jblitzar/github-python · Datasets at Hugging Face

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'in that directory. (Default: offsets.txt)')
parser.add_argument('--max5mis', type=int, default=1, help='Maximum 5\' mismatches to trim. Reads with more than this number will be excluded.'
'(Default: 1)')
parser.add_argument('--regressfile', default='regression.h5',
help='Filename to which to output the table of regression scores for each ORF. Formatted as pandas HDF (tables generated include '
'"start_strengths", "orf_strengths", and "stop_strengths"). If SUBDIR is set, this file will be placed in that directory. '
'(Default: regression.h5)')
parser.add_argument('--startonly', action='store_true', help='Toggle for datasets collected in the presence of initiation inhibitor (e.g. HARR, '
'LTM). If selected, "stop_strengths" will not be calculated or saved.')
parser.add_argument('--startrange', type=int, nargs=2, default=[1, 50],
help='Region around start codon (in codons) to model explicitly. Ignored if reading metagene from file (Default: 1 50, meaning '
'one full codon before the start is modeled, as are the start codon and the 49 codons following it).')
parser.add_argument('--stoprange', type=int, nargs=2, default=[7, 0],
help='Region around stop codon (in codons) to model explicitly. Ignored if reading metagene from file (Default: 7 0, meaning '
'seven full codons before and including the stop are modeled, but none after).')
parser.add_argument('--mincdsreads', type=int, default=64,
help='Minimum number of reads required within the body of the CDS (and any surrounding nucleotides indicated by STARTRANGE or '
'STOPRANGE) for it to be included in the metagene. Ignored if reading metagene from file (Default: 64).')
parser.add_argument('--startcount', type=int, default=0,
help='Minimum reads at putative translation initiation codon. Useful to reduce computational burden by only considering ORFs '
'with e.g. at least 1 read at the start. (Default: 0)')
parser.add_argument('--metagenefile', default='metagene.txt',
help='File to save metagene profile, OR if the file already exists, it will be used as the input metagene. Formatted as '
'tab-delimited text, with position, readlength, value, and type ("START", "CDS", or "STOP"). If SUBDIR is set, this file '
'will be placed in that directory. (Default: metagene.txt)')
parser.add_argument('--noregress', action='store_true', help='Only generate a metagene (i.e. do not perform any regressions)')
parser.add_argument('--exclude', nargs='+', help='Names of transcript families (tfams) to exclude from analysis due to excessive computational time '
'or memory footprint (e.g. TTN can be so large that the regression never finishes).')
parser.add_argument('-v', '--verbose', action='count', help='Output a log of progress and timing (to stdout). Repeat for higher verbosity level.')
parser.add_argument('-p', '--numproc', type=int, default=1, help='Number of processes to run. Defaults to 1 but more recommended if available.')
parser.add_argument('-f', '--force', action='store_true',
help='Force file overwrite. This will overwrite both METAGENEFILE and REGRESSFILE, if they exist. To overwrite only REGRESSFILE '
'(and not the METAGENEFILE), do not invoke this option but simply delete REGRESSFILE.')
opts = parser.parse_args()

offsetfilename = os.path.join(opts.subdir, opts.offsetfile)
metafilename = os.path.join(opts.subdir, opts.metagenefile)
regressfilename = os.path.join(opts.subdir, opts.regressfile)

if not opts.force:
if os.path.exists(regressfilename):
if os.path.exists(metafilename):
raise IOError('%s exists; use --force to overwrite (will also recalculate metagene and overwrite %s)' % (regressfilename, metafilename))
raise IOError('%s exists; use --force to overwrite' % regressfilename)

restrictbystartfilenames = []
if opts.restrictbystarts:
if len(opts.restrictbystarts) > 1 and len(opts.minwstart) == 1:
opts.minwstart *= len(opts.restrictbystarts) # expand the list to the same number of arguments
if len(opts.minwstart) != len(opts.restrictbystarts):
raise ValueError('--minwstart must be given same number of values as --restrictbystarts, or one value for all')
for restrictbystart in opts.restrictbystarts:
if os.path.isfile(restrictbystart):
restrictbystartfilenames.append(restrictbystart)
elif os.path.isdir(restrictbystart) and os.path.isfile(os.path.join(restrictbystart, opts.regressfile)):
restrictbystartfilenames.append(os.path.join(restrictbystart, opts.regressfile))
else:
raise IOError('Regression file/directory %s not found' % restrictbystart)

if opts.verbose:
sys.stdout.write(' '.join(sys.argv) + '\n')

def logprint(nextstr):
sys.stdout.write('[%s] %s\n' % (strftime('%Y-%m-%d %H:%M:%S'), nextstr))
sys.stdout.flush()

log_lock = mp.Lock()

rdlens = []
Pdict = {}
with open(offsetfilename, 'rU') as infile:
for line in infile:
ls = line.strip().split()
rdlen = int(ls[0])
for nmis in range(opts.max5mis+1):
Pdict[(rdlen, nmis)] = int(ls[1])+nmis # e.g. if nmis == 1, offset as though the read were missing that base entirely
rdlens.append(rdlen)
# Pdict = {(int(ls[0]), nmis): int(ls[1])+nmis for ls in [line.strip().split() for line in infile] for nmis in range(opts.max5mis+1)}
# Pdict = {(ls[0], nmis): ls[1] for ls in [line.strip().split() for line in infile] if opts.maxrdlen >= ls[0] >= opts.minrdlen
# for nmis in range(opts.max5mis+1)}
rdlens.sort()

# hash transcripts by ID for easy reference later
with open(opts.inbed, 'rU') as inbed:
bedlinedict = {line.split()[3]: line for line in inbed}


def _get_annotated_counts_by_chrom(chrom_to_do):
"""Accumulate counts from annotated CDSs into a metagene profile. Only the longest CDS in each transcript family will be included, and only if it
meets the minimum number-of-reads requirement. Reads are normalized by gene, so every gene included contributes equally to the final metagene."""
found_cds = pd.read_hdf(opts.orfstore, 'all_orfs', mode='r',
where="chrom == '%s' and orftype == 'annotated' and tstop > 0 and tcoord > %d and AAlen > %d"
% (chrom_to_do, -startnt[0], min_AAlen),
columns=['orfname', 'tfam', 'tid', 'tcoord', 'tstop', 'AAlen']) \
.sort_values('AAlen', ascending=False).drop_duplicates('tfam') # use the longest annotated CDS in each transcript family
num_cds_incl = 0 # number of CDSs included from this chromosome
startprof = np.zeros((len(rdlens), startlen))
cdsprof = np.zeros((len(rdlens), 3))
stopprof = np.zeros((len(rdlens), stoplen))
inbams = [pysam.Samfile(infile, 'rb') for infile in opts.bamfiles]

'in that directory. (Default: offsets.txt)')

parser.add_argument('--max5mis', type=int, default=1, help='Maximum 5\' mismatches to trim. Reads with more than this number will be excluded.'

'(Default: 1)')

parser.add_argument('--regressfile', default='regression.h5',

help='Filename to which to output the table of regression scores for each ORF. Formatted as pandas HDF (tables generated include '

'"start_strengths", "orf_strengths", and "stop_strengths"). If SUBDIR is set, this file will be placed in that directory. '

'(Default: regression.h5)')

parser.add_argument('--startonly', action='store_true', help='Toggle for datasets collected in the presence of initiation inhibitor (e.g. HARR, '

'LTM). If selected, "stop_strengths" will not be calculated or saved.')

parser.add_argument('--startrange', type=int, nargs=2, default=[1, 50],

help='Region around start codon (in codons) to model explicitly. Ignored if reading metagene from file (Default: 1 50, meaning '

'one full codon before the start is modeled, as are the start codon and the 49 codons following it).')

parser.add_argument('--stoprange', type=int, nargs=2, default=[7, 0],

help='Region around stop codon (in codons) to model explicitly. Ignored if reading metagene from file (Default: 7 0, meaning '

'seven full codons before and including the stop are modeled, but none after).')

parser.add_argument('--mincdsreads', type=int, default=64,

help='Minimum number of reads required within the body of the CDS (and any surrounding nucleotides indicated by STARTRANGE or '

'STOPRANGE) for it to be included in the metagene. Ignored if reading metagene from file (Default: 64).')

parser.add_argument('--startcount', type=int, default=0,

help='Minimum reads at putative translation initiation codon. Useful to reduce computational burden by only considering ORFs '

'with e.g. at least 1 read at the start. (Default: 0)')

parser.add_argument('--metagenefile', default='metagene.txt',

help='File to save metagene profile, OR if the file already exists, it will be used as the input metagene. Formatted as '

'tab-delimited text, with position, readlength, value, and type ("START", "CDS", or "STOP"). If SUBDIR is set, this file '

'will be placed in that directory. (Default: metagene.txt)')

parser.add_argument('--noregress', action='store_true', help='Only generate a metagene (i.e. do not perform any regressions)')

parser.add_argument('--exclude', nargs='+', help='Names of transcript families (tfams) to exclude from analysis due to excessive computational time '

'or memory footprint (e.g. TTN can be so large that the regression never finishes).')

parser.add_argument('-v', '--verbose', action='count', help='Output a log of progress and timing (to stdout). Repeat for higher verbosity level.')

parser.add_argument('-p', '--numproc', type=int, default=1, help='Number of processes to run. Defaults to 1 but more recommended if available.')

parser.add_argument('-f', '--force', action='store_true',

help='Force file overwrite. This will overwrite both METAGENEFILE and REGRESSFILE, if they exist. To overwrite only REGRESSFILE '

'(and not the METAGENEFILE), do not invoke this option but simply delete REGRESSFILE.')

opts = parser.parse_args()

offsetfilename = os.path.join(opts.subdir, opts.offsetfile)

metafilename = os.path.join(opts.subdir, opts.metagenefile)

regressfilename = os.path.join(opts.subdir, opts.regressfile)

if not opts.force:

if os.path.exists(regressfilename):

if os.path.exists(metafilename):

raise IOError('%s exists; use --force to overwrite (will also recalculate metagene and overwrite %s)' % (regressfilename, metafilename))

raise IOError('%s exists; use --force to overwrite' % regressfilename)

restrictbystartfilenames = []

if opts.restrictbystarts:

if len(opts.restrictbystarts) > 1 and len(opts.minwstart) == 1:

opts.minwstart *= len(opts.restrictbystarts) # expand the list to the same number of arguments

if len(opts.minwstart) != len(opts.restrictbystarts):

raise ValueError('--minwstart must be given same number of values as --restrictbystarts, or one value for all')

for restrictbystart in opts.restrictbystarts:

if os.path.isfile(restrictbystart):

restrictbystartfilenames.append(restrictbystart)

elif os.path.isdir(restrictbystart) and os.path.isfile(os.path.join(restrictbystart, opts.regressfile)):

restrictbystartfilenames.append(os.path.join(restrictbystart, opts.regressfile))

else:

raise IOError('Regression file/directory %s not found' % restrictbystart)

if opts.verbose:

sys.stdout.write(' '.join(sys.argv) + '\n')

def logprint(nextstr):

sys.stdout.write('[%s] %s\n' % (strftime('%Y-%m-%d %H:%M:%S'), nextstr))

sys.stdout.flush()

log_lock = mp.Lock()

rdlens = []

Pdict = {}

with open(offsetfilename, 'rU') as infile:

for line in infile:

ls = line.strip().split()

rdlen = int(ls[0])

for nmis in range(opts.max5mis+1):

Pdict[(rdlen, nmis)] = int(ls[1])+nmis # e.g. if nmis == 1, offset as though the read were missing that base entirely

rdlens.append(rdlen)

# Pdict = {(int(ls[0]), nmis): int(ls[1])+nmis for ls in [line.strip().split() for line in infile] for nmis in range(opts.max5mis+1)}

# Pdict = {(ls[0], nmis): ls[1] for ls in [line.strip().split() for line in infile] if opts.maxrdlen >= ls[0] >= opts.minrdlen

# for nmis in range(opts.max5mis+1)}

rdlens.sort()

# hash transcripts by ID for easy reference later

with open(opts.inbed, 'rU') as inbed:

bedlinedict = {line.split()[3]: line for line in inbed}

def _get_annotated_counts_by_chrom(chrom_to_do):

"""Accumulate counts from annotated CDSs into a metagene profile. Only the longest CDS in each transcript family will be included, and only if it

meets the minimum number-of-reads requirement. Reads are normalized by gene, so every gene included contributes equally to the final metagene."""

found_cds = pd.read_hdf(opts.orfstore, 'all_orfs', mode='r',

where="chrom == '%s' and orftype == 'annotated' and tstop > 0 and tcoord > %d and AAlen > %d"

% (chrom_to_do, -startnt[0], min_AAlen),

columns=['orfname', 'tfam', 'tid', 'tcoord', 'tstop', 'AAlen']) \

.sort_values('AAlen', ascending=False).drop_duplicates('tfam') # use the longest annotated CDS in each transcript family

num_cds_incl = 0 # number of CDSs included from this chromosome

startprof = np.zeros((len(rdlens), startlen))

cdsprof = np.zeros((len(rdlens), 3))

stopprof = np.zeros((len(rdlens), stoplen))

inbams = [pysam.Samfile(infile, 'rb') for infile in opts.bamfiles]