Initial commit

README.md

+# MBMA
+
+A Mapping Based Microbiome Analysis tool for species and resistance genes quantification
+
+## About
+  A main challenge in the field of metagenomics is to quickly and precisely determine the composition and the relative abundance of constituents of a microbial community. State-of-the-art metagenomic methods rely on mapping-, kmer- or assembly- based  approaches for identification and quantification. However, currently available methods are either time-consuming, less sensitive and generates false positives, which leads to a lack of accuracy in quantification at gene and strain levels. 
+
+  To overcome these limitations, we developed Mapping Based Microbiome Analysis (MBMA), a mapping-based approach for identification and quantification of species, strains and resistance genes, with three main innovations, the use of : <br />
+- a **efficient and discriminatory database** for rapid quantification, <br />
+- an **advanced counting method** to decrease the false discovery rate, <br />
+- combined with **variant calling** from samples sequences for an accurate abundance prediction.
+
+**MBMA** identifies and quantifies constituents (species, resistance genes) from metagenomic samples by mapping reads against a database and performing variant calling. Other constituents can be quantifies by changing the database. It has 3 way of working :
+- **mapping**, it simply map reads against an indexed reference database, using different mapping tools (bowtie2, bwa and novoalign) and different counting methods (best, ex-aequo and shared), for non redundant databases. 
+- **variant**, it performs, in addition to the mapping step, a variant calling step. Reads are mapped against a clustered database, and then variant calling using GATK is performed, to able an accurate quantification at a gene level.
+- **mode**, it provide two presets modes for the identification of species (option : --species) and resistance genes (option : --resistance). For bacterial species, reads are mapped reads against *RefMG.v1*, and for resistance genes, against *ResFinder*. 
+
+## Version
+1.0
+
+## Requirements
+
+## Usage
+
+
+## Examples
+
+

Utils/Rscript/Draw_barplot.R

+library(ggplot2)
+
+# Lecture des arguments en ligne de commande
+filename = "../data/mock/perc_especes_mdg.txt"
+
+# Lecture des fichiers
+d = read.table(filename, header=T ,sep='\t', stringsAsFactors = F)
+
+new_d=c()
+Ech = unique(d$Echantillon)
+for(i in Ech){
+  sample = d[d$Echantillon == i,]
+  sample = sample[order(-sample$Percentage),]
+  new_d=rbind(new_d,sample)
+}
+
+## species
+ggplot(new_d,aes(x=new_d$Echantillon,y=new_d$Percentage))+
+geom_bar(aes(fill=new_d$Species, width=0.75),colour="white",stat="identity")+
+ylab("Pourcentage") +
+xlab("Echantillon") +
+scale_y_continuous(labels = scales::percent) +
+theme(strip.text=element_text(size=14, face='bold'),
+      legend.position="None",
+      axis.text.x = element_text(angle = 90, hjust=1),
+      axis.title = element_text(size = 15, face ='bold'),
+      legend.title = element_text(face = 'bold', size = 12))
+
+#### genes de resistance
+ggplot(new_d,aes(x=new_d$Echantillon,y=new_d$Percentage))+
+  geom_bar(aes(fill=new_d$Resistance.genes, width=0.75),colour="white",stat="identity")+
+  ylab("Relative abundance") +
+  xlab("Sample") +
+  scale_y_continuous(labels = scales::percent) +
+  theme(strip.text=element_text(size=14, face='bold'),
+        legend.position="None",
+        axis.text.x = element_text(angle = 90, hjust=1),
+        axis.title = element_text(size = 15, face ='bold'),
+        legend.title = element_text(face = 'bold', size = 12))
+
+write.table(new_d, file="resist_krem_ordonné.txt", row.names=FALSE, quote=FALSE, sep='\t', col.names = colnames(new_d))
+
+
+## calcul de la divergence de KL
+
+# function
+KLtest = function(x1,x2)
+{
+  res = 0
+  x1 = x1/sum(x1)
+  x2 = x2/sum(x2)
+  for(i in 1:length(x1)){
+  if(x1[i] != 0 && x2[i] != 0)
+  {
+    res = res + x1[i]*log(x1[i]/x2[i])
+    
+  }
+  }
+  return(res)
+}
+
+#value = KLtest(percentage1, percentage2)
\ No newline at end of file

Utils/Rscript/Resistance/ResFinder_Count2perc_nbcount.R

+
+library("optparse")
+
+option_list = list(
+  make_option(c("-f", "--file"), type = "character", default=NULL, 
+              help="count matrice file name", metavar="character"),
+  make_option(c("-l","--label"), type = "character", default = "MBMA",
+              help = "label for the analysis [default= %default]", metavar = "character"),
+  make_option(c("-o", "--out"), type="character", default="out.txt", 
+              help="output file name [default= %default]", metavar="character")
+); 
+
+opt_parser = OptionParser(option_list=option_list);
+opt = parse_args(opt_parser);
+
+if (is.null(opt$file)){
+  print_help(opt_parser)
+  stop("At least one argument must be supplied (input file)", call.=FALSE)
+}
+
+# Lecture des fichiers
+matrix = read.table(opt$file, header=T ,sep='\t', stringsAsFactors = F)
+
+# Récupération du taxID des souches
+geneID = NULL
+for(id in matrix$ref_genome) {
+  geneID = c(geneID, unlist(strsplit(id, "_"))[1])
+}
+
+# ajout du taxID et des noms de chaque espèces 
+matrix = cbind(geneID, matrix)
+
+head = c("Echantillon", "Program", "Resistance genes", "Percentage", "Rpkm", "Nb mapped reads")
+echantillon = c()
+resistance = c()
+percentage = c()
+rpkm_ech = c()
+mapped_ech = c()
+
+col = colnames(matrix)
+       
+for(i in 4:length(col)){
+  sample = data.frame(matrix$geneID, matrix[i])
+  colnames(sample) = c("geneID", "count")
+  rpkm = (sample$count * (10**9))/(matrix$Size * as.numeric(sum(sample$count)))
+  sample = cbind(sample, rpkm)
+  sample = aggregate(cbind(rpkm, count) ~ geneID, sample, FUN=sum)
+  perc = round(sample$rpkm/sum(sample$rpkm),3)
+  sample = cbind(sample, perc)
+  sample = sample[sample$perc != 0,]
+ 
+#  sample_name = unlist(strsplit(col[i],"[.]|_"))[1]
+  echantillon = c(echantillon,rep(col[i], dim(sample)[1]))
+  resistance = c(resistance,as.character(sample$geneID))
+  percentage = c(percentage, sample$perc)
+  rpkm_ech = c(rpkm_ech, sample$rpkm)
+  mapped_ech = c(mapped_ech, sample$count)
+}
+
+mod = rep(opt$label,length(echantillon))
+df = data.frame(echantillon, mod, resistance, percentage, rpkm_ech, mapped_ech)
+write.table(df, file=opt$out, row.names=FALSE, quote=FALSE, sep='\t', col.names = head)

Utils/Rscript/Species/RefMG_Count2perc_nbcount.R

+# # Lecture des arguments en ligne de commande
+
+library("optparse")
+
+option_list = list(
+  make_option(c("-f", "--file"), type = "character", default=NULL, 
+              help="count matrice file name", metavar="character"),
+  make_option(c("-r", "--reference_db"), type="character", default=NULL,
+              help="dabases genes informations", metavar = "character"),
+  make_option(c("-l","--label"), type = "character", default = "MBMA",
+              help = "label for the analysis [default= %default]", metavar = "character"),
+  make_option(c("-o", "--out"), type="character", default="out.txt", 
+              help="output file name [default= %default]", metavar="character")
+); 
+
+opt_parser = OptionParser(option_list=option_list);
+opt = parse_args(opt_parser);
+
+if (is.null(opt$file)){
+  print_help(opt_parser)
+  stop("At least two argument must be supplied (input file & reference file)", call.=FALSE)
+}
+
+# Lecture des fichiers
+matrix = read.table(opt$file, header=T ,sep='\t', stringsAsFactors = F)
+ref=read.table(opt$reference_db,header=T, sep='\t')
+
+# Récupération du taxID des souches
+TaxID = NULL
+for(id in matrix$ref_genome) {
+  TaxID = c(TaxID, unlist(strsplit(id, "[.]"))[1])
+}
+
+# Attribution des noms de chaque espèces au TaxID
+name = NULL
+for(id in TaxID){
+  species = ref[which(ref$TaxID == id),]$Species
+  name=c(name, as.character(species))
+}
+
+# ajout du taxID et des noms de chaque espèces 
+matrix = cbind(TaxID, name, matrix)
+
+head = c("Echantillon", "Program", "Species", "Percentage", "Rpkm", "Nb mapped reads")
+echantillon = c()
+species = c()
+percentage = c()
+rpkm_ech = c()
+mapped_ech = c()
+
+col = colnames(matrix)
+       
+for(i in 5:length(col)){
+  sample = data.frame(matrix$name, matrix$TaxID, matrix[i])
+  colnames(sample) = c("reference", "TaxID", "count")
+  rpkm = (sample$count * (10**9))/(matrix$Size * as.numeric(sum(sample$count)))
+  sample = cbind(sample, rpkm)
+  sample = aggregate(cbind(rpkm, count) ~ TaxID + reference, sample, FUN=function(x) c(mn=mean(x), s=sum(x) ))
+  sample = aggregate(cbind(rpkm[,1], count[,2]) ~ reference, sample, sum)
+  perc = round(sample$V1/sum(sample$V1),3)
+  sample = cbind(sample, perc)
+  sample = sample[sample$perc != 0,]
+ 
+#  sample_name = unlist(strsplit(col[i],"[.]|_"))[1]
+  echantillon = c(echantillon,rep(col[i], dim(sample)[1]))
+  species = c(species,as.character(sample$reference))
+  percentage = c(percentage, sample$perc)
+  rpkm_ech = c(rpkm_ech, sample$V1)
+  mapped_ech = c(mapped_ech, sample$V2)
+}
+
+mod = rep(opt$label,length(echantillon))
+df = data.frame(echantillon, mod, species, percentage, rpkm_ech, mapped_ech)
+write.table(df, file=opt$out, row.names=FALSE, quote=FALSE, sep='\t', col.names = head)

Utils/cluster2multifasta.py

+#! /usr/bin/env python
+# -*- coding: utf8 -*-
+
+
+__author__ = "Anita Annamalé"
+__version__  = "1.0"
+__copyright__ = "copyleft"
+__date__ = "2016/05"
+
+#-------------------------- MODULES IMPORTATION -------------------------------#
+
+import sys
+import os
+
+#-------------------------- FUNCTIONS DEFINITION ------------------------------#
+
+def parse_cltr(filename):
+    """
+    Function that parse a cluster information file from CDHIT-est
+    
+    Args : 
+        filename [STR] = cluster informations file name
+    
+    Returns:
+        annot [DICT] = contains gene as key and his cluster name as value
+        clstr_vs_ref [DICT] = contains cluster name as key and cluster 
+                              representative gene as value
+    """
+    # initialize
+    annot = {}
+    clstr_vs_ref = {}
+    
+    with open(filename, "rt") as infile:
+        for line in infile:
+            if line.startswith('>Cluster '):
+                clst_nb = line.rstrip()
+                clst_nb = clst_nb.replace(" ", "") # number of the cluster
+            else :
+                genes_info = line.split(" ")
+                # annotate each gene to a cluster
+                annot[genes_info[1][:-3]] = clst_nb[1:]
+                # if gene is the representative gene of the cluster
+                if genes_info[2] == '*\n':
+                    # annotate each cluster to his reference sequence
+                    clstr_vs_ref[clst_nb[1:]] = genes_info[1][1:-3]
+                    
+    return annot, clstr_vs_ref
+
+
+def create_multifasta(fasta_file, annot, clstr_vs_ref, outdir):
+    """
+    Function that create multifasta file in th provided directory (outdir).
+    Multifasta files are named by the clusters representatives name, and 
+    contains all the genes that are similar to the representatives.
+    
+    Args:
+        fasta_file [STR] = fasta file of the un clusterized database
+        annot [DICT] = contains gene as key and his cluster name as value
+        clstr_vs_ref [DICT] = contains cluster name as key and cluster 
+                              representative gene as value
+        outdir [STR] = output directory name
+    
+    No return
+    """
+    # initialize
+    fasta_data = {}
+    
+    # open the database fasta file and get all information
+    with open(fasta_file, "rt") as multifasta:
+        for line in multifasta:
+            if line.startswith('>'):
+                name = line.rstrip()  # get gene name
+                clstr_name = annot[name] # get gene cluster name
+                ref_name = clstr_vs_ref[clstr_name]  # get cluster 
+                                                     # representative gene name
+            # append to the fasta dictionnary 
+            fasta_data.setdefault(ref_name, []).append(line)
+                                                            
+    # write all the multifasta files
+    for element in fasta_data: # for representative
+        with open('{0}/{1}.fa'.format(outdir,element), "wt") as output:
+            output.write("".join(fasta_data[element])) # write sequences
+    
+
+#--------------------------------- MAIN ---------------------------------------#
+
+if __name__ == '__main__' :
+    
+    # command-line parsing
+    if not len(sys.argv) == 4:
+        msg = "usage: python cluster2multifasta.py <file.clstr> <file.fasta> \
+<fasta_dir>"
+        print msg 
+        exit(1)
+        
+    # get absolute path
+    clstr_name = os.path.abspath(sys.argv[1])
+    fasta_name = os.path.abspath(sys.argv[2])
+    dir_name = os.path.abspath(sys.argv[3])
+    
+    # parse the cluster information file from CDHIT
+    annotate_gene, get_ref = parse_cltr(clstr_name)
+    
+    # create a repertory in the working directory
+    os.mkdir(dir_name)
+    
+    # create multifasta files
+    create_multifasta(fasta_name, annotate_gene, get_ref, dir_name)
+    
+    
+    
+    

Utils/jobs_make_matrices.sh

+#!/bin/sh
+
+#$ -S /bin/bash
+#$ -M {email}
+#$ -m bea
+#$ -q {queue}
+#$ -N {jobname}
+#$ -j y
+#$ -o {stdout}
+
+### Import your modules
+module add Python/2.7.8 java/1.8.0 cd-hit/v4.6.1 muscle/3.8.31 
+
+###PROGRAMS
+# clustering
+cd-hit-est -i {database} -c {id_threshold} -aS 0.9 -d 0 -g 1 -r 1 -o {clustered_db}
+
+# create multifasta
+python {script_loc}/cluster2multifasta.py {clustered_db}.clstr {database} {clstr_fasta}
+
+# filter only multifasta
+mkdir {clstr_fasta}/cluster
+for file in {clstr_fasta}/*.fa; do a=`grep '>' $file | wc -l`; if [ ! $a == 1 ] ; then mv $file {clstr_fasta}/cluster/ ; fi; done;
+
+# alignement
+mkdir {output}/alignment
+for file in {clstr_fasta}/cluster/*.fa; do muscle -in $file -out {output}/alignment/`basename $file` -clwstrict; done;
+for file in {output}/alignment/*.fa; do mv $file  ${{file%fa}}aln; done;
+
+# VCF calling
+mkdir {output}/vcf_db
+for file in {output}/alignment/*.aln ; do {script_loc}/tools/jvarkit/dist/msa2vcf $file -c `basename ${{file%.aln}}` -R `basename ${{file%.aln}}` -o {output}/vcf_db/`basename ${{file%aln}}`vcf ; done;
+

Utils/make_matrices.py

+#! /usr/bin/env python
+# -*- coding: utf8 -*-
+
+"""
+"""
+
+__author__ = "Anita Annamalé"
+__version__  = "2.0"
+__copyright__ = "copyleft"
+__date__ = "2016/07"
+
+#-------------------------- MODULES IMPORTATION -------------------------------#
+
+import sys
+import argparse
+import os
+import subprocess
+import shlex
+
+#-------------------------- FUNCTIONS DEFINITION ------------------------------#
+
+def is_fasta(path):
+    """
+    Check if a path is an existing FASTA file.
+
+    Args:
+        path [STR] = Path to the file
+
+    Returns:
+        abs_path [STR] = absolute path
+        or quit
+    """
+    abs_path = os.path.abspath(path) # get absolute path
+
+    # if not a file
+    if not os.path.isfile(abs_path):
+        # check if it is a path to a dir
+        if os.path.isdir(abs_path):
+            msg = "{0} is a directory not a file.".format(abs_path)
+        # else the path doesn't not exist
+        else:
+            msg = "The path {0} does not exist.".format(abs_path)
+        raise argparse.ArgumentTypeError(msg)
+
+    else :
+        ext = os.path.splitext(abs_path)[1] # get the file extension
+        if (ext != 'fa') or (ext != 'fasta'):
+            msg = ("{0} isn't a fasta file. "
+                  "Please, provide a database in FASTA format with extension \
+.fasta or .fa".format(abs_path))
+
+    return abs_path
+    
+def is_dir(path):
+    """
+    Check if a given path is an existing directory.
+
+    Args:
+        path [string] = Path to the directory
+
+    Returns:
+        abs_path [string] = absolute path
+        or quit
+    """
+    abs_path = os.path.abspath(path) # get absolute path
+
+    # if not a directory
+    if not os.path.isdir(abs_path):
+        # check if it is a path to a file
+        if os.path.isfile(abs_path):
+            msg = "{0} is a file not a directory.".format(abs_path)
+        # else the path doesn't not exist
+        else:
+            msg = "The path {0} does not exist.".format(abs_path)
+        raise argparse.ArgumentTypeError(msg)
+
+    return abs_path
+
+
+def write_soum(template, param):
+    """
+    Write the qsub submission script.
+
+    Args:
+        param [dict] = containing all information needed for the submission 
+                       script creation
+    
+    No returns
+    """
+    # read the template
+    filein = open(template, "rt")
+    temp = filein.read()
+    # Write the submission script
+    with open("{0}/submission.sh".format(param['output']), "wt") as out:
+        out.write(temp.format(**param))
+    # close the template
+    filein.close()
+    
+
+#---------------------------- PROGRAMME MAIN ----------------------------------#
+
+if __name__ == '__main__' :
+    
+    parser = argparse.ArgumentParser(description = "VCF matrices generation \
+program ")
+    
+    parser.add_argument('database', 
+                        metavar = 'database',
+                        type = is_fasta,
+                        help = "input database in fasta format to clusterize\n")
+