# HG changeset patch # User gregory-minevich # Date 1340654908 14400 # Node ID 1bee9742af9ba2c33556f06b993b3e70b5e2a52c # Parent 6d2dbdfa11e33094717308ab2b29b97c29dcda3a Deleted selected files diff -r 6d2dbdfa11e3 -r 1bee9742af9b EMS_VariantDensityMapping.py --- a/EMS_VariantDensityMapping.py Thu Jun 14 21:22:51 2012 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,126 +0,0 @@ -#!/usr/bin/python - -import re -import sys -import optparse -import csv -from rpy import * - -def main(): - parser = optparse.OptionParser() - parser.add_option('-s', '--snp_vcf', dest = 'snp_vcf', action = 'store', type = 'string', default = None, help = "VCF of SNPs") - parser.add_option('-c', '--hist_color', dest = 'hist_color', action = 'store', type = 'string', default = "darkgray", help = "Color for 1Mb histograms") - parser.add_option('-y', '--ylim', dest = 'ylim', action = 'store', type = 'int', default= 100, help = "Upper limit of Y axis") - parser.add_option('-z', '--standardize', dest = 'standardize', default= 'false', help = "Standardize X-axis") - parser.add_option('-e', '--ems', dest = 'ems', default= 'false', help = "Whether EMS variants should be filtered for") - parser.add_option('-o', '--output', dest = 'plot_output', action = 'store', type = 'string', default = 'EMS_Variant_Density_Plot.pdf', help = "Output file name of plot") - (options, args) = parser.parse_args() - - - i, ii, iii, iv, v, x = parse_snp_vcf(snp_vcf = options.snp_vcf, ems=options.ems) - create_histograms(plot_output = options.plot_output, hist_color=options.hist_color, ylim=options.ylim, ems=options.ems, standardize=options.standardize, i = i, ii = ii, iii = iii, iv = iv, v = v, x = x) - -def create_histograms(plot_output = None, hist_color=None, ylim=None, ems=None, standardize=None , i = None, ii = None, iii = None, iv = None, v = None, x = None): - breaks = { 'I' : 16 , 'II' : 16, 'III' : 14, 'IV' : 18, 'V' : 21, 'X' : 18 } - - try: - r.pdf(plot_output, 8, 8) - if len(i) > 0: - plot_data(position_list = i, chr = "I", breaks = breaks["I"], hist_color=hist_color, ylim=ylim, ems=ems, standardize=standardize) - if len(ii) > 0: - plot_data(position_list = ii, chr = "II", breaks = breaks["II"], hist_color=hist_color, ylim=ylim, ems=ems, standardize=standardize) - if len(iii) > 0: - plot_data(position_list = iii, chr = "III", breaks = breaks["III"], hist_color=hist_color, ylim=ylim, ems=ems, standardize=standardize) - if len(iv) > 0: - plot_data(position_list = iv, chr = "IV", breaks = breaks["IV"], hist_color=hist_color, ylim=ylim, ems=ems, standardize=standardize) - if len(v) > 0: - plot_data(position_list = v, chr = "V", breaks = breaks["V"], hist_color=hist_color, ylim=ylim, ems=ems, standardize=standardize) - if len(x) > 0: - plot_data(position_list = x, chr = "X", breaks = breaks["X"], hist_color=hist_color, ylim=ylim, ems=ems, standardize=standardize) - r.dev_off() - except Exception as inst: - print inst - print "There was an error creating the plot pdf... Please try again" - -def parse_snp_vcf(snp_vcf = None, ems=None): - i_file = open(snp_vcf, 'rU') - reader = csv.reader(i_file, delimiter = '\t', quoting = csv.QUOTE_NONE) - - skip_headers(reader = reader, i_file = i_file) - - i_position_list = [] - ii_position_list = [] - iii_position_list = [] - iv_position_list = [] - v_position_list = [] - x_position_list = [] - - for row in reader: - chromosome = row[0].upper() - chromosome = re.sub("chr", "", chromosome, flags = re.IGNORECASE) - chromosome = re.sub("CHROMOSOME_", "", chromosome, flags = re.IGNORECASE) - - position = row[1] - ref_allele = row[3] - alt_allele = row[4] - - if (ems=='true'): - if (ref_allele =="G" or ref_allele =="C") and (alt_allele =="A" or alt_allele =="T"): - if chromosome == "I": - i_position_list.append(position) - elif chromosome == "II": - ii_position_list.append(position) - elif chromosome == "III": - iii_position_list.append(position) - elif chromosome == "IV": - iv_position_list.append(position) - elif chromosome == "V": - v_position_list.append(position) - elif chromosome == "X": - x_position_list.append(position) - elif (ems=='false'): - if chromosome == "I": - i_position_list.append(position) - elif chromosome == "II": - ii_position_list.append(position) - elif chromosome == "III": - iii_position_list.append(position) - elif chromosome == "IV": - iv_position_list.append(position) - elif chromosome == "V": - v_position_list.append(position) - elif chromosome == "X": - x_position_list.append(position) - - return i_position_list, ii_position_list, iii_position_list, iv_position_list, v_position_list, x_position_list - -def skip_headers(reader = None, i_file = None): - # count headers - comment = 0 - while reader.next()[0].startswith('#'): - comment = comment + 1 - - # skip headers - i_file.seek(0) - for i in range(0, comment): - reader.next() - -def plot_data(position_list = None, chr = None, breaks = None, hist_color=None, ylim = None, ems=None, standardize=None): - positions = ",".join(map(str, map(lambda x: float(x) / 1000000, position_list))) - positions = "c(" + positions + ")" - - if (standardize=='true'): - r("hist(" + positions + ", xlim=c(0,21), ylim=c(0, %d "%ylim +"),col='"+ hist_color + "', breaks = seq(0, as.integer( ' " + str(breaks) + " '), by=1), main = 'LG " + chr + "', ylab = 'Frequency Of SNPs', xlab = 'Location (Mb)')") - r("hist(" + positions + ", xlim=c(0,21), add=TRUE, ylim=c(0, %d "%ylim +"), col=rgb(1, 0, 0, 1), breaks = seq(0, as.integer( ' " + str(breaks) + " '), by=.5), main = 'Chr " + chr + "', ylab = 'Number Of SNPs', xlab = 'Location (Mb)')") - r("axis(1, at=seq(0, 21, by=1), labels=FALSE, tcl=-0.5)") - r("axis(1, at=seq(0, 21, by=0.5), labels=FALSE, tcl=-0.25)") - elif (standardize=='false'): - r("hist(" + positions + ", xlim=c(0,as.integer( ' " + str(breaks) + " ')), ylim=c(0, %d "%ylim +"),col='"+ hist_color + "', breaks = seq(0, as.integer( ' " + str(breaks) + " '), by=1), main = 'LG " + chr + "', ylab = 'Frequency Of SNPs', xlab = 'Location (Mb)')") - r("hist(" + positions + ", xlim=c(0,as.integer( ' " + str(breaks) + " ')), add=TRUE, ylim=c(0, %d "%ylim +"), col=rgb(1, 0, 0, 1), breaks = seq(0, as.integer( ' " + str(breaks) + " '), by=.5), main = 'Chr " + chr + "', ylab = 'Number Of SNPs', xlab = 'Location (Mb)')") - r("axis(1, at=seq(0, as.integer( ' " + str(breaks) + " '), by=1), labels=FALSE, tcl=-0.5)") - r("axis(1, at=seq(0, as.integer( ' " + str(breaks) + " '), by=0.5), labels=FALSE, tcl=-0.25)") - - - -if __name__ == "__main__": - main() diff -r 6d2dbdfa11e3 -r 1bee9742af9b EMS_VariantDensityMapping.xml --- a/EMS_VariantDensityMapping.xml Thu Jun 14 21:22:51 2012 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,80 +0,0 @@ - - Map a mutation by linkage to regions of high mutation density using WGS data - EMS_VariantDensityMapping.py --snp_vcf $snp_vcf --ylim $ylim --hist_color $hist_color --standardize $standardize --ems $ems --output $output - - - - - - - - - - - - sys - optparse - csv - re - decimal - rpy - - - - - - -**What it does:** - -This tool is part of the CloudMap pipeline for analysis of mutant genome sequences. For further details, please see `Gregory Minevich, Danny Park, Richard J. Poole and Oliver Hobert. CloudMap: A Cloud-based Pipeline for Analysis of Mutant Genome Sequences. (2012 In Preparation)`__ - - .. __: http://biochemistry.hs.columbia.edu/labs/hobert/literature.html - -Following the approach detailed in Zuryn et al., Genetics 2010, this tool plots histograms of variant density in a mutant C.elegans strain that has been backcrossed to its (pre-mutagenesis) starting strain. Common (i.e. non-phenotype causing) variants present in multiple WGS strains **with the same background** should first be subtracted using the GATK tool *Select Variants*. - -Sample output where LG III shows linkage to the causal mutation is shown below. In this example, common variants from another strain have been subtracted and remaining variants have been filtered for most common EMS-induced mutations i.e. G/C --> A/T): - -.. image:: http://biochemistry.hs.columbia.edu/labs/hobert/CloudMap/EMS_Variant_Density_750px.png - - - - - -The experimental approach is detailed in Figure 1a from Zuryn et al., Genetics 2010: - -.. image:: http://biochemistry.hs.columbia.edu/labs/hobert/CloudMap/Zuryn_2010_Genetics_Fig1a.pdf - - -Subtracting common (non-phenotype causing) variants from more whole genome sequenced strains (using GATK Tools *Select Variants*) will result in less noise and a tighter mapping region. Additional backcrosses will also result in a smaller mapping region. - ------- - -**Settings:** - -.. class:: infomark - -Supported colors for data points and loess regression line: - -http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf - -http://research.stowers-institute.org/efg/R/Color/Chart/ColorChart.pdf - - - - -.. class:: warningmark - -This tool requires that the statistical programming environment R has been installed on the system hosting Galaxy (http://www.r-project.org/). If you are accessing this tool on Galaxy via the Cloud, this does not apply to you. - ------- - -**Citation:** - -This tool is part of the CloudMap package from the Hobert Lab. If you use this tool, please cite `Gregory Minevich, Danny Park, Richard J. Poole and Oliver Hobert. CloudMap: A Cloud-based Pipeline for Analysis of Mutant Genome Sequences. (2012 In Preparation)`__ - - .. __: http://biochemistry.hs.columbia.edu/labs/hobert/literature.html - -Correspondence to gm2123@columbia.edu (G.M.) or or38@columbia.edu (O.H.) - - -