def parseArguments(): parentParser = parserCommon.getParentArgParse() bamParser = parserCommon.read_options() normalizationParser = parserCommon.normalization_options() requiredArgs = get_required_args() optionalArgs = get_optional_args() outputParser = parserCommon.output() parser = \ argparse.ArgumentParser( parents=[requiredArgs, outputParser, optionalArgs, parentParser, normalizationParser, bamParser], formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='This tool takes an alignment of reads or fragments ' 'as input (BAM file) and generates a coverage track (bigWig or ' 'bedGraph) as output. ' 'The coverage is calculated as the number of reads per bin, ' 'where bins are short consecutive counting windows of a defined ' 'size. It is possible to extended the length of the reads ' 'to better reflect the actual fragment length. *bamCoverage* ' 'offers normalization by scaling factor, Reads Per Kilobase per ' 'Million mapped reads (RPKM), counts per million (CPM), bins per ' 'million mapped reads (BPM) and 1x depth (reads per genome ' 'coverage, RPGC).\n', usage='An example usage is:' '$ bamCoverage -b reads.bam -o coverage.bw', add_help=False) return parser
def parseArguments(): parentParser = parserCommon.getParentArgParse() bamParser = parserCommon.read_options() normalizationParser = parserCommon.normalization_options() requiredArgs = getRequiredArgs() optionalArgs = getOptionalArgs() outputParser = parserCommon.output() parser = argparse.ArgumentParser( parents=[requiredArgs, outputParser, optionalArgs, parentParser, normalizationParser, bamParser], formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='This tool compares two BAM files based on the number of ' 'mapped reads. To compare the BAM files, the genome is partitioned ' 'into bins of equal size, then the number of reads found in each bin' 'is counted per file and finally a summary value is ' 'reported. This value can be the ratio of the number of reads per ' 'bin, the log2 of the ratio or the difference. \nThis tool can ' 'normalize the number of reads in each BAM file using the SES method ' 'proposed by Diaz et al. (2012) "Normalization, bias correction, and ' 'peak calling for ChIP-seq". Statistical Applications in Genetics ' 'and Molecular Biology, 11(3). Normalization based on read counts ' 'is also available. \nThe output is either a bedgraph or bigWig file ' 'containing the bin location and the resulting comparison value. By ' 'default, if reads are paired, the fragment length reported in the BAM ' 'file is used. Each mate, however, ' 'is treated independently to avoid a bias when a mixture of concordant ' 'and discordant pairs is present. This means that *each end* will ' 'be extended to match the fragment length.', usage=' bamCompare -b1 treatment.bam -b2 control.bam -o log2ratio.bw', add_help=False) return parser
def parseArguments(): parentParser = parserCommon.getParentArgParse() bamParser = parserCommon.read_options() normalizationParser = parserCommon.normalization_options() requiredArgs = getRequiredArgs() optionalArgs = getOptionalArgs() outputParser = parserCommon.output() parser = argparse.ArgumentParser( parents=[ requiredArgs, outputParser, optionalArgs, parentParser, normalizationParser, bamParser ], formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='This tool compares two BAM files based on the number of ' 'mapped reads. To compare the BAM files, the genome is partitioned ' 'into bins of equal size, then the number of reads found in each is counted per file ' 'and finally a summary value is ' 'reported. This value can be the ratio of the number of reads per ' 'bin, the log2 of the ratio or the difference. This tool can ' 'normalize the number of reads in each BAM file using the SES method ' 'proposed in Diaz et al. (2012). "Normalization, bias correction, and ' 'peak calling for ChIP-seq". Statistical applications in genetics ' 'and molecular biology, 11(3). Normalization based on read counts ' 'is also available. The output is either a bedgraph or bigWig file ' 'containing the bin location and the resulting comparison values. By ' 'default, if reads are mated, the fragment length reported in the BAM ' 'file is used. In the case of paired-end mapping, each mate ' 'is treated independently to avoid a bias when a mixture of concordant ' 'and discordant pairs is present. This means that *each end* will ' 'be extended to match the fragment length.', usage='An example usage is:\n bamCompare ' '-b1 treatment.bam -b2 control.bam -o log2ratio.bw', add_help=False) return parser
def parseArguments(): parentParser = parserCommon.getParentArgParse() bamParser = parserCommon.read_options() normalizationParser = parserCommon.normalization_options() requiredArgs = getRequiredArgs() optionalArgs = getOptionalArgs() outputParser = parserCommon.output() parser = argparse.ArgumentParser( parents=[ requiredArgs, outputParser, optionalArgs, parentParser, normalizationParser, bamParser ], formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='This tool compares two BAM files based on the number of ' 'mapped reads. To compare the BAM files, the genome is partitioned ' 'into bins of equal size, then the number of reads found in each bin' ' is counted per file, and finally a summary value is ' 'reported. This value can be the ratio of the number of reads per ' 'bin, the log2 of the ratio, or the difference. This tool can ' 'normalize the number of reads in each BAM file using the SES method ' 'proposed by Diaz et al. (2012) "Normalization, bias correction, and ' 'peak calling for ChIP-seq". Statistical Applications in Genetics ' 'and Molecular Biology, 11(3). Normalization based on read counts ' 'is also available. The output is either a bedgraph or bigWig file ' 'containing the bin location and the resulting comparison value. ' 'Note that *each end* in a pair (for paired-end reads) is treated ' 'independently. If this is undesirable, then use the --samFlagInclude ' 'or --samFlagExclude options.', usage=' bamCompare -b1 treatment.bam -b2 control.bam -o log2ratio.bw', add_help=False) return parser
def parseArguments(): parentParser = parserCommon.getParentArgParse() bamParser = parserCommon.read_options() normalizationParser = parserCommon.normalization_options() requiredArgs = getRequiredArgs() optionalArgs = getOptionalArgs() outputParser = parserCommon.output() parser = argparse.ArgumentParser( parents=[requiredArgs, outputParser, optionalArgs, parentParser, normalizationParser, bamParser], formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='This tool compares two BAM files based on the number of ' 'mapped reads. To compare the BAM files, the genome is partitioned ' 'into bins of equal size, then the number of reads found in each bin' ' is counted per file, and finally a summary value is ' 'reported. This value can be the ratio of the number of reads per ' 'bin, the log2 of the ratio, or the difference. This tool can ' 'normalize the number of reads in each BAM file using the SES method ' 'proposed by Diaz et al. (2012) "Normalization, bias correction, and ' 'peak calling for ChIP-seq". Statistical Applications in Genetics ' 'and Molecular Biology, 11(3). Normalization based on read counts ' 'is also available. The output is either a bedgraph or bigWig file ' 'containing the bin location and the resulting comparison value. ' 'Note that *each end* in a pair (for paired-end reads) is treated ' 'independently. If this is undesirable, then use the --samFlagInclude ' 'or --samFlagExclude options.', usage=' bamCompare -b1 treatment.bam -b2 control.bam -o log2ratio.bw', add_help=False) return parser