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 = 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 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
