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309 lines
12 KiB
Python
309 lines
12 KiB
Python
# Last updated Jan 2024
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# Authors: Auden Cote-L'Heureux and Mario Ceron-Romero
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# This script chooses orthologs to concatenate OGs. This can be done as part of an end-to-end EukPhylo run,
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# or by inputting already complete alignments and gene trees and running only the concatenation step.
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# Use the --concatenate flag to run this step, and optionally use the argument --concat_target_taxa to input
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# a file containing a list of taxon codes to be included in the concatenated alignment. If a GF has more
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# than one sequence from a taxon, a representative ortholog must be chosen to include in the concatenated alignment.
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# To do this, for each taxon EukPhylo keeps only the sequences falling in the monophyletic clade in the tree
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# that contains the greatest number of species of the taxon’s minor clade (or major clade, if the ‘target taxon list’
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# uses major-clade codes). If multiple sequences from the taxon fall into this largest clade, then the sequence
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# with the highest ‘score’ (defined as length times k-mer coverage for transcriptomic data with k-mer coverage
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# in the sequence ID as formatted by rnaSpades, and otherwise just length) is kept for the concatenated alignment.
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# If a GF is not present as a taxon, its missing data are filled in with gaps in the concatenated alignment.
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# Along with the concatenated alignment, this part of the pipeline outputs individual alignments with orthologs
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# selected (and re-aligned with MAFFT), in case a user wants to construct a model-partitioned or other specialized
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# kind of species tree.
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#Dependencies
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import os, sys
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from Bio import SeqIO
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import ete3
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import argparse
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from tqdm import tqdm
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#Small utility function to extract newick strings from nexus file
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def get_newick(fname):
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newick = ''
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for line in open(fname):
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line = line.split(' ')[-1]
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if(line.startswith('(') or line.startswith('tree1=')):
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newick = line.split('tree1=')[-1].replace("'", '').replace('\\', '')
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return newick
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#This function reroots the tree on the largest Ba/Za clade. If there is no prokaryote clade,
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#it roots on the largest Op clade, then Pl, then Am, then Ex, then Sr.
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def reroot(tree):
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#This nested function returns the largest clade of a given taxonomic group
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def get_best_clade(taxon):
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best_size = 0; best_clade = []; seen_leaves = []
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#Traverse all nodes
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for node in tree.traverse('levelorder'):
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#If the node is big enough and not subsumed by a node we've already accepted
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if len(node) >= 3 and len(list(set(seen_leaves) & set([leaf.name for leaf in node]))) == 0:
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leaves = [leaf.name for leaf in node]
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#Create a record of leaves that belong to the taxonomic group
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target_leaves = set()
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for leaf in leaves[::-1]:
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if leaf[:2] in taxon:
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target_leaves.add(leaf[:10])
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leaves.remove(leaf)
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#If this clade is better than any clade we've seen before, grab it
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if len(target_leaves) > best_size and len(leaves) <= 2:
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best_clade = node
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best_size = len(target_leaves)
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seen_leaves.extend([leaf.name for leaf in node])
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return best_clade
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#Get the biggest clade for each taxonomic group (stops once it finds one)
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for taxon in [('Ba', 'Za'), ('Op'), ('Pl'), ('Am'), ('Ex'), ('Sr')]:
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clade = get_best_clade(taxon)
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if len([leaf for leaf in clade if leaf.name[:2] in taxon]) > 3:
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tree.set_outgroup( clade)
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break
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return tree
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#Function to select sequences to use per tree
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def remove_paralogs(params):
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seqs_per_og = { }
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for file in tqdm(os.listdir(params.output + '/Output/Guidance')):
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if file.split('.')[-1] in ('fasta', 'fas', 'faa'):
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prefix = '.'.join(file.split('.')[:-1])
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tre_f = [t for t in os.listdir(params.output + '/Output/Trees') if t.startswith(prefix)]
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if len(tre_f) == 0:
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tre_f = [t for t in os.listdir(params.output + '/Output/Trees') if t.startswith(prefix.split('.')[0])]
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if len(tre_f) == 0:
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tre_f = [t for t in os.listdir(params.output + '/Output/Trees') if t.startswith(file[:10])]
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if len(tre_f) == 0:
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print('\nNo tree file found for alignment ' + file + '. Skipping this gene family.\n')
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continue
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elif len(tre_f) > 1:
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print('\nMore than one tree file found matching the alignment file ' + file + '. Please make your file names unique: there should be one alignment file for every tree file, with a matching unique prefix (everything before the first "."). Skipping this gene family.\n')
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continue
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elif len(tre_f) > 1:
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print('\nMore than one tree file found matching the alignment file ' + file + '. Please make your file names unique: there should be one sequence file for every tree file, with a matching unique prefix (everything before the first "."). Skipping this gene family.\n')
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continue
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elif len(tre_f) > 1:
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print('\nMore than one tree file found matching the alignment file ' + file + '. Please make your file names unique: there should be one sequence file for every tree file, with a matching unique prefix (everything before the first "."). Skipping this gene family.\n')
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continue
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seqs_per_og.update({ file : [] })
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og_recs = { rec.id : rec for rec in SeqIO.parse(params.output + '/Output/Guidance/' + file, 'fasta')}
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newick = get_newick(params.output + '/Output/Trees/' + tre_f[0])
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tree = ete3.Tree(newick)
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try:
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tree = reroot(tree)
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except:
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print('\nUnable to re-root the tree ' + file + ' (maybe it had only 1 major clade, or an inconvenient polytomy). Skipping this step and continuing to try to grab robust clades from the tree.\n')
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#Getting a clean list of all target taxa
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if os.path.isfile(params.concat_target_taxa):
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try:
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target_codes = [l.strip() for l in open(params.concat_target_taxa).readlines() if l.strip() != '']
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except AttributeError:
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print('\n\nError: invalid "concat_target_taxa" argument. This must be a comma-separated list of any number of digits/characters to describe focal taxa (e.g. Sr_ci_S OR Am_tu), or a file with the extension .txt containing a list of complete or partial taxon codes. All sequences containing the complete/partial code will be identified as belonging to target taxa.\n\n')
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elif params.concat_target_taxa != None:
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target_codes = [params.concat_target_taxa]
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else:
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print('\nERROR: missing --concat_target_taxa argument. When concatenating, you need to give the taxonomic group (sequence prefix), groups, or a file containing a list of groups (multiple prefixes) for which to select sequences to construct a concatenated alignment\n')
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exit()
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monophyletic_clades = { }
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#Create list of relevant major/minor clades for clade grabbing
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for taxon in target_codes:
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if len(taxon) < 5 and taxon[:2] not in monophyletic_clades:
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monophyletic_clades.update({ taxon : [] })
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elif len(taxon) >= 5 and taxon[:5] not in monophyletic_clades:
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monophyletic_clades.update({ taxon[:5] : [] })
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#Grab best clades from all target groups
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seen_leaves = []
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for clade in monophyletic_clades:
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for node in tree.traverse('levelorder'):
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#If the node is big enough and not subsumed by a node we've already accepted
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if len(list(set(seen_leaves) & set([leaf.name for leaf in node]))) == 0:
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leaves = [leaf.name for leaf in node]
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#Create a record of leaves that belong to the taxonomic group
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target_leaves = set()
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for leaf in leaves[::-1]:
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if leaf[:2] == clade or leaf[:5] == clade:
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target_leaves.add(leaf[:10])
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leaves.remove(leaf)
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#If the clade is monophyletic
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if len(leaves) == 0:
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monophyletic_clades[clade].append(node)
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seen_leaves.extend([leaf.name for leaf in node])
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#Get all target taxa in the alignment
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taxa = []
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for seq in tree:
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for code in target_codes:
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if code in seq.name:
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taxa.append(seq.name[:10])
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break
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taxa = list(dict.fromkeys(taxa))
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#For each taxon, get its best sequence
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for tax in taxa:
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#Get all sequences belonging to the taxon
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taxseqs = [seq.name for seq in tree if seq.name[:10] == tax]
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score = False
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#If there's more than one sequence
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if len(taxseqs) > 1:
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clades = { }
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#Get the size of the clade in which each sequence falls (at minor clade level if available, otherwise major clade)
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if tax[:5] in monophyletic_clades:
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clades = { seq : len([leaf for clade in monophyletic_clades[tax[:5]] for leaf in clade if seq in [l.name for l in clade]]) for seq in taxseqs }
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elif tax[:2] in monophyletic_clades:
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clades = { seq : len([leaf for clade in monophyletic_clades[tax[:2]] for leaf in clade if seq in [l.name for l in clade]]) for seq in taxseqs }
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#If there's more than one sequence that falls in a robust clade
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if len(clades) > 0:
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#Filter the list of sequences to those that fall in clades
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taxseqs = [seq for seq in taxseqs if seq in clades]
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#Get the largest clade in which a sequence from the taxon falls
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best_size = max(list(clades.values()))
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#Get a list of sequences in a clade of that size
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best_seqs = [seq for seq in taxseqs if clades[seq] == best_size]
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#If there is only one sequence in the best-sized clade, take it and finish
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if len(best_seqs) == 1:
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seqs_per_og[file].append(og_recs[best_seqs[0]])
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#Otherwise, need to take the sequence with the best score that falls into a clade of that size
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else:
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taxseqs = best_seqs
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score = True
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#Otherwise, of all sequences that don't fall in any clade, take the one with the best score
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else:
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score = True
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#If there's only one sequence for the taxon, no problem
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elif len(taxseqs) == 1:
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seqs_per_og[file].append(og_recs[taxseqs[0]])
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#If scoring is necessary, do it on the filter set of sequences for the taxon and keep the best
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if score:
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use_cov = True
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for seq in taxseqs:
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if 'Cov' not in seq[10:]:
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use_cov = False
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break
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if use_cov:
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taxseqs = sorted(taxseqs, key = lambda x : -len(og_recs[x].seq.replace('-', '')) * float(x.split('Cov')[-1].split('_')[0]))
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else:
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taxseqs = sorted(taxseqs, key = lambda x : -len(og_recs[x].seq.replace('-', '')))
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seqs_per_og[file].append(og_recs[taxseqs[0]])
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return seqs_per_og
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#Function to concatenate all the selected sequences into one alignment file
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def concat(seqs_per_og, params):
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taxa = list(dict.fromkeys([rec.id[:10] for og in seqs_per_og for rec in seqs_per_og[og]]))
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seqs_per_og = { og : { rec.id : str(rec.seq).replace('-', '') for rec in seqs_per_og[og] } for og in seqs_per_og }
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if not os.path.isdir(params.output + '/Output/DataToConcatenate'):
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os.mkdir(params.output + '/Output/DataToConcatenate')
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os.mkdir(params.output + '/Output/DataToConcatenate/Unaligned')
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os.mkdir(params.output + '/Output/DataToConcatenate/Aligned')
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for og in seqs_per_og:
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with open(params.output + '/Output/DataToConcatenate/Unaligned/' + '.'.join(og.split('.')[:-1]) + '_TargetTaxaUnaligned.fasta', 'w') as o:
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for tax in seqs_per_og[og]:
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o.write('>' + tax + '\n' + seqs_per_og[og][tax] + '\n\n')
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os.system('mafft ' + params.output + '/Output/DataToConcatenate/Unaligned/' + '.'.join(og.split('.')[:-1]) + '_TargetTaxaUnaligned.fasta > ' + params.output + '/Output/DataToConcatenate/Aligned/' + '.'.join(og.split('.')[:-1]) + '_TargetTaxaAligned.fasta')
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seqs_per_og[og] = { rec.id[:10] : str(rec.seq) for rec in SeqIO.parse(params.output + '/Output/DataToConcatenate/Aligned/' + '.'.join(og.split('.')[:-1]) + '_TargetTaxaAligned.fasta', 'fasta') }
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concat_seqs_per_tax = { tax : '' for tax in taxa }
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for taxon in taxa:
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for og in seqs_per_og:
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if taxon in seqs_per_og[og]:
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concat_seqs_per_tax[taxon] += seqs_per_og[og][taxon]
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else:
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print(list(seqs_per_og[og].values()))
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print(og)
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concat_seqs_per_tax[taxon] += ''.join(['-' for i in range(len(list(seqs_per_og[og].values())[0]))])
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with open(params.output + '/Output/ConcatenatedAlignment.fasta', 'w') as o:
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for tax in concat_seqs_per_tax:
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o.write('>' + tax + '\n' + concat_seqs_per_tax[tax] + '\n\n')
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#wrapper
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def run(params):
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if not os.path.isdir(params.output + '/Output/Guidance') or not os.path.isdir(params.output + '/Output/Trees'):
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print('\nERROR in concatenation: cannot find alignments and/or trees (looking in ' + params.output + '/Output/Guidance and ' + params.output + '/Output/Trees)')
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exit()
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else:
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seqs_per_og = remove_paralogs(params)
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concat(seqs_per_og, params)
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