virannot_rps2tsv: otu.py comparison

comparison otu.py @ 0:bbaa89f070f4 draft

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/virAnnot commit 3a3b40c15ae5e82334f016e88b1f3c5bbbb3b2cd

author	iuc
date	Mon, 04 Mar 2024 19:56:16 +0000
parents
children	d1fd5579469d

comparison

equal deleted inserted replaced

--1:000000000000
+:bbaa89f070f4
+#!/usr/bin/env python3
+# Name: virAnnot_otu
+# Author: Marie Lefebvre - INRAE
+# Reuirements: Ete3 toolkit and external apps
+# Aims: Create viral OTUs based on RPS and Blast annotations
+import argparse
+import csv
+import logging as log
+import os
+import random
+import re
+import pandas as pd
+import xlsxwriter
+from Bio import SeqIO
+from Bio.Align.Applications import ClustalOmegaCommandline
+from ete3 import NodeStyle, SeqGroup, SeqMotifFace, Tree, TreeStyle
+def main():
+"""
+1 - retrieve info (sequence, query_id, taxo) from RPS file
+2 - align protein sequences of the same domain, calculate
+matrix of distances, generate trees
+3 - get statistics (read number) per otu
+4 - create HTML report
+"""
+options = _set_options()
+_set_log_level(options.verbosity)
+hits_collection = _cut_sequence(options)
+_align_sequences(options, hits_collection)
+_get_stats(options, hits_collection)
+_create_html(options, hits_collection)
+def _cut_sequence(options):
+"""
+Retrieve viral hits and sequences from RPS files
+"""
+log.info("Cut sequences")
+i = 0  # keep track of iterations over rps files to use the corresponding fasta file
+collection = {}
+options.rps.sort()
+for rps_file in options.rps:
+log.debug("Reading rps file " + str(rps_file))
+with open(rps_file[0], 'r') as rps_current_file:
+rps_reader = csv.reader(rps_current_file, delimiter='\t')
+headers = 0
+for row in rps_reader:
+if headers == 0:
+# headers
+headers += 1
+else:
+if row[1] == "no_hit":
+pass
+else:
+query_id = row[0]
+cdd_id = row[2]
+startQ = int(row[5])
+endQ = int(row[6])
+frame = float(row[7])
+description = row[8]
+superkingdom = row[9]
+match = re.search("Viruses", superkingdom)
+# if contig is viral then retrieve sequence
+if match:
+options.fasta.sort()
+seq = _retrieve_fasta_seq(options.fasta[i][0], query_id)
+seq_length = len(seq)
+if endQ < seq_length:
+seq = seq[startQ - 1:endQ]
+else:
+seq = seq[startQ - 1:seq_length]
+if frame < 0:
+seq = seq.reverse_complement()
+prot = seq.translate()
+if len(prot) >= options.min_protein_length:
+log.debug("Add " + query_id + " to collection")
+if cdd_id not in collection:
+collection[cdd_id] = {}
+collection[cdd_id][query_id] = {}
+collection[cdd_id][query_id]["nuccleotide"] = seq
+collection[cdd_id][query_id]["protein"] = prot
+collection[cdd_id][query_id]["full_description"] = description
+if options.blast is not None:
+options.blast.sort()
+with open(options.blast[i][0], 'r') as blast_current_file:
+blast_reader = csv.reader(blast_current_file, delimiter='\t')
+for b_query in blast_reader:
+if b_query[1] == query_id:
+collection[cdd_id][query_id]["nb"] = b_query[2]
+if len(b_query) > 10:
+collection[cdd_id][query_id]["taxonomy"] = b_query[14]
+else:
+collection[cdd_id][query_id]["taxonomy"] = "Unknown"
+else:
+if "nb" not in collection[cdd_id][query_id]:
+collection[cdd_id][query_id]["nb"] = 0
+if "taxonomy" not in collection[cdd_id][query_id]:
+collection[cdd_id][query_id]["taxonomy"] = "Unknown"
+else:
+log.info("No blast file")
+collection[cdd_id][query_id]["taxonomy"] = "Unknown"
+collection[cdd_id][query_id]["nb"] = 0
+collection[cdd_id]["short_description"] = description.split(",")[0] + description.split(",")[1]  # keep pfamXXX and RdRp 1
+collection[cdd_id]["full_description"] = description
+i += 1
+return collection
+def _retrieve_fasta_seq(fasta_file, query_id):
+"""
+From fasta file retrieve specific sequence with id
+"""
+contigs_list = SeqIO.to_dict(SeqIO.parse(open(fasta_file), 'fasta'))
+try:
+seq = contigs_list[query_id].seq
+except KeyError:
+print("KeyError for " + query_id + " file " + fasta_file)
+else:
+return seq
+def _create_tree(tree, fasta, out, color):
+"""
+Create phylogenic tree from multiple alignments
+"""
+try:
+f = open(tree, 'r')
+except IOError:
+log.info("Unknown file: " + tree + ". You may have less than 2 sequences to align.")
+return
+line = ""
+for word in f:
+line += word.strip()
+f.close()
+seqs = SeqGroup(fasta, format="fasta")
+t = Tree(tree)
+ts = TreeStyle()
+ts.show_branch_length = True
+colors = _parse_color_file(color)
+node_names = t.get_leaf_names()
+for name in node_names:
+seq = seqs.get_seq(name)
+seqFace = SeqMotifFace(seq, seq_format="()")
+node = t.get_leaves_by_name(name)
+for i in range(0, len(node)):
+if name in colors:
+ns = NodeStyle()
+ns['bgcolor'] = colors[name]
+node[i].set_style(ns)
+node[i].add_face(seqFace, 0, 'aligned')
+t.render(out, tree_style=ts)
+def _parse_color_file(file):
+fh = open(file)
+reader = csv.reader(fh, delimiter="\t")
+data = list(reader)
+colors = {}
+for i in range(0, len(data)):
+colors[data[i][0]] = data[i][1]
+return colors
+def _align_sequences(options, hits_collection):
+"""
+Align hit sequences with pfam reference
+"""
+log.info("Align sequences")
+if not os.path.exists(options.output):
+os.mkdir(options.output)
+color_by_sample = {}
+for cdd_id in hits_collection:
+cdd_output = options.output + "/" + hits_collection[cdd_id]["short_description"].replace(" ", "_")
+if not os.path.exists(cdd_output):
+os.mkdir(cdd_output)
+if os.path.exists(cdd_output + "/seq_to_align.fasta"):
+os.remove(cdd_output + "/seq_to_align.fasta")
+file_seq_to_align = cdd_output + "/seq_to_align.fasta"
+file_color_config = cdd_output + "/color_config.txt"
+f = open(file_seq_to_align, "a")
+f_c = open(file_color_config, "w+")
+log.info("Writing to " + file_seq_to_align)
+count = 0  # count number of contig per domain
+for query_id in hits_collection[cdd_id]:
+if query_id not in ["short_description", "full_description"]:
+sample = query_id.split("_")[0]  # get sample from SAMPLE_IdCONTIG
+sample_color = "#" + ''.join([random.choice('ABCDEF0123456789') for i in range(6)])
+# same color for each contig of the same sample
+if sample not in color_by_sample.keys():
+color_by_sample[sample] = sample_color
+f.write(">" + query_id + "\n")
+f.write(str(hits_collection[cdd_id][query_id]["protein"]) + "\n")
+f_c.write(query_id + '\t' + color_by_sample[sample] + '\n')
+count += 1
+f.close()
+f_c.close()
+file_seq_aligned = cdd_output + '/seq_aligned.final_tree.fa'
+tree_file = cdd_output + '/tree.dnd'
+file_cluster = cdd_output + '/otu_cluster.csv'
+# create alignment for domain with more than 1 contigs
+if count > 1:
+log.info("Run clustal omega...")
+clustalo_cmd = ClustalOmegaCommandline("clustalo", infile=file_seq_to_align, outfile=file_seq_aligned,
+guidetree_out=tree_file, seqtype="protein", force=True)
+log.debug(clustalo_cmd)
+stdout, stderr = clustalo_cmd()
+log.debug(stdout + stderr)
+# create tree plot with colors
+file_matrix = cdd_output + "/identity_matrix.csv"
+log.info("Create tree...")
+_create_tree(tree_file, file_seq_aligned, tree_file + '.png', file_color_config)
+_compute_pairwise_distance(options, file_seq_aligned, file_matrix, cdd_id)
+log.info("Retrieve OTUs...")
+# if os.path.exists(file_cluster):
+#     os.remove(file_cluster)
+otu_cmd = os.path.join(options.tool_path, 'seek_otu.R') + ' ' + file_matrix + ' ' + file_cluster + ' ' + str(options.perc)
+log.debug(otu_cmd)
+os.system(otu_cmd)
+# only one contig
+else:
+mv_cmd = 'cp ' + file_seq_to_align + ' ' + file_seq_aligned
+log.debug(mv_cmd)
+os.system(mv_cmd)
+f = open(file_cluster, "w+")
+f.write('OTU_1,1,' + list(hits_collection[cdd_id].keys())[0] + ',')
+f.close()
+def _compute_pairwise_distance(options, file_seq_aligned, file_matrix, cdd_id):
+"""
+Calculate paiwise distance between aligned protein sequences
+from a cdd_id
+"""
+log.info("Compute pairwise distance of " + cdd_id)
+matrix = {}
+for k1 in SeqIO.parse(file_seq_aligned, "fasta"):
+row = []
+for k2 in SeqIO.parse(file_seq_aligned, "fasta"):
+identic = 0
+compared = 0
+keep_pos = 0
+for base in k1:
+base2 = k2[keep_pos]
+# mutation, next
+if base == 'X' or base2 == 'X':
+keep_pos += 1
+continue
+# gap in both sequences, next
+if base == '-' and base2 == '-':
+keep_pos += 1
+continue
+# gap in one of the sequence, next
+if base == '-' or base2 == '-':
+keep_pos += 1
+continue
+# identity
+if base == base2:
+identic += 1
+compared += 1
+keep_pos += 1
+# set minimum overlap to 20
+if compared == 0 or compared < 20:
+percentIdentity = 0
+else:
+percentIdentity = (identic / compared) * 100
+row.append(percentIdentity)
+matrix[k1.id] = row
+log.debug("Write " + file_matrix)
+f = open(file_matrix, "w+")
+for row in matrix:
+f.write(row + ',' + ', '.join(map(str, matrix[row])) + "\n")
+f.close()
+def _get_stats(options, hits_collection):
+"""
+Retrieve annotation and number of read
+for    each OTUs
+"""
+file_xlsx = options.output + '/otu_stats.xlsx'  # Create a workbook
+workbook = xlsxwriter.Workbook(file_xlsx)
+log.info("Writing stats to " + file_xlsx)
+for cdd_id in hits_collection:
+otu_collection = {}
+cdd_output = options.output + "/" + hits_collection[cdd_id]["short_description"].replace(" ", "_")
+worksheet = workbook.add_worksheet(hits_collection[cdd_id]["short_description"])  # add a worksheet
+file_cluster = cdd_output + '/otu_cluster.csv'
+with open(file_cluster, 'r') as clust:
+otu_reader = csv.reader(clust, delimiter=',')
+samples_list = []
+for row in otu_reader:
+contigs_list = row[2:len(row) - 1]  # remove last empty column
+otu_collection[row[0]] = {}  # key -> otu number
+otu_collection[row[0]]['contigs_list'] = contigs_list
+for contig in contigs_list:
+sample = contig.split('_')[0]
+samples_list.append(sample) if sample not in samples_list else samples_list
+if sample not in otu_collection[row[0]]:
+otu_collection[row[0]][sample] = {}
+otu_collection[row[0]][sample][contig] = {}
+# add read number of the contig and annotation
+if 'nb' in hits_collection[cdd_id][contig]:
+otu_collection[row[0]][sample][contig]['nb'] = hits_collection[cdd_id][contig]["nb"]
+else:
+otu_collection[row[0]][sample][contig]['nb'] = 0
+if 'taxonomy' in hits_collection[cdd_id][contig]:
+otu_collection[row[0]][sample][contig]['taxonomy'] = hits_collection[cdd_id][contig]["taxonomy"]
+else:
+otu_collection[row[0]][sample][contig]['taxonomy'] = 'unknown'
+else:
+otu_collection[row[0]][sample][contig] = {}
+# add read number of the contig and annotation
+if 'nb' in hits_collection[cdd_id][contig]:
+otu_collection[row[0]][sample][contig]['nb'] = hits_collection[cdd_id][contig]["nb"]
+else:
+otu_collection[row[0]][sample][contig]['nb'] = 0
+if 'taxonomy' in hits_collection[cdd_id][contig]:
+otu_collection[row[0]][sample][contig]['taxonomy'] = hits_collection[cdd_id][contig]["taxonomy"]
+else:
+otu_collection[row[0]][sample][contig]['taxonomy'] = 'unknown'
+if 'taxonomy' in hits_collection[cdd_id][contig]:
+otu_collection[row[0]]['global_taxonomy'] = hits_collection[cdd_id][contig]["taxonomy"]
+else:
+otu_collection[row[0]]['global_taxonomy'] = 'unknown'
+# calculate total number of reads for each sample of each OTU
+for otu in otu_collection:
+for sample in otu_collection[otu]:
+if sample not in ['contigs_list', 'global_taxonomy']:
+total_nb_read = 0
+for contig in otu_collection[otu][sample]:
+total_nb_read += int(otu_collection[otu][sample][contig]['nb'])
+otu_collection[otu][sample]['total_nb_read'] = total_nb_read
+row = 0
+column = 0
+item = '#OTU_name'
+worksheet.write(row, column, item)
+for samp in samples_list:
+column += 1
+worksheet.write(row, column, samp)
+worksheet.write(row, column + 1, 'taxonomy')
+worksheet.write(row, column + 2, 'contigs_list')
+row = 1
+# column = 0
+for otu in otu_collection:
+if isinstance(otu_collection[otu], dict):
+column = 0
+worksheet.write(row, column, otu)
+# prepare table with 0 in each cells
+for sample in otu_collection[otu]:
+column = 1
+for samp in samples_list:
+worksheet.write(row, column, 0)
+column += 1
+# fill in table with nb of read for each sample and each OTU
+for sample in otu_collection[otu]:
+column = 1
+for samp in samples_list:
+if samp == sample:
+worksheet.write(row, column, otu_collection[otu][sample]['total_nb_read'])
+column += 1
+worksheet.write(row, len(samples_list) + 1, otu_collection[otu]['global_taxonomy'].replace(';', ' '))
+worksheet.write(row, len(samples_list) + 2, ",".join(otu_collection[otu]['contigs_list']))
+row += 1
+workbook.close()
+read_file = pd.ExcelFile(file_xlsx)
+for sheet in read_file.sheet_names:
+cluster_nb_reads_file = options.output + "/" + sheet.replace(" ", "_") + "/cluster_nb_reads_files.tab"
+data_xls = pd.read_excel(file_xlsx, sheet, dtype=str, index_col=None)
+data_xls.to_csv(cluster_nb_reads_file, encoding='utf-8', index=False, sep='\t')
+def _create_html(options, hits_collection):
+"""
+Create HTML file with all results
+"""
+# create mapping file with all informations to use to create HTML report
+map_file_path = options.output + "/map.txt"
+if os.path.exists(map_file_path):
+os.remove(map_file_path)
+map_file = open(map_file_path, "w+")
+headers = ['#cdd_id', 'align_files', 'tree_files', 'cluster_files', 'cluster_nb_reads_files', 'pairwise_files', 'description', 'full_description\n']
+map_file.write("\t".join(headers))
+for cdd_id in hits_collection:
+cdd_output = hits_collection[cdd_id]["short_description"].replace(" ", "_")
+short_description = cdd_output
+file_seq_aligned = cdd_output + '/seq_aligned.final_tree.fa'
+tree_file = cdd_output + '/tree.dnd.png'
+file_cluster = cdd_output + '/otu_cluster.csv'
+file_matrix = cdd_output + "/identity_matrix.csv"
+cluster_nb_reads_files = cdd_output + "/cluster_nb_reads_files.tab"
+map_file.write(cdd_id + "\t" + file_seq_aligned + "\t" + tree_file + "\t")
+map_file.write(file_cluster + "\t" + cluster_nb_reads_files + "\t" + file_matrix + "\t")
+map_file.write(short_description + "\t" + hits_collection[cdd_id]["full_description"] + "\n")
+map_file.close()
+log.info("Writing HTML report")
+html_cmd = os.path.join(options.tool_path, 'rps2tree_html.py') + ' -m ' + map_file_path + ' -o ' + options.output
+log.debug(html_cmd)
+os.system(html_cmd)
+def _set_options():
+parser = argparse.ArgumentParser()
+parser.add_argument('-b', '--blast', help='TAB blast file from blast2ecsv module.', action='append', required=False, dest='blast', nargs='+')
+parser.add_argument('-r', '--rps', help='TAB rpsblast file from rps2ecsv module.', action='append', required=True, dest='rps', nargs='+')
+parser.add_argument('-f', '--fasta', help='FASTA file with contigs', action='append', required=True, dest='fasta', nargs='+')
+parser.add_argument('-p', '--percentage', help='Percentage similarity threshold for OTUs cutoff.', action='store', type=int, default=90, dest='perc')
+parser.add_argument('-vp', '--viral_portion', help='Minimun portion of viral sequences in RPS domain to be included.', action='store', type=float, default=0.3, dest='viral_portion')
+parser.add_argument('-mpl', '--min_protein_length', help='Minimum query protein length.', action='store', type=int, default=100, dest='min_protein_length')
+parser.add_argument('-tp', '--tool_path', help='Path to otu_seek.R', action='store', type=str, default='./', dest='tool_path')
+parser.add_argument('-o', '--out', help='The output directory', action='store', type=str, default='./Rps2tree_OTU', dest='output')
+parser.add_argument('-rgb', '--rgb-conf', help='Color palette for contigs coloration', action='store', type=str, default='rgb.txt', dest='file_rgb')
+parser.add_argument('-v', '--verbosity', help='Verbose level', action='store', type=int, choices=[1, 2, 3, 4], default=1)
+args = parser.parse_args()
+return args
+def _set_log_level(verbosity):
+if verbosity == 1:
+log_format = '%(asctime)s %(levelname)-8s %(message)s'
+log.basicConfig(level=log.INFO, format=log_format)
+elif verbosity == 3:
+log_format = '%(filename)s:%(lineno)s - %(asctime)s %(levelname)-8s %(message)s'
+log.basicConfig(level=log.DEBUG, format=log_format)
+if __name__ == "__main__":
+main()

Mercurial > repos > iuc > virannot_rps2tsv

comparison otu.py @ 0:bbaa89f070f4 draft