#!/usr/bin/env python
# ***************************************************************
# Name:      COG_table.py
# Purpose:   This script integrates with PROKKA or Prodigal and generates Cogs assignments for the protein sequences.
# Version:   0.1
# Authors:   Umer Zeeshan Ijaz (Umer.Ijaz@glasgow.ac.uk)
#                 http://userweb.eng.gla.ac.uk/umer.ijaz
# Created:   2014-01-11
# License:   Copyright (c) 2014 Computational Microbial Genomics Group, University of Glasgow, UK
#            This program is free software: you can redistribute it and/or modify
#            it under the terms of the GNU General Public License as published by
#            the Free Software Foundation, either version 3 of the License, or
#            (at your option) any later version.
#            This program is distributed in the hope that it will be useful,
#            but WITHOUT ANY WARRANTY; without even the implied warranty of
#            GNU General Public License for more details.
#            You should have received a copy of the GNU General Public License
#            along with this program.  If not, see <http://www.gnu.org/licenses/>.
# **************************************************************/
import sys
from BCBio import GFF
import argparse
from Bio import Entrez
from collections import defaultdict
def get_records_from_cdd(queries, email):
    # We need CDD accession to COG accession mapping. For this we will use NCBI eutils and parse the returned XML
    # file. For example,
    # 	http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=cdd&id=223855
    # returns the following XML record
    #	<eSummaryResult>
    #		<DocSum>
    #			<Id>223855</Id>
    #			<Item Name="Accession" Type="String">COG0784</Item>
    #			<Item Name="Title" Type="String">CheY</Item>
    #			<Item Name="Abstract" Type="String">FOG: CheY-like receiver [Signal transduction mechanisms]</Item>
    #			<Item Name="Status" Type="Integer">0</Item>
    #			<Item Name="LivePssmID" Type="Integer">0</Item>
    #		</DocSum>
    #	</eSummaryResult>
    Entrez.email = email # Always tell ncbi who you are.
    search_result = Entrez.read(Entrez.epost("cdd", id=",".join(queries)))
    records = Entrez.read(Entrez.efetch(db="cdd",
    return records
def usage():
    return '\n'.join([
           'Example usage:',
           '\tStep 1: Run PROKKA_XXXXXXXX.faa with rpsblast against the  Cog database',
	   '\twith following format:',    
           '\t\t\trpsblast -query PROKKA_XXXXXXXX.faa -db Cog -evalue 0.00001', 
           '\t\t\t-outfmt \"6 qseqid sseqid evalue pident score qstart qend', 
           '\t\t\tsstart send length slen\" -out blast_output.out',
           '\tStep 2: Run this script to generate the table with marker gene abundance per cluster.:',
           '\t\t\t./COG_table.py -g PROKKA_XXXXXXXX.gff -b blast_output.out -e mail@example.com',
           '\t\t\t -c clustering_gt1000.csv -m marker_genes.txt > scg_table.tsv',
           'Refer to rpsblast tutorial: http://www2.warwick.ac.uk/fac/sci/moac/people/students/peter_cock/python/rpsblast/',
def read_blast_output(blastoutfile): 
    sseq_ids = []
    records = []
    with open(blastoutfile) as in_handle:
        for line in in_handle:
            line_items = line.split("\t")
            qseq = line_items[0]
            sseq = line_items[1]
            pident = line_items[3]
            send = line_items[7]
            sstart = line_items[8]
            slen = line_items[10]
            records.append({'qseqid': qseq,
                            'sseqid': sseq,
                            'pident': float(pident),
                            'send': float(send),
                            'sstart': float(sstart),
                            'slen': float(slen)})
    return records, sseq_ids
def read_gff_file(gfffile):
    with open(gfffile) as in_handle:
        for rec in GFF.parse(in_handle, limit_info=limits):
            for feature in rec.features:
                featureid_locations[feature.id] = rec.id
    return featureid_locations
def read_markers_file(marker_file):
    # Stores each line of marker_file as an item in a list
    with open(marker_file) as mf:
        return [l.strip() for l in mf.readlines()]
def read_clustering_file(cluster_file):
    # Returns the cluster names and the contig names per cluster
    contigs_per_cluster = defaultdict(list)
    clusters = set()
    with open(cluster_file) as cf:
        for line in cf.readlines():
            line_items = line.strip().split(',')
            cluster = line_items[1]
            contig = line_items[0]
    return list(clusters), contigs_per_cluster
def main(args):
    RPSBLAST_SCOVS_THRESHOLD = args.scovs_threshold
    RPSBLAST_PIDENT_THRESHOLD = args.pident_threshold
    records, sseq_ids = read_blast_output(args.blastoutfile)
    # Retrieve the cog accession number from ncbi
    cogrecords_l = get_records_from_cdd(sseq_ids, args.email)
    cogrecords = {}
    for rec in cogrecords_l:
        cogrecords[rec['Id']] = rec
    # If a gff file is given, the contig ids will be fetched from this.
    if args.gfffile:
        featureid_locations = read_gff_file(args.gfffile)
    features_per_contig = defaultdict(list)
    for record_d in records:
        pident_above_threshold = record_d['pident'] >= RPSBLAST_PIDENT_THRESHOLD
        # A certain fraction of the cog should be covered to avoid the same cog 
        # to be counted twice in the case when a cog is split across two or more contigs.
        alignment_length_in_subject = abs(record_d['send'] - record_d['sstart']) + 1
        percent_seq_covered = (alignment_length_in_subject / record_d['slen']) * 100.0
        seq_cov_above_threshold =  percent_seq_covered >= RPSBLAST_SCOVS_THRESHOLD
        if pident_above_threshold and seq_cov_above_threshold:
            cog_accession = cogrecords[record_d['sseqid'].split('|')[2]]['Accession']
            if args.gfffile:
                contig = featureid_locations[record_d['qseqid']]
                contig = "".join(record_d['qseqid'].split(args.separator)[:-1])
    # Load clustering
    clusters, contigs_per_cluster = read_clustering_file(args.cluster_file)
    # Load markers
    markers = read_markers_file(args.marker_file)
    # print header
    print "\t".join(["Cluster", "Contigs", "Num_contigs"] + markers)
    # Per cluster, count the number of features
    for cluster in clusters:
        contigs = contigs_per_cluster[cluster]
        counts = [cluster, "|".join(contigs), str(len(contigs))]
        for marker in markers:
            count = 0
            for contig in contigs:
                for feature in features_per_contig[contig]:
                    if feature == marker:
                        count += 1
        print "\t".join(counts)
if __name__ == "__main__":
   parser = argparse.ArgumentParser(usage=usage())
   parser.add_argument('-b', '--blastoutfile', required=True,
           help=('Output of rpsblast run, assumed to be in tabular format whith '
               'columns: qseqid sseqid evalue pident score qstart qend sstart send length slen. '
               'The contigs ids are assumed to be recoverable by removing the last underscore '
               'and the characters following it from the qseqid column.' ))
   parser.add_argument('-g', '--gfffile',
           help=('GFF file generated by e.g. prodigal '
           'only needed if the contig names are not recoverable from the '
           'blast output file.'))
   parser.add_argument('-c', '--cluster_file', required=True,
           help=('Clustering file from concoct execution.'))
   parser.add_argument('-m', '--marker_file', required=True,
           help=('File containing a list of genes that will be used as marker genes'))
   parser.add_argument('-s', '--scovs-threshold', type=float, default=50.0,
           help='Threshold covered in percent, default=50.0')
   parser.add_argument('-p', '--pident-threshold', type=float, default=0.0,
           help='Threshold identity in percent, default=0.0')
   parser.add_argument('-e', '--email',
           help='Email adress needed to fetch data through ncbi api')
   parser.add_argument('--separator', default="_",
           help=('Character that is used to separate the contig id from the '
                 'protein identifier. Everything before the last occurence ' 
                 'of this character will be used as the contig id. Default '
                 'value is "_"'))
   args = parser.parse_args()