def get_parser():
epilog = """\
Trimmed sequences will be placed in
``${input_sequence_filename}.abundfilt`` for each input sequence file. If
the input sequences are from RNAseq or metagenome sequencing then
:option:`--variable-coverage` should be used.
Example::
load-into-counting.py -k 20 -x 5e7 countgraph data/100k-filtered.fa
filter-abund.py -C 2 countgraph data/100k-filtered.fa
"""
parser = argparse.ArgumentParser(
description='Trim sequences at a minimum k-mer abundance.',
epilog=textwrap.dedent(epilog),
formatter_class=ComboFormatter)
parser.add_argument('input_graph', metavar='input_count_graph_filename',
help='The input k-mer countgraph filename')
parser.add_argument('input_filename', metavar='input_sequence_filename',
help='Input FAST[AQ] sequence filename', nargs='+')
add_threading_args(parser)
parser.add_argument('--cutoff', '-C', dest='cutoff',
default=DEFAULT_CUTOFF,
type=check_argument_range(0, 256, 'cutoff'),
help="Trim at k-mers below this abundance.")
parser.add_argument('--variable-coverage', '-V', action='store_true',
dest='variable_coverage', default=False,
help='Only trim low-abundance k-mers from sequences '
'that have high coverage.')
parser.add_argument('--normalize-to', '-Z', type=int, dest='normalize_to',
help='Base the variable-coverage cutoff on this median'
' k-mer abundance.',
default=DEFAULT_NORMALIZE_LIMIT)
parser.add_argument('-o', '--output', dest='single_output_file',
type=argparse.FileType('wb'),
metavar="optional_output_filename",
help='Output the trimmed sequences into a single file '
'with the given filename instead of creating a new '
'file for each input file.')
parser.add_argument('--version', action=_VersionStdErrAction,
version='khmer {v}'.format(v=__version__))
parser.add_argument('-f', '--force', default=False, action='store_true',
help='Overwrite output file if it exists')
parser.add_argument('-q', '--quiet', dest='quiet', default=False,
action='store_true')
add_output_compression_type(parser)
return parser
def get_parser():
epilog = """\
Trimmed sequences will be placed in
``${input_sequence_filename}.abundfilt``.
This script is constant memory.
To trim reads based on k-mer abundance across multiple files, use
:program:`load-into-counting.py` and :program:`filter-abund.py`.
Example::
filter-abund-single.py -k 20 -x 5e7 -C 2 data/100k-filtered.fa
"""
parser = build_counting_args(
descr="Trims sequences at a minimum k-mer abundance "
"(in memory version).", epilog=textwrap.dedent(epilog),
citations=['counting', 'SeqAn'])
add_threading_args(parser)
parser.add_argument('--cutoff', '-C', default=DEFAULT_CUTOFF,
type=check_argument_range(0, 256, "cutoff"),
help="Trim at k-mers below this abundance.")
parser.add_argument('--variable-coverage', '-V', action='store_true',
dest='variable_coverage', default=False,
help='Only trim low-abundance k-mers from sequences '
'that have high coverage.')
parser.add_argument('--normalize-to', '-Z', type=int, dest='normalize_to',
help='Base the variable-coverage cutoff on this median'
' k-mer abundance.',
default=DEFAULT_NORMALIZE_LIMIT)
parser.add_argument('--savegraph', metavar="filename", default='',
help="If present, the name of the file to save the "
"k-mer countgraph to")
parser.add_argument('-o', '--outfile', metavar='optional_output_filename',
default=None, help='Override default output filename '
'and output trimmed sequences into a file with the '
'given filename.')
parser.add_argument('datafile', metavar='input_sequence_filename',
help="FAST[AQ] sequence file to trim")
parser.add_argument('-f', '--force', default=False, action='store_true',
help='Overwrite output file if it exists')
parser.add_argument('-q', '--quiet', dest='quiet', default=False,
action='store_true')
add_output_compression_type(parser)
return parser
def get_parser():
epilog = """\
Trimmed sequences will be placed in
``${input_sequence_filename}.abundfilt``.
This script is constant memory.
To trim reads based on k-mer abundance across multiple files, use
:program:`load-into-counting.py` and :program:`filter-abund.py`.
Example::
filter-abund-single.py -k 20 -x 5e7 -C 2 data/100k-filtered.fa
"""
parser = build_counting_args(
descr="Trims sequences at a minimum k-mer abundance "
"(in memory version).", epilog=textwrap.dedent(epilog),
citations=['counting', 'SeqAn'])
add_threading_args(parser)
parser.add_argument('-C', '--cutoff', default=DEFAULT_CUTOFF,
type=check_argument_range(0, 256, "cutoff"),
help="Trim at k-mers below this abundance.")
parser.add_argument('-V', '--variable-coverage', action='store_true',
dest='variable_coverage', default=False,
help='Only trim low-abundance k-mers from sequences '
'that have high coverage.')
parser.add_argument('-Z', '--normalize-to', type=int, dest='normalize_to',
help='Base the variable-coverage cutoff on this median'
' k-mer abundance.',
default=DEFAULT_NORMALIZE_LIMIT)
parser.add_argument('--savegraph', metavar="filename", default='',
help="If present, the name of the file to save the "
"k-mer countgraph to")
parser.add_argument('-o', '--outfile', metavar='optional_output_filename',
default=None, help='Override default output filename '
'and output trimmed sequences into a file with the '
'given filename.')
parser.add_argument('datafile', metavar='input_sequence_filename',
help="FAST[AQ] sequence file to trim")
parser.add_argument('-f', '--force', default=False, action='store_true',
help='Overwrite output file if it exists')
parser.add_argument('-q', '--quiet', dest='quiet', default=False,
action='store_true')
add_output_compression_type(parser)
return parser
def get_parser():
epilog = """\
Trimmed sequences will be placed in
``${input_sequence_filename}.abundfilt`` for each input sequence file. If
the input sequences are from RNAseq or metagenome sequencing then
:option:`--variable-coverage` should be used.
Example::
load-into-counting.py -k 20 -x 5e7 countgraph data/100k-filtered.fa
filter-abund.py -C 2 countgraph data/100k-filtered.fa
"""
parser = KhmerArgumentParser(
description='Trim sequences at a minimum k-mer abundance.',
epilog=textwrap.dedent(epilog),
citations=['counting'])
parser.add_argument('input_graph', metavar='input_count_graph_filename',
help='The input k-mer countgraph filename')
parser.add_argument('input_filename', metavar='input_sequence_filename',
help='Input FAST[AQ] sequence filename', nargs='+')
add_threading_args(parser)
parser.add_argument('--cutoff', '-C', dest='cutoff',
default=DEFAULT_CUTOFF,
type=check_argument_range(0, 256, 'cutoff'),
help="Trim at k-mers below this abundance.")
parser.add_argument('--variable-coverage', '-V', action='store_true',
dest='variable_coverage', default=False,
help='Only trim low-abundance k-mers from sequences '
'that have high coverage.')
parser.add_argument('--normalize-to', '-Z', type=int, dest='normalize_to',
help='Base the variable-coverage cutoff on this median'
' k-mer abundance.',
default=DEFAULT_NORMALIZE_LIMIT)
parser.add_argument('-o', '--output', dest='single_output_file',
type=khFileType('wb'),
metavar="optional_output_filename",
help='Output the trimmed sequences into a single file '
'with the given filename instead of creating a new '
'file for each input file.')
parser.add_argument('-f', '--force', default=False, action='store_true',
help='Overwrite output file if it exists')
parser.add_argument('-q', '--quiet', dest='quiet', default=False,
action='store_true')
add_output_compression_type(parser)
return parser
def get_parser():
epilog = """\
Discard sequences based on whether or not their median k-mer abundance lies
above a specified cutoff. Kept sequences will be placed in <fileN>.keep.
By default, paired end reads will be considered together; if
either read should be kept, both will be kept. (This keeps both
reads from a fragment, and helps with retention of repeats.)
Unpaired reads are treated individually.
If :option:`-p`/:option:`--paired` is set, then proper pairing is required
and the script will exit on unpaired reads, although
:option:`--unpaired-reads` can be used to supply a file of orphan
reads to be read after the paired reads.
:option:`--force_single` will ignore all pairing information and treat
reads individually.
With :option:`-s`/:option:`--savegraph`, the k-mer countgraph
will be saved to the specified file after all sequences have been
processed. :option:`-l`/:option:`--loadgraph` will load the
specified k-mer countgraph before processing the specified
files. Note that these graphs are are in the same format as those
produced by :program:`load-into-counting.py` and consumed by
:program:`abundance-dist.py`.
To append reads to an output file (rather than overwriting it), send output
to STDOUT with `--output -` and use UNIX file redirection syntax (`>>`) to
append to the file.
Example::
normalize-by-median.py -k 17 tests/test-data/test-abund-read-2.fa
Example::
normalize-by-median.py -p -k 17 \\
tests/test-data/test-abund-read-paired.fa
Example::
normalize-by-median.py -p -k 17 -o - tests/test-data/paired.fq \\
>> appended-output.fq
Example::
normalize-by-median.py -k 17 -f tests/test-data/test-error-reads.fq \\
tests/test-data/test-fastq-reads.fq
Example::
normalize-by-median.py -k 17 -s test.ct \\
tests/test-data/test-abund-read-2.fa \\
tests/test-data/test-fastq-reads.fq"""
parser = build_counting_args(
descr="Do digital normalization (remove mostly redundant sequences)",
epilog=textwrap.dedent(epilog),
citations=['diginorm'])
parser.add_argument('-q',
'--quiet',
dest='quiet',
default=False,
action='store_true')
parser.add_argument('-C',
'--cutoff',
help="when the median "
"k-mer coverage level is above this number the "
"read is not kept.",
type=check_argument_range(0, 256, "cutoff"),
default=DEFAULT_DESIRED_COVERAGE)
parser.add_argument('-p',
'--paired',
action='store_true',
help='require that all sequences be properly paired')
parser.add_argument('--force_single',
dest='force_single',
action='store_true',
help='treat all sequences as single-ended/unpaired')
parser.add_argument('-u',
'--unpaired-reads',
metavar="unpaired_reads_filename",
help='include a file of unpaired reads to which '
'-p/--paired does not apply.')
parser.add_argument('-s',
'--savegraph',
metavar="filename",
default=None,
help='save the k-mer countgraph to disk after all '
'reads are loaded.')
parser.add_argument('-R',
'--report',
help='write progress report to report_filename',
metavar='report_filename',
type=argparse.FileType('w'))
parser.add_argument('--report-frequency',
metavar='report_frequency',
type=int,
default=100000,
help='report progress every report_frequency reads')
parser.add_argument('-f',
'--force',
dest='force',
help='continue past file reading errors',
action='store_true')
parser.add_argument('-o',
'--output',
metavar="filename",
type=khFileType('wb'),
default=None,
dest='single_output_file',
help='only output a single file with '
'the specified filename; use a single dash "-" to '
'specify that output should go to STDOUT (the '
'terminal)')
parser.add_argument('input_filenames',
metavar='input_sequence_filename',
help='Input FAST[AQ] sequence filename.',
nargs='+')
add_loadgraph_args(parser)
add_output_compression_type(parser)
return parser
def get_parser():
epilog = """\
Discard sequences based on whether or not their median k-mer abundance lies
above a specified cutoff. Kept sequences will be placed in <fileN>.keep.
By default, paired end reads will be considered together; if
either read should be kept, both will be kept. (This keeps both
reads from a fragment, and helps with retention of repeats.)
Unpaired reads are treated individually.
If :option:`-p`/:option:`--paired` is set, then proper pairing is required
and the script will exit on unpaired reads, although
:option:`--unpaired-reads` can be used to supply a file of orphan
reads to be read after the paired reads.
:option:`--force_single` will ignore all pairing information and treat
reads individually.
With :option:`-s`/:option:`--savegraph`, the k-mer countgraph
will be saved to the specified file after all sequences have been
processed. :option:`-l`/:option:`--loadgraph` will load the
specified k-mer countgraph before processing the specified
files. Note that these graphs are are in the same format as those
produced by :program:`load-into-counting.py` and consumed by
:program:`abundance-dist.py`.
To append reads to an output file (rather than overwriting it), send output
to STDOUT with `--output -` and use UNIX file redirection syntax (`>>`) to
append to the file.
Example::
normalize-by-median.py -k 17 tests/test-data/test-abund-read-2.fa
Example::
normalize-by-median.py -p -k 17 \\
tests/test-data/test-abund-read-paired.fa
Example::
normalize-by-median.py -p -k 17 -o - tests/test-data/paired.fq \\
>> appended-output.fq
Example::
normalize-by-median.py -k 17 -f tests/test-data/test-error-reads.fq \\
tests/test-data/test-fastq-reads.fq
Example::
normalize-by-median.py -k 17 -s test.ct \\
tests/test-data/test-abund-read-2.fa \\
tests/test-data/test-fastq-reads.fq"""
parser = build_counting_args(
descr="Do digital normalization (remove mostly redundant sequences)",
epilog=textwrap.dedent(epilog),
citations=['diginorm'])
parser.add_argument('-q', '--quiet', dest='quiet', default=False,
action='store_true')
parser.add_argument('-C', '--cutoff', help="when the median "
"k-mer coverage level is above this number the "
"read is not kept.",
type=check_argument_range(0, 256, "cutoff"),
default=DEFAULT_DESIRED_COVERAGE)
parser.add_argument('-p', '--paired', action='store_true',
help='require that all sequences be properly paired')
parser.add_argument('--force_single', dest='force_single',
action='store_true',
help='treat all sequences as single-ended/unpaired')
parser.add_argument('-u', '--unpaired-reads',
metavar="unpaired_reads_filename",
help='include a file of unpaired reads to which '
'-p/--paired does not apply.')
parser.add_argument('-s', '--savegraph', metavar="filename", default=None,
help='save the k-mer countgraph to disk after all '
'reads are loaded.')
parser.add_argument('-R', '--report',
help='write progress report to report_filename',
metavar='report_filename', type=argparse.FileType('w'))
parser.add_argument('--report-frequency',
metavar='report_frequency', type=int, default=100000,
help='report progress every report_frequency reads')
parser.add_argument('-f', '--force', dest='force',
help='continue past file reading errors',
action='store_true')
parser.add_argument('-o', '--output', metavar="filename",
type=khFileType('wb'),
default=None, dest='single_output_file',
help='only output a single file with '
'the specified filename; use a single dash "-" to '
'specify that output should go to STDOUT (the '
'terminal)')
parser.add_argument('input_filenames', metavar='input_sequence_filename',
help='Input FAST[AQ] sequence filename.', nargs='+')
add_loadgraph_args(parser)
add_output_compression_type(parser)
return parser
def get_parser():
epilog = """\
Trimmed sequences will be placed in
``${input_sequence_filename}.abundfilt``.
This script is constant memory.
To trim reads based on k-mer abundance across multiple files, use
:program:`load-into-counting.py` and :program:`filter-abund.py`.
Example::
filter-abund-single.py -k 20 -x 5e7 -C 2 data/100k-filtered.fa
"""
parser = build_counting_args(
descr="Trims sequences at a minimum k-mer abundance " "(in memory version).",
epilog=textwrap.dedent(epilog),
citations=["counting", "SeqAn"],
)
add_threading_args(parser)
parser.add_argument(
"--cutoff",
"-C",
default=DEFAULT_CUTOFF,
type=check_argument_range(0, 256, "cutoff"),
help="Trim at k-mers below this abundance.",
)
parser.add_argument(
"--variable-coverage",
"-V",
action="store_true",
dest="variable_coverage",
default=False,
help="Only trim low-abundance k-mers from sequences " "that have high coverage.",
)
parser.add_argument(
"--normalize-to",
"-Z",
type=int,
dest="normalize_to",
help="Base the variable-coverage cutoff on this median" " k-mer abundance.",
default=DEFAULT_NORMALIZE_LIMIT,
)
parser.add_argument(
"--savegraph",
metavar="filename",
default="",
help="If present, the name of the file to save the " "k-mer countgraph to",
)
parser.add_argument(
"-o",
"--outfile",
metavar="optional_output_filename",
default=None,
help="Override default output filename " "and output trimmed sequences into a file with the " "given filename.",
)
parser.add_argument("datafile", metavar="input_sequence_filename", help="FAST[AQ] sequence file to trim")
parser.add_argument("-f", "--force", default=False, action="store_true", help="Overwrite output file if it exists")
parser.add_argument("-q", "--quiet", dest="quiet", default=False, action="store_true")
add_output_compression_type(parser)
return parser
