2ranks = ncbi.get_rank(lineage) 105 | ranks2lineage = dict((rank, taxid) for (taxid, rank) in lineage2ranks.items()) 106 | # print(ranks2lineage) 107 | 108 | try : 109 | taxid2name = ncbi.get_taxid_translator([ranks2lineage['phylum'], ranks2lineage['species']]) 110 | if taxid2name[ranks2lineage["phylum"]] != "Ascomycota": 111 | accession = accession + "-" + taxid2name[ranks2lineage["species"]].replace(" ", "_") + "@" + taxid2name[ranks2lineage["phylum"]] 112 | 113 | print(accession) 114 | id_seq[accession] = seq 115 | continue 116 | except : 117 | pass 118 | 119 | try : 120 | taxid2name = ncbi.get_taxid_translator([ranks2lineage['phylum'], ranks2lineage['subphylum'], ranks2lineage['species']]) 121 | # print(taxid2name[ranks2lineage["subphylum"]]) 122 | 123 | if accession_similarity[accession] < 80 : # Sequences with more than 80% similarity were eliminated. 124 | if taxid2name[ranks2lineage["subphylum"]] == "Saccharomycotina" : 125 | accession = accession + "-" + taxid2name[ranks2lineage["species"]].replace(" ", "_") + "@Saccharomycotina" 126 | if taxid2name[ranks2lineage["subphylum"]] != "Saccharomycotina" and taxid2name[ranks2lineage["phylum"]] == "Ascomycota": 127 | accession = accession + "-" + taxid2name[ranks2lineage["species"]].replace(" ", "_") + "@other_Ascomycota" 128 | if taxid2name[ranks2lineage["phylum"]] != "Ascomycota": 129 | accession = accession + "-" + taxid2name[ranks2lineage["species"]].replace(" ", "_") + "@" + taxid2name[ranks2lineage["phylum"]] 130 | print(accession) 131 | id_seq[accession] = seq 132 | else : 133 | continue 134 | except : 135 | continue 136 | 137 | gene_seq = get_refSeq(gene) 138 | gene_query = "QUERY_" + 'Saccharomyces_cerevisiae_' + gene 139 | print(gene_query) 140 | print(gene_seq) 141 | id_seq[gene_query] = gene_seq 142 | # print(len(id_seq)) 143 | 144 | with open("./input/%s_homologs.fasta" % gene, "w") as outfile : 145 | for accession,seq in id_seq.items() : 146 | outfile.write(">"+accession+"\n") 147 | outfile.write(seq+"\n") 148 | 149 | 150 | if __name__== "__main__": 151 | main() 152 | -------------------------------------------------------------------------------- /phylogenetics/scripts/create_iTOL_config.pl: -------------------------------------------------------------------------------- 1 | #!/usr/bin/perl -w 2 | # This script is obtained from one high impact paper shown as below: 3 | # Shen, Xing-Xing, et al. "Tempo and mode of genome evolution in the budding yeast subphylum." Cell 175.6 (2018): 1533-1545 4 | 5 | use strict; 6 | use warnings; 7 | use Bio::TreeIO; 8 | 9 | my $infile = $ARGV[0]; #newick tree must end in .tree 10 | 11 | $infile =~ /^(.*)\.tree$/; 12 | my $query = $1; 13 | my %color_hash; 14 | popcolhash(); 15 | 16 | my $outfile1 = "${query}-color.txt"; 17 | my $outfile2 = "${query}-font.txt"; 18 | 19 | open (OFIL1, '>', $outfile1) or die "Couldn't write to file $outfile1: $!\n"; 20 | open (OFIL2, '>', $outfile2) or die "Couldn't write to file $outfile2: $!\n"; 21 | popcolorfile(); 22 | popfontfile(); 23 | 24 | my $in = new Bio::TreeIO(-file => "$infile", -format => 'newick'); 25 | while( my $tree = $in->next_tree ) { 26 | for my $node ( $tree->get_nodes ) { 27 | if ( $node->is_Leaf ) { 28 | my $name = $node->id(); 29 | 30 | my $lineage = ''; 31 | if ( $name =~ /.+_(other_[A-Za-z0-9]+)$/ or $name =~ /.+_([A-Za-z0-9]+)$/){ 32 | $lineage = $1; 33 | } 34 | 35 | if ($name =~ /^QUERY/i){ 36 | print OFIL2 "${name},label,#e31a1c,bold,1\n"; 37 | print OFIL1 "${name} \#e31a1c Saccharomycotina\n"; 38 | }else{ 39 | print OFIL2 "${name},label,#000000,normal,1\n"; 40 | } 41 | 42 | if (exists $color_hash{$lineage}) { 43 | my $color = $color_hash{$lineage}; 44 | print OFIL1 "${name} ${color}\n"; 45 | }else{unless ($name =~ /^QUERY/i) {print OFIL1 "${name} \#969696 Other\n";}} 46 | 47 | } 48 | } 49 | } 50 | 51 | close OFIL1; 52 | close OFIL2; 53 | 54 | sub popcolorfile 55 | { 56 | print OFIL1 "DATASET_COLORSTRIP 57 | #In colored strip datasets, each ID is associated to a color box/strip and can have an optional label. Color can be specified in hexadecimal, RGB or RGBA notation. When using RGB or RGBA notation, you cannot use COMMA as the dataset separator 58 | 59 | #lines starting with a hash are comments and ignored during parsing 60 | 61 | #=================================================================# 62 | # MANDATORY SETTINGS # 63 | #=================================================================# 64 | #select the separator which is used to delimit the data below (TAB,SPACE or COMMA).This separator must be used throughout this file (except in the SEPARATOR line, which uses space). 65 | 66 | #SEPARATOR TAB 67 | #SEPARATOR COMMA 68 | SEPARATOR SPACE 69 | 70 | #label is used in the legend table (can be changed later) 71 | DATASET_LABEL Taxonomy 72 | 73 | #dataset color (can be changed later) 74 | COLOR #000000 75 | 76 | #=================================================================# 77 | # OPTIONAL SETTINGS # 78 | #=================================================================# 79 | 80 | #If COLOR_BRANCHES is set to 1, branches of the tree will be colored according to the colors of the strips above the leaves. 81 | #When all children of a node have the same color, it will be colored the same, ie. the color will propagate inwards towards the root. 82 | COLOR_BRANCHES 1 83 | 84 | #each dataset can have a legend, which is defined below 85 | #for each row in the legend, there should be one shape, color and label 86 | #shape should be a number between 1 and 5: 87 | #1: square 88 | #2: circle 89 | #3: star 90 | #4: right pointing triangle 91 | #5: left pointing triangle 92 | 93 | LEGEND_TITLE Taxonomy 94 | LEGEND_SHAPES 1 1 1 1 95 | LEGEND_COLORS #e31a1c #28b1aa #2D33E7 #969696 96 | LEGEND_LABELS Saccharomycotina Fungi Bacteria Other 97 | # LEGEND_SHAPES 1 1 1 1 1 1 1 1 1 1 1 1 98 | # LEGEND_COLORS #e31a1c #fb9a99 #ff7f00 #28b1aa #b15928 #6a3d9a #cab2d6 #1f78b4 #a6cee3 #33a02c #ffff99 #969696 99 | # LEGEND_LABELS Leotiomycetes Sordariomycetes Eurotiomycetes Dothideomycetes other_Pezizomycotina other_Ascomycota other_Fungi other_Opisthokonta other_Eukaryota Bacteria Archaea Viruses 100 | 101 | #=================================================================# 102 | # all other optional settings can be set or changed later # 103 | # in the web interface (under 'Datasets' tab) # 104 | #=================================================================# 105 | 106 | #width of the colored strip 107 | STRIP_WIDTH 35 108 | 109 | #left margin, used to increase/decrease the spacing to the next dataset. Can be negative, causing datasets to overlap. 110 | #MARGIN 5 111 | 112 | #border width; if set above 0, a border of specified width (in pixels) will be drawn around the color strip 113 | #BORDER_WIDTH 2 114 | 115 | #border color; used when BORDER_WIDTH is above 0 116 | #BORDER_COLOR #969696 117 | 118 | #always show internal values; if set, values associated to internal nodes will be displayed even if these nodes are not collapsed. It could cause overlapping in the dataset display. 119 | #SHOW_INTERNAL 1 120 | 121 | #Internal tree nodes can be specified using IDs directly, or using the 'last common ancestor' method described in iTOL help pages 122 | 123 | #=================================================================# 124 | # Actual data follows after the DATA keyword # 125 | #=================================================================# 126 | DATA 127 | 128 | #Examples: 129 | #assign a red colored strip to leaf 9606, with label 'Human' (label is displayed in the mouseover popups) 130 | #9606 #ff0000 Human 131 | 132 | #assign a green, semi-transparent (alpha 0.5) strip to an internal node, without any label. If 'Show internal values' is set to 'No', this will only be displayed if the node is collapsed. 133 | #9606|5664 rgba(0,255,0,0.5) 134 | "; 135 | 136 | } 137 | 138 | sub popfontfile 139 | { 140 | print OFIL2 "TREE_COLORS 141 | #use this template to define branch colors and styles, colored ranges and label colors/font styles 142 | #lines starting with a hash are comments and ignored during parsing 143 | 144 | #=================================================================# 145 | # MANDATORY SETTINGS # 146 | #=================================================================# 147 | #select the separator which is used to delimit the data below (TAB,SPACE or COMMA).This separator must be used throughout this file (except in the SEPARATOR line, which uses space). 148 | 149 | #SEPARATOR TAB 150 | #SEPARATOR SPACE 151 | SEPARATOR COMMA 152 | 153 | #First 3 fields define the node, type and color 154 | #Possible types are: 155 | #'range': defines a colored range (colored background for labels/clade) 156 | #'clade': defines color/style for all branches in a clade 157 | #'branch': defines color/style for a single branch 158 | #'label': defines font color/style for the leaf label 159 | 160 | #The following additional fields are required: 161 | #for 'range', field 4 defines the colored range label (used in the legend) 162 | 163 | #The following additional fields are optional: 164 | #for 'label', field 4 defines the font style ('normal',''bold', 'italic' or 'bold-italic') and field 5 defines the numeric scale factor for the font size (eg. with value 2, font size for that label will be 2x the standard size) 165 | #for 'clade' and 'branch', field 4 defines the branch style ('normal' or 'dashed') and field 5 defines the branch width scale factor (eg. with value 0.5, branch width for that clade will be 0.5 the standard width) 166 | 167 | #Internal tree nodes can be specified using IDs directly, or using the 'last common ancestor' method described in iTOL help pages 168 | #=================================================================# 169 | # Actual data follows after the DATA keyword # 170 | #=================================================================# 171 | DATA 172 | #NODE_ID,TYPE,COLOR,LABEL_OR_STYLE,SIZE_FACTOR 173 | 174 | #Examples 175 | #internal node with solid branches colored blue and twice the standard width 176 | #9031|9606,clade,#0000ff,normal,2 177 | #internal node with dashed branches colored red and one half the standard width 178 | #601|340,clade,#ff0000,dashed,0.5 179 | #a single internal branch colored green, dashed and 5 times the normal width 180 | #915|777,branch,#00ff00,dashed,5 181 | 182 | #colored range covering all leaves of an internal node, colored red and with label 'Eukaryota' 183 | #184922|9606,range,#ff0000,Eukaryota 184 | #examples of colored ranges from iTOL's Tree of Life 185 | #2190|2287,range,#aaffaa,Archaea 186 | #623|1502,range,#aaaaff,Bacteria 187 | 188 | #leaf label for node 9606 will be displayed in green, bold and twice the regular font size 189 | #9606,label,#00ff00,bold,2 190 | 191 | #leaf label for node 9031 will be displayed in yellow, bold italic and half the regular font size 192 | #9031,label,#ffff00,bold-italic,0.5 193 | 194 | #leaf label for node 8015 will be displayed in blue 195 | #8015,label,#0000ff 196 | "; 197 | } 198 | 199 | sub popcolhash 200 | { 201 | 202 | #good general color scheme 203 | %color_hash = ( 204 | "Actinobacteria","#2D33E7 Bacteria", 205 | "Firmicutes","#2D33E7 Bacteria", 206 | "Proteobacteria","#2D33E7 Bacteria", 207 | "other_Bacteria","#2D33E7 Bacteria", 208 | "Aquificae","#2D33E7 Bacteria", 209 | "Armatimonadetes","#2D33E7 Bacteria", 210 | "BacteroidetesChlorobi","#2D33E7 Bacteria", 211 | "Caldiserica","#2D33E7 Bacteria", 212 | "ChlamydiaeVerrucomicrobia","#2D33E7 Bacteria", 213 | "Chloroflexi","#2D33E7 Bacteria", 214 | "Chrysiogenetes","#2D33E7 Bacteria", 215 | "Cyanobacteria","#2D33E7 Bacteria", 216 | "Deferribacteres","#2D33E7 Bacteria", 217 | "DeinococcusThermus","#2D33E7 Bacteria", 218 | "Dictyoglomi","#2D33E7 Bacteria", 219 | "Elusimicrobia","#2D33E7 Bacteria", 220 | "FibrobacteresAcidobacteria","#2D33E7 Bacteria", 221 | "Fusobacteria","#2D33E7 Bacteria", 222 | "Gemmatimonadetes","#2D33E7 Bacteria", 223 | "Nitrospinae","#2D33E7 Bacteria", 224 | "Nitrospirae","#2D33E7 Bacteria", 225 | "Planctomycetes","#2D33E7 Bacteria", 226 | "Spirochaetes","#2D33E7 Bacteria", 227 | "Synergistetes","#2D33E7 Bacteria", 228 | "Tenericutes","#2D33E7 Bacteria", 229 | "Thermodesulfobacteria","#2D33E7 Bacteria", 230 | "Thermotogae","#2D33E7 Bacteria", 231 | "other_Fungi","#28b1aa Fungi", 232 | "Microsporidia","#28b1aa Fungi", 233 | "other_Pezizomycotina","#28b1aa Fungi", 234 | "Dothideomycetes","#28b1aa Fungi", 235 | "Eurotiomycetes","#28b1aa Fungi", 236 | "Sordariomycetes","#28b1aa Fungi", 237 | "Leotiomycetes","#28b1aa Fungi", 238 | "Taphrinomycotina","#28b1aa Fungi", 239 | "Basidiomycota","#28b1aa Fungi", 240 | "Mucoromycota", "#28b1aa Fungi", 241 | "other_Dikarya","#28b1aa Fungi", 242 | "other_Ascomycota","#28b1aa Fungi", 243 | "Saccharomycotina","#e31a1c Saccharomycotina", 244 | ); 245 | 246 | } -------------------------------------------------------------------------------- /phylogenetics/scripts/generate_tree.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | var=$1 4 | gene=${var%.fas*} 5 | echo 'This is gene '$gene' ' 6 | echo 'Begin to run!!!' 7 | 8 | cd ../ 9 | 10 | python scripts/HGT_homologs_sequence.py input/${gene}.fasta 11 | 12 | mafft --thread 6 --auto ./input/${gene}_homologs.fasta > ./intermediate/${gene}_aln.fasta 13 | 14 | trimal -in ./intermediate/${gene}_aln.fasta -out ./intermediate/${gene}_aln_trimmed.fasta -automated1 15 | 16 | iqtree -nt 6 -st AA -s ./intermediate/${gene}_aln_trimmed.fasta -m TEST -mrate G4 -keep-ident -bb 1000 -pre ./intermediate/${gene} 17 | 18 | Rscript scripts/midpoint_tree.R ${gene} 19 | 20 | perl scripts/create_iTOL_config.pl ./intermediate/${gene}_midpoint.tree 21 | 22 | echo 'Yep, finish!!!' 23 | 24 | -------------------------------------------------------------------------------- /phylogenetics/scripts/midpoint_tree.R: -------------------------------------------------------------------------------- 1 | # Usage: run command line Rscript midpoint_tree.R gene 2 | # For example: Rscript scripts/midpoint_tree.R YOL164W 3 | 4 | # Midpoint root a phylogeny with R: 5 | library(ape) 6 | library(phangorn) 7 | 8 | args = commandArgs(T) 9 | print(args[1]) 10 | 11 | treefile <- paste("./intermediate/",args[1],".treefile",sep="") 12 | mytree <- read.tree(treefile) 13 | midponit_mytree <- ladderize (midpoint(mytree)) 14 | write.tree (midponit_mytree, paste("./intermediate/",args[1],"_midpoint.tree",sep="")) 15 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | ete3==3.1.2 2 | biopython==1.78 --------------------------------------------------------------------------------