def _get_tid_info(tup):
"""For each transcript on this chromosome/strand, identifies every sub-sequence of the appropriate length (fpsize), converts it to an integer,
identifies the number of reads mapping to that position, and outputs all of that information to a pandas HDF store."""
(chrom, strand) = tup
inbams = [pysam.Samfile(infile, 'rb') for infile in opts.bamfiles]
gnd = BAMGenomeArray(inbams, mapping=FivePrimeMapFactory(psite))
# map to roughly the center of each read so that identical sequences that cross different splice sites
# (on different transcripts) still end up mapping to the same place
gnd.add_filter('size', SizeFilterFactory(opts.minlen, opts.maxlen))
tid_seq_info = []
tid_summary = pd.DataFrame(
{'chrom': chrom, 'strand': strand, 'n_psite': -1, 'n_reads': -1, 'peak_reads': -1, 'dropped': ''},
index=pd.Index(bedlinedict[(chrom, strand)].keys(), name='tid'))
for (tid, line) in bedlinedict[(chrom, strand)].iteritems():
currtrans = SegmentChain.from_bed(line)
curr_pos_list = currtrans.get_position_list() # not in stranded order!
if strand == '-':
curr_pos_list = curr_pos_list[::-1]
n_psite = len(curr_pos_list) + 1 - fpsize
tid_summary.at[tid, 'n_psite'] = n_psite
if n_psite > 0:
curr_counts = np.array(currtrans.get_counts(gnd))[psite:n_psite + psite]
# if((curr_counts>0).any()):
sumcounts = curr_counts.sum()
maxcounts = curr_counts.max()
tid_summary.at[tid, 'n_reads'] = sumcounts
tid_summary.at[tid, 'peak_reads'] = maxcounts
if sumcounts >= opts.minreads:
if maxcounts < sumcounts * opts.peakfrac:
numseq = np.array(list(currtrans.get_sequence(genome).upper().translate(str_dict)))
curr_seq = ''.join(numseq)
tid_seq_info.append(pd.DataFrame({'tid': tid,
'genpos': curr_pos_list[psite:n_psite + psite],
'seq': np.array([(int(curr_seq[i:i + fpsize], 4) if 'N' not in curr_seq[i:i + fpsize] else -1)
for i in xrange(n_psite)], dtype=np.int64),
'reads': curr_counts}))
else:
tid_summary.at[tid, 'dropped'] = 'peakfrac'
else:
tid_summary.at[tid, 'dropped'] = 'lowreads'
if tid_seq_info: # don't bother saving anything if there's nothing to save
pd.concat(tid_seq_info, ignore_index=True).to_hdf(seq_info_hdf % (chrom, strand), 'tid_seq_info', format='t',
data_columns=True, complevel=1, complib='blosc')
# sp.call(['ptrepack', orig_store_name, seq_info_hdf%(chrom,strand)]) # repack for efficiency
# os.remove(orig_store_name)
if opts.verbose > 1:
with log_lock:
logprint('%s (%s strand) complete' % (chrom, strand))
for inbam in inbams:
inbam.close()
return tid_summary
def _quantify_tfam(orf_set, gnds):
"""Performs non-negative least squares regression to quantify all of the ORFs in a transcript family, using a simplified profile consisting of
the same three numbers tiled across each ORF. All readlengths are treated identically. Regions around start and stop codons are masked in
accordance with startmask and stopmask"""
strand = orf_set['strand'].iat[0]
chrom = orf_set['chrom'].iat[0]
tids = orf_set['tid'].drop_duplicates().tolist()
all_tfam_genpos = set()
tid_genpos = {}
tlens = {}
for (i, tid) in enumerate(tids):
currtrans = SegmentChain.from_bed(bedlinedict[tid])
curr_pos_set = currtrans.get_position_set()
tlens[tid] = len(curr_pos_set)
tid_genpos[tid] = curr_pos_set
all_tfam_genpos.update(curr_pos_set)
all_tfam_genpos = np.array(sorted(all_tfam_genpos))
if strand == '-':
all_tfam_genpos = all_tfam_genpos[::-1]
nnt = len(all_tfam_genpos)
tid_indices = {tid: np.flatnonzero(np.in1d(all_tfam_genpos, list(curr_tid_genpos), assume_unique=True))
for (tid, curr_tid_genpos) in tid_genpos.iteritems()}
orf_matrix = np.zeros((nnt, len(orf_set)))
ignore_coords = []
for (orf_num, (tid, tcoord, tstop, AAlen)) in enumerate(orf_set[['tid', 'tcoord', 'tstop', 'AAlen']].itertuples(False)):
orf_matrix[tid_indices[tid][tcoord:tstop], orf_num] = np.tile(cdsprof, AAlen + 1)
ignore_coords.append(tid_indices[tid][max(tcoord+startmask[0], 0):tcoord+startmask[1]])
ignore_coords.append(tid_indices[tid][max(tstop+stopmask[0], 0):tstop+stopmask[1]])
ignore_coords = np.unique(np.concatenate(ignore_coords))
orf_matrix[ignore_coords, :] = 0 # mask out all positions within the mask region around starts and stops
valid_orfs = np.array([(orf_matrix[:, i] > 0).any() and (orf_matrix.T[i, :] != orf_matrix.T[:i, :]).any(1).all() for i in xrange(len(orf_set))])
# require at least one valid position, and if >1 ORFs are identical, only include one of them
orf_matrix[:, ~valid_orfs] = 0 # completely ignore these positions
valid_nts = (orf_matrix > 0).any(1) # only bother checking nucleotides where there is a valid ORF
orf_res = orf_set.copy()
if valid_nts.any():
orf_matrix = orf_matrix[valid_nts, :]
valid_nt_segs = SegmentChain(*positionlist_to_segments(chrom, strand, list(all_tfam_genpos[valid_nts])))
orf_res['nts_quantified'] = (orf_matrix > 0).sum(0) # the number of nucleotides included in the quantification
for colname, gnd in zip(colnames, gnds):
orf_res[colname] = nnls(orf_matrix, valid_nt_segs.get_counts(gnd))[0]
# gnd is a HashedReadBAMGenomeArray, but it still works with get_counts(), which will collapse all read lengths to a single array
return orf_res
else:
orf_res['nts_quantified'] = 0
for colname in colnames:
orf_res[colname] = 0.
return orf_res
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]
gnd = HashedReadBAMGenomeArray(inbams, ReadKeyMapFactory(Pdict, read_length_nmis))
for (tid, tcoord, tstop) in found_cds[['tid', 'tcoord', 'tstop']].itertuples(False):
curr_trans = SegmentChain.from_bed(bedlinedict[tid])
tlen = curr_trans.get_length()
if tlen >= tstop + stopnt[1]: # need to guarantee that the 3' UTR is sufficiently long
curr_hashed_counts = get_hashed_counts(curr_trans, gnd)
cdslen = tstop+stopnt[1]-tcoord-startnt[0] # cds length, plus the extra bases...
curr_counts = np.zeros((len(rdlens), cdslen))
for (i, rdlen) in enumerate(rdlens):
for nmis in range(opts.max5mis+1):
curr_counts[i, :] += curr_hashed_counts[(rdlen, nmis)][tcoord+startnt[0]:tstop+stopnt[1]]
# curr_counts is limited to the CDS plus any extra requested nucleotides on either side
if curr_counts.sum() >= opts.mincdsreads:
curr_counts /= curr_counts.mean() # normalize by mean of counts across all readlengths and positions within the CDS
startprof += curr_counts[:, :startlen]
cdsprof += curr_counts[:, startlen:cdslen-stoplen].reshape((len(rdlens), -1, 3)).mean(1)
stopprof += curr_counts[:, cdslen-stoplen:cdslen]
num_cds_incl += 1
for inbam in inbams:
inbam.close()
return startprof, cdsprof, stopprof, num_cds_incl
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]
gnd = HashedReadBAMGenomeArray(inbams, ReadKeyMapFactory(Pdict, read_length_nmis))
for (tid, tcoord, tstop) in found_cds[['tid', 'tcoord', 'tstop']].itertuples(False):
curr_trans = SegmentChain.from_bed(bedlinedict[tid])
tlen = curr_trans.get_length()
if tlen >= tstop + stopnt[1]: # need to guarantee that the 3' UTR is sufficiently long
curr_hashed_counts = get_hashed_counts(curr_trans, gnd)
cdslen = tstop+stopnt[1]-tcoord-startnt[0] # cds length, plus the extra bases...
curr_counts = np.zeros((len(rdlens), cdslen))
for (i, rdlen) in enumerate(rdlens):
for nmis in range(opts.max5mis+1):
curr_counts[i, :] += curr_hashed_counts[(rdlen, nmis)][tcoord+startnt[0]:tstop+stopnt[1]]
# curr_counts is limited to the CDS plus any extra requested nucleotides on either side
if curr_counts.sum() >= opts.mincdsreads:
curr_counts /= curr_counts.mean() # normalize by mean of counts across all readlengths and positions within the CDS
startprof += curr_counts[:, :startlen]
cdsprof += curr_counts[:, startlen:cdslen-stoplen].reshape((len(rdlens), -1, 3)).mean(1)
stopprof += curr_counts[:, cdslen-stoplen:cdslen]
num_cds_incl += 1
for inbam in inbams:
inbam.close()
return startprof, cdsprof, stopprof, num_cds_incl
def _quantify_tfam(orf_set, gnds):
"""Performs non-negative least squares regression to quantify all of the ORFs in a transcript family, using a simplified profile consisting of
the same three numbers tiled across each ORF. All readlengths are treated identically. Regions around start and stop codons are masked in
accordance with startmask and stopmask"""
strand = orf_set['strand'].iat[0]
chrom = orf_set['chrom'].iat[0]
tids = orf_set['tid'].drop_duplicates().tolist()
all_tfam_genpos = set()
tid_genpos = {}
tlens = {}
for (i, tid) in enumerate(tids):
currtrans = SegmentChain.from_bed(bedlinedict[tid])
curr_pos_set = currtrans.get_position_set()
tlens[tid] = len(curr_pos_set)
tid_genpos[tid] = curr_pos_set
all_tfam_genpos.update(curr_pos_set)
all_tfam_genpos = np.array(sorted(all_tfam_genpos))
if strand == '-':
all_tfam_genpos = all_tfam_genpos[::-1]
nnt = len(all_tfam_genpos)
tid_indices = {
tid: np.flatnonzero(
np.in1d(all_tfam_genpos, list(curr_tid_genpos),
assume_unique=True))
for (tid, curr_tid_genpos) in tid_genpos.iteritems()
}
orf_matrix = np.zeros((nnt, len(orf_set)))
ignore_coords = []
for (orf_num,
(tid, tcoord, tstop,
AAlen)) in enumerate(orf_set[['tid', 'tcoord', 'tstop',
'AAlen']].itertuples(False)):
orf_matrix[tid_indices[tid][tcoord:tstop],
orf_num] = np.tile(cdsprof, AAlen + 1)
ignore_coords.append(tid_indices[tid][max(tcoord +
startmask[0], 0):tcoord +
startmask[1]])
ignore_coords.append(
tid_indices[tid][max(tstop + stopmask[0], 0):tstop + stopmask[1]])
ignore_coords = np.unique(np.concatenate(ignore_coords))
orf_matrix[
ignore_coords, :] = 0 # mask out all positions within the mask region around starts and stops
valid_orfs = np.array([
(orf_matrix[:, i] > 0).any()
and (orf_matrix.T[i, :] != orf_matrix.T[:i, :]).any(1).all()
for i in xrange(len(orf_set))
])
# require at least one valid position, and if >1 ORFs are identical, only include one of them
orf_matrix[:, ~valid_orfs] = 0 # completely ignore these positions
valid_nts = (orf_matrix > 0).any(
1) # only bother checking nucleotides where there is a valid ORF
orf_res = orf_set.copy()
if valid_nts.any():
orf_matrix = orf_matrix[valid_nts, :]
valid_nt_segs = SegmentChain(*positionlist_to_segments(
chrom, strand, list(all_tfam_genpos[valid_nts])))
orf_res['nts_quantified'] = (orf_matrix > 0).sum(
0) # the number of nucleotides included in the quantification
for colname, gnd in zip(colnames, gnds):
orf_res[colname] = nnls(orf_matrix,
valid_nt_segs.get_counts(gnd))[0]
# gnd is a HashedReadBAMGenomeArray, but it still works with get_counts(), which will collapse all read lengths to a single array
return orf_res
else:
orf_res['nts_quantified'] = 0
for colname in colnames:
orf_res[colname] = 0.
return orf_res
def _identify_tfam_orfs((tfam, tids)):
"""Identify all of the possible ORFs within a family of transcripts. Relevant information such as genomic start and stop positions, amino acid
length, and initiation codon will be collected for each ORF. Additionally, each ORF will be assigned a unique 'orfname', such that if it occurs
on multiple transcripts, it can be recognized as the same ORF."""
currtfam = SegmentChain.from_bed(tfambedlines[tfam])
chrom = currtfam.chrom
strand = currtfam.strand
tfam_genpos = np.array(currtfam.get_position_list())
if strand == '-':
tfam_genpos = tfam_genpos[::-1]
tmask = np.empty((len(tids), len(tfam_genpos)), dtype=np.bool) # True if transcript covers that position, False if not
tfam_orfs = []
tidx_lookup = {}
for tidx, tid in enumerate(tids):
tidx_lookup[tid] = tidx
curr_trans = Transcript.from_bed(bedlinedict[tid])
tmask[tidx, :] = np.in1d(tfam_genpos, curr_trans.get_position_list(), assume_unique=True)
trans_orfs = _find_all_orfs(curr_trans.get_sequence(genome).upper())
if trans_orfs:
(startpos, stoppos, codons) = zip(*trans_orfs)
startpos = np.array(startpos, dtype='i4')
stoppos = np.array(stoppos, dtype='i4')
gcoords = np.array([curr_trans.get_genomic_coordinate(x)[1] for x in startpos], dtype='i4')
stop_present = (stoppos > 0)
gstops = np.zeros(len(trans_orfs), dtype='i4')
gstops[stop_present] = \
np.array([curr_trans.get_genomic_coordinate(x - 1)[1] for x in stoppos[stop_present]]) + (1 if strand == '+' else -1)
# the decrementing/incrementing stuff preserves half-openness regardless of strand
AAlens = np.zeros(len(trans_orfs), dtype='i4')
AAlens[stop_present] = (stoppos[stop_present] - startpos[stop_present])/3 - 1
tfam_orfs.append(pd.DataFrame.from_items([('tfam', tfam),
('tid', tid),
('tcoord', startpos),
('tstop', stoppos),
('chrom', chrom),
('gcoord', gcoords),
('gstop', gstops),
('strand', strand),
('codon', codons),
('AAlen', AAlens),
('orfname', '')]))
if any(x is not None for x in tfam_orfs):
orf_pos_dict = {}
tfam_orfs = pd.concat(tfam_orfs, ignore_index=True)
for ((gcoord, AAlen), gcoord_grp) in tfam_orfs.groupby(['gcoord', 'AAlen']): # group by genomic start position and length
if len(gcoord_grp) == 1:
tfam_orfs.loc[gcoord_grp.index, 'orfname'] = _name_orf(tfam, gcoord, AAlen)
else:
orf_gcoords = np.vstack(np.flatnonzero(tmask[tidx_lookup[tid], :])[tcoord:tstop]
for (tid, tcoord, tstop) in gcoord_grp[['tid', 'tcoord', 'tstop']].itertuples(False))
if (orf_gcoords == orf_gcoords[0, :]).all(): # all of the grouped ORFs are identical, so should receive the same name
orfname = _name_orf(tfam, gcoord, AAlen)
tfam_orfs.loc[gcoord_grp.index, 'orfname'] = orfname
orf_pos_dict[orfname] = tfam_genpos[orf_gcoords[0, :]]
else:
named_so_far = 0
unnamed = np.ones(len(gcoord_grp), dtype=np.bool)
basename = _name_orf(tfam, gcoord, AAlen)
while unnamed.any():
next_gcoords = orf_gcoords[unnamed, :][0, :]
identicals = (orf_gcoords == next_gcoords).all(1)
orfname = '%s_%d' % (basename, named_so_far)
tfam_orfs.loc[gcoord_grp.index[identicals], 'orfname'] = orfname
orf_pos_dict[orfname] = tfam_genpos[next_gcoords]
unnamed[identicals] = False
named_so_far += 1
# Now that the ORFs have been found and named, figure out their orftype
tfam_orfs['annot_start'] = False
tfam_orfs['annot_stop'] = False # start out assuming all are False; replace with True as needed
tfam_orfs['orftype'] = 'new'
tfam_orfs['untyped'] = tfam_orfs['tstop'] > 0
tfam_orfs.loc[~tfam_orfs['untyped'], 'orftype'] = 'nonstop' # no stop codon
if tfam in tfams_with_annots:
cds_info = []
all_annot_pos = set()
for (annot_fidx, (annot_tfam_lookup, annot_tid_lookup)) in enumerate(zip(annot_tfam_lookups, annot_tid_lookups)):
if tfam in annot_tfam_lookup:
for (annot_tidx, annot_tid) in enumerate(annot_tfam_lookup[tfam]):
curr_trans = Transcript.from_bed(annot_tid_lookup[annot_tid])
if curr_trans.cds_start is not None and curr_trans.cds_end is not None:
curr_cds_pos_set = curr_trans.get_cds().get_position_set()
curr_len = len(curr_cds_pos_set)
if curr_len % 3 == 0:
curr_gcoord = curr_trans.get_genomic_coordinate(curr_trans.cds_start)[1]
curr_gstop = curr_trans.get_genomic_coordinate(curr_trans.cds_end - 1)[1] + (1 if strand == '+' else -1)
in_tfam = curr_cds_pos_set.issubset(tfam_genpos)
cds_info.append((curr_gcoord, curr_gstop, (curr_len-3)/3, in_tfam, annot_fidx, annot_tid, curr_cds_pos_set))
all_annot_pos.update(curr_cds_pos_set)
if cds_info: # False means no annotated CDSs or none are multiples of 3 in length
cds_info = pd.DataFrame(cds_info, columns=['gcoord', 'gstop', 'AAlen', 'in_tfam', 'annot_fidx', 'annot_tid', 'pos']) \
.groupby(['gcoord', 'gstop', 'AAlen', 'in_tfam'], as_index=False) \
.apply(lambda x: x if len(x) == 1 else x[[not any(pos == x['pos'].iat[j] for j in xrange(i))
for (i, pos) in enumerate(x['pos'])]]) \
.set_index(['annot_fidx', 'annot_tid'])
# this operation organizes cds_info into a dataframe and effectively drops duplicates
# pandas drop_duplicates() is incompatible with sets so have to do it this manual way
# the combination of annot_fidx (the number of the file if more than one annotation file provided) and annot_tid should be a unique ID
tfam_orfs['annot_start'] = tfam_orfs['gcoord'].isin(cds_info['gcoord'])
tfam_orfs['annot_stop'] = tfam_orfs['gstop'].isin(cds_info['gstop'])
def _get_orf_pos(orfname, tid=None, tcoord=None, tstop=None):
"""Helper function that identifies the genomic coordinates of an ORF (in stranded order) and caches them by orfname"""
if orfname in orf_pos_dict:
return orf_pos_dict[orfname]
else:
if tid is None or tcoord is None or tstop is None:
(tid, tcoord, tstop) = tfam_orfs.loc[tfam_orfs['orfname'] == orfname, ['tid', 'tcoord', 'tstop']].iloc[0]
res = tfam_genpos[np.flatnonzero(tmask[tidx_lookup[tid], :])[tcoord:tstop]]
orf_pos_dict[orfname] = res
return res
# ANNOTATED and XISO
cds_info['found'] = False
possible_annot = tfam_orfs.drop_duplicates('orfname').merge(cds_info[cds_info['in_tfam']].reset_index())
# merges on gcoord, gstop, and len - need to reset_index to preserve annot_fidx and annot_tid
for ((orfname, tid, tcoord, tstop), cds_grp) in possible_annot.groupby(['orfname', 'tid', 'tcoord', 'tstop']):
orf_pos = _get_orf_pos(orfname, tid, tcoord, tstop)
for (annot_fidx, annot_tid, cds_pos_set) in cds_grp[['annot_fidx', 'annot_tid', 'pos']].itertuples(False):
if cds_pos_set.issubset(orf_pos):
tfam_orfs.loc[tfam_orfs['orfname'] == orfname, ['orftype', 'untyped']] = ['annotated', False]
cds_info.loc[(annot_fidx, annot_tid), 'found'] = True
break
else:
tfam_orfs.loc[tfam_orfs['orfname'] == orfname, ['orftype', 'untyped']] = ['Xiso', False]
# matching start and stop but differing in between
if tfam_orfs['untyped'].any():
tfam_orfs.loc[tfam_orfs['orfname'].isin(tfam_orfs[tfam_orfs['untyped']].merge(cds_info[['gcoord', 'gstop']])['orfname']),
['orftype', 'untyped']] = ['Xiso', False]
# matching start and stop, but must differ somewhere, otherwise would have been identified as annotated (Xiso => "exact isoform")
# SISO
tfam_orfs.loc[tfam_orfs['annot_start'] & tfam_orfs['annot_stop'] & tfam_orfs['untyped'], ['orftype', 'untyped']] = ['Siso', False]
# start and stop each match at least one CDS, but not the same one (Siso => "spliced isoform")
# CISO
tfam_orfs.loc[tfam_orfs['annot_start'] & tfam_orfs['untyped'], ['orftype', 'untyped']] = ['Ciso', False]
# start is annotated, but stop is not - so must be on a new transcript (Ciso => "C-terminal isoform")
# TRUNCATION
if tfam_orfs['untyped'].any():
found_matched_stop = tfam_orfs[tfam_orfs['untyped']].merge(tfam_orfs[tfam_orfs['orftype'] == 'annotated'],
on=['tid', 'tstop'], suffixes=('', '_annot'))
tfam_orfs.loc[tfam_orfs['orfname'].isin(found_matched_stop.loc[found_matched_stop['tcoord'] > found_matched_stop['tcoord_annot'],
'orfname']), ['orftype', 'untyped']] = ['truncation', False]
# on the same transcript with an annotated CDS, with matching stop codon, initiating downstream - must be a truncation
# still some missing truncations, if the original CDS was not on a transcript in the present transcriptome
if tfam_orfs['untyped'].any() and not cds_info['found'].all():
possible_truncs = tfam_orfs[tfam_orfs['untyped']].drop_duplicates('orfname') \
.merge(cds_info.loc[~cds_info['found'], ['gstop', 'pos', 'AAlen']], on='gstop', suffixes=('', '_annot'))
possible_truncs = possible_truncs[possible_truncs['AAlen'] < possible_truncs['AAlen_annot']]
for ((orfname, tid, tcoord, tstop, gcoord), cds_pos_sets) in \
possible_truncs.groupby(['orfname', 'tid', 'tcoord', 'tstop', 'gcoord'])['pos']:
orf_pos = _get_orf_pos(orfname, tid, tcoord, tstop)
if strand == '-':
if any(cds_pos_set.issuperset(orf_pos) and
all(pos in orf_pos for pos in cds_pos_set if pos <= gcoord) for cds_pos_set in cds_pos_sets):
tfam_orfs.loc[tfam_orfs['orfname'] == orfname, ['orftype', 'untyped']] = ['truncation', False]
else:
if any(cds_pos_set.issuperset(orf_pos) and
all(pos in orf_pos for pos in cds_pos_set if pos >= gcoord) for cds_pos_set in cds_pos_sets):
tfam_orfs.loc[tfam_orfs['orfname'] == orfname, ['orftype', 'untyped']] = ['truncation', False]
# matching stop codon, contained within, and all positions in the annotation past the orf start codon are included in the orf
# EXTENSION
if tfam_orfs['untyped'].any():
found_matched_stop = tfam_orfs[tfam_orfs['untyped']].merge(tfam_orfs[tfam_orfs['orftype'] == 'annotated'],
on=['tid', 'tstop'], suffixes=('', '_annot'))
assert (found_matched_stop['tcoord'] < found_matched_stop['tcoord_annot']).all() # other possibilities should be done by now
tfam_orfs.loc[tfam_orfs['orfname'].isin(found_matched_stop['orfname']), ['orftype', 'untyped']] = ['extension', False]
# on the same transcript with an annotated CDS, with matching stop codon, initiating upstream - must be an extension
# no possibility for an "unfound" extension - if the extension is in the transcriptome, the CDS it comes from must be as well
# (except for a few edge cases e.g. annotated CDS is a CUG initiator, but not considering CUG ORFs)
# NISO
tfam_orfs.loc[tfam_orfs['annot_stop'] & (tfam_orfs['untyped']), ['orftype', 'untyped']] = ['Niso', False]
# stop is annotated, but start is not, and it's not a truncation or extension - so must be an isoform (Niso => "N-terminal isoform")
# NCISO
if tfam_orfs['untyped'].any():
orf_codons = []
for (orfname, tid, tcoord, tstop) in \
tfam_orfs.loc[tfam_orfs['untyped'], ['orfname', 'tid', 'tcoord', 'tstop']].drop_duplicates('orfname').itertuples(False):
orf_codons.append(pd.DataFrame(_get_orf_pos(orfname, tid, tcoord, tstop).reshape((-1, 3))))
orf_codons[-1]['orfname'] = orfname
orf_codons = pd.concat(orf_codons, ignore_index=True)
if strand == '-':
annot_codons = pd.DataFrame(np.vstack([np.reshape(sorted(cds_pos_set, reverse=True), (-1, 3))
for cds_pos_set in cds_info['pos'] if len(cds_pos_set) % 3 == 0])).drop_duplicates()
else:
annot_codons = pd.DataFrame(np.vstack([np.reshape(sorted(cds_pos_set, reverse=False), (-1, 3))
for cds_pos_set in cds_info['pos'] if len(cds_pos_set) % 3 == 0])).drop_duplicates()
tfam_orfs.loc[tfam_orfs['orfname'].isin(orf_codons.merge(annot_codons)['orfname']), ['orftype', 'untyped']] = ['NCiso', False]
# ORFs that have at least one full codon overlapping (in-frame) with a CDS are isoforms (NCiso => "N- and C-terminal isoform")
# Note that these must already differ at N- and C- termini, otherwise they would already have been classified
# INTERNAL
if tfam_orfs['untyped'].any():
sametrans = tfam_orfs[tfam_orfs['untyped']].merge(tfam_orfs[tfam_orfs['orftype'] == 'annotated'],
on='tid', suffixes=('', '_annot'))
sametrans_internal = (sametrans['tcoord'] > sametrans['tcoord_annot']) & (sametrans['tstop'] < sametrans['tstop_annot'])
tfam_orfs.loc[tfam_orfs['orfname'].isin(sametrans.loc[sametrans_internal, 'orfname']),
['orftype', 'untyped']] = ['internal', False]
# ORFs completely contained within a CDS on the same transcript, and not containing any full codon overlaps, must be internal
# Still could be other ORFs internal to a CDS on a transcript not in the current transcriptome - need to check manually
if tfam_orfs['untyped'].any() and not cds_info['found'].all():
for (orfname, gcoord, gstop) in \
tfam_orfs.loc[tfam_orfs['untyped'], ['orfname', 'gcoord', 'gstop']].drop_duplicates('orfname').itertuples(False):
orf_pos = _get_orf_pos(orfname) # should be cached by now
if strand == '-':
if any(cds_pos_set.issuperset(orf_pos) and all(pos in orf_pos for pos in cds_pos_set if gcoord >= pos > gstop)
for cds_pos_set in cds_info.loc[(~cds_info['found'])
& (cds_info['gcoord'] > gcoord)
& (cds_info['gstop'] < gstop), 'pos']):
tfam_orfs.loc[tfam_orfs['orfname'] == orfname, ['orftype', 'untyped']] = ['internal', False]
else:
if any(cds_pos_set.issuperset(orf_pos) and all(pos in orf_pos for pos in cds_pos_set if gcoord <= pos < gstop)
for cds_pos_set in cds_info.loc[(~cds_info['found'])
& (cds_info['gcoord'] < gcoord)
& (cds_info['gstop'] > gstop), 'pos']):
tfam_orfs.loc[tfam_orfs['orfname'] == orfname, ['orftype', 'untyped']] = ['internal', False]
# STOP_OVERLAP
if tfam_orfs['untyped'].any():
sametrans = tfam_orfs[tfam_orfs['untyped']].merge(tfam_orfs[tfam_orfs['orftype'] == 'annotated'],
on='tid', suffixes=('', '_annot'))
sametrans_stopover = (sametrans['tcoord'] > sametrans['tcoord_annot']) & (sametrans['tcoord'] < sametrans['tstop_annot'])
tfam_orfs.loc[tfam_orfs['orfname'].isin(sametrans.loc[sametrans_stopover, 'orfname']),
['orftype', 'untyped']] = ['stop_overlap', False]
# starts within a CDS and not an internal - must be a stop_overlap
# do not need to check for unfounds - requiring that stop_overlap must be on same transcript as cds
# START_OVERLAP
if tfam_orfs['untyped'].any():
sametrans = tfam_orfs[tfam_orfs['untyped']].merge(tfam_orfs[tfam_orfs['orftype'] == 'annotated'],
on='tid', suffixes=('', '_annot'))
sametrans_startover = (sametrans['tstop'] > sametrans['tcoord_annot']) & (sametrans['tstop'] < sametrans['tstop_annot'])
tfam_orfs.loc[tfam_orfs['orfname'].isin(sametrans.loc[sametrans_startover, 'orfname']),
['orftype', 'untyped']] = ['start_overlap', False]
# ends within a CDS and not an internal - must be a start_overlap
# do not need to check for unfounds - requiring that start_overlap must be on same transcript as cds
# LOOF
if tfam_orfs['untyped'].any():
sametrans = tfam_orfs[tfam_orfs['untyped']].merge(tfam_orfs[tfam_orfs['orftype'] == 'annotated'],
on='tid', suffixes=('', '_annot'))
sametrans_loof = (sametrans['tcoord'] < sametrans['tcoord_annot']) & (sametrans['tstop'] > sametrans['tstop_annot'])
tfam_orfs.loc[tfam_orfs['orfname'].isin(sametrans.loc[sametrans_loof, 'orfname']), ['orftype', 'untyped']] = ['LOOF', False]
# starts upstream of a CDS and ends downstream of it - must be a LOOF (long out-of-frame)
# don't need to check for unfounds because the CDS must be on the same transcript as the ORF if the ORF completely contains it
# UPSTREAM
if tfam_orfs['untyped'].any():
sametrans = tfam_orfs[tfam_orfs['untyped']].merge(tfam_orfs[tfam_orfs['orftype'] == 'annotated'],
on='tid', suffixes=('', '_annot'))
sametrans_upstream = (sametrans['tstop'] <= sametrans['tcoord_annot'])
tfam_orfs.loc[tfam_orfs['orfname'].isin(sametrans.loc[sametrans_upstream, 'orfname']),
['orftype', 'untyped']] = ['upstream', False]
# ends upstream of a CDS - must be an upstream (uORF)
# cannot check manually for unfounds because those are not on well-defined transcripts
# DOWNSTREAM
if tfam_orfs['untyped'].any():
sametrans = tfam_orfs[tfam_orfs['untyped']].merge(tfam_orfs[tfam_orfs['orftype'] == 'annotated'],
on='tid', suffixes=('', '_annot'))
sametrans_downstream = (sametrans['tstop_annot'] <= sametrans['tcoord'])
tfam_orfs.loc[tfam_orfs['orfname'].isin(sametrans.loc[sametrans_downstream, 'orfname']),
['orftype', 'untyped']] = ['downstream', False]
# starts downstream of a CDS - must be an upstream (uORF)
# cannot check manually for unfounds because those are not on well-defined transcripts
# NEW_ISO and GISO
for orfname in tfam_orfs.loc[tfam_orfs['untyped'], 'orfname'].drop_duplicates():
if all_annot_pos.isdisjoint(_get_orf_pos(orfname)):
tfam_orfs.loc[tfam_orfs['orfname'] == orfname, ['orftype', 'untyped']] = ['new_iso', False]
# no overlaps whatsoever with any annotated CDS, but in a tfam that has annotations: new_iso
else:
tfam_orfs.loc[tfam_orfs['orfname'] == orfname, ['orftype', 'untyped']] = ['Giso', False]
# overlaps out-of-frame with a CDS, and not on the same transcript with a CDS: Giso => "genomic isoform"
assert not tfam_orfs['untyped'].any()
return tfam_orfs.drop('untyped', axis=1)
else:
return None
def _get_tid_info(tup):
"""For each transcript on this chromosome/strand, identifies every sub-sequence of the appropriate length (fpsize), converts it to an integer,
identifies the number of reads mapping to that position, and outputs all of that information to a pandas HDF store."""
(chrom, strand) = tup
inbams = [pysam.Samfile(infile, 'rb') for infile in opts.bamfiles]
gnd = BAMGenomeArray(inbams, mapping=FivePrimeMapFactory(psite))
# map to roughly the center of each read so that identical sequences that cross different splice sites
# (on different transcripts) still end up mapping to the same place
gnd.add_filter('size', SizeFilterFactory(opts.minlen, opts.maxlen))
tid_seq_info = []
tid_summary = pd.DataFrame(
{
'chrom': chrom,
'strand': strand,
'n_psite': -1,
'n_reads': -1,
'peak_reads': -1,
'dropped': ''
},
index=pd.Index(bedlinedict[(chrom, strand)].keys(), name='tid'))
for (tid, line) in bedlinedict[(chrom, strand)].iteritems():
currtrans = SegmentChain.from_bed(line)
curr_pos_list = currtrans.get_position_list() # not in stranded order!
if strand == '-':
curr_pos_list = curr_pos_list[::-1]
n_psite = len(curr_pos_list) + 1 - fpsize
tid_summary.at[tid, 'n_psite'] = n_psite
if n_psite > 0:
curr_counts = np.array(currtrans.get_counts(gnd))[psite:n_psite +
psite]
# if((curr_counts>0).any()):
sumcounts = curr_counts.sum()
maxcounts = curr_counts.max()
tid_summary.at[tid, 'n_reads'] = sumcounts
tid_summary.at[tid, 'peak_reads'] = maxcounts
if sumcounts >= opts.minreads:
if maxcounts < sumcounts * opts.peakfrac:
numseq = np.array(
list(
currtrans.get_sequence(genome).upper().translate(
str_dict)))
curr_seq = ''.join(numseq)
tid_seq_info.append(
pd.DataFrame({
'tid':
tid,
'genpos':
curr_pos_list[psite:n_psite + psite],
'seq':
np.array([(int(curr_seq[i:i + fpsize], 4) if 'N'
not in curr_seq[i:i + fpsize] else -1)
for i in xrange(n_psite)],
dtype=np.int64),
'reads':
curr_counts
}))
else:
tid_summary.at[tid, 'dropped'] = 'peakfrac'
else:
tid_summary.at[tid, 'dropped'] = 'lowreads'
if tid_seq_info: # don't bother saving anything if there's nothing to save
pd.concat(tid_seq_info,
ignore_index=True).to_hdf(seq_info_hdf % (chrom, strand),
'tid_seq_info',
format='t',
data_columns=True,
complevel=1,
complib='blosc')
# sp.call(['ptrepack', orig_store_name, seq_info_hdf%(chrom,strand)]) # repack for efficiency
# os.remove(orig_store_name)
if opts.verbose > 1:
with log_lock:
logprint('%s (%s strand) complete' % (chrom, strand))
for inbam in inbams:
inbam.close()
return tid_summary
def _regress_tfam(orf_set, gnd):
"""Performs non-negative least squares regression on all of the ORFs in a transcript family, using profiles constructed via _orf_profile()
Also calculates Wald statistics for each orf and start codon, and for each stop codon if opts.startonly is False"""
tfam = orf_set['tfam'].iat[0]
strand = orf_set['strand'].iat[0]
chrom = orf_set['chrom'].iat[0]
tids = orf_set['tid'].drop_duplicates().tolist()
all_tfam_genpos = set()
tid_genpos = {}
tlens = {}
for (i, tid) in enumerate(tids):
currtrans = SegmentChain.from_bed(bedlinedict[tid])
curr_pos_set = currtrans.get_position_set()
tlens[tid] = len(curr_pos_set)
tid_genpos[tid] = curr_pos_set
all_tfam_genpos.update(curr_pos_set)
tfam_segs = SegmentChain(*positionlist_to_segments(chrom, strand, list(all_tfam_genpos)))
all_tfam_genpos = np.array(sorted(all_tfam_genpos))
if strand == '-':
all_tfam_genpos = all_tfam_genpos[::-1]
nnt = len(all_tfam_genpos)
tid_indices = {tid: np.flatnonzero(np.in1d(all_tfam_genpos, list(curr_tid_genpos), assume_unique=True))
for (tid, curr_tid_genpos) in tid_genpos.iteritems()}
hashed_counts = get_hashed_counts(tfam_segs, gnd)
counts = np.zeros((len(rdlens), nnt), dtype=np.float64) # even though they are integer-valued, will need to do float arithmetic
for (i, rdlen) in enumerate(rdlens):
for nmis in range(1+opts.max5mis):
counts[i, :] += hashed_counts[(rdlen, nmis)]
counts = counts.ravel()
if opts.startcount:
# Only include ORFS for which there is at least some minimum reads within one nucleotide of the start codon
offsetmat = np.tile(nnt*np.arange(len(rdlens)), 3) # offsets for each cond, expecting three positions to check for each
# try:
orf_set = orf_set[[(counts[(start_idxes.repeat(len(rdlens))+offsetmat)].sum() >= opts.startcount) for start_idxes in
[tid_indices[tid][tcoord-1:tcoord+2] for (tid, tcoord, tstop) in orf_set[['tid', 'tcoord', 'tstop']].itertuples(False)]]]
if orf_set.empty:
return failure_return
orf_strength_df = orf_set.sort_values('tcoord', ascending=False).drop_duplicates('orfname').reset_index(drop=True)
abort_set = orf_set.drop_duplicates('gcoord').copy()
abort_set['gstop'] = abort_set['gcoord'] # should maybe be +/-3, but then need to worry about splicing - and this is an easy flag
abort_set['tstop'] = abort_set['tcoord']+3 # stop after the first codon
abort_set['orfname'] = abort_set['gcoord'].apply(lambda x: '%s_%d_abort' % (tfam, x))
orf_strength_df = pd.concat((orf_strength_df, abort_set), ignore_index=True)
if not opts.startonly: # if marking full ORFs, include histop model
stop_set = orf_set.drop_duplicates('gstop').copy()
stop_set['gcoord'] = stop_set['gstop'] # this is an easy flag
stop_set['tcoord'] = stop_set['tstop'] # should probably be -3 nt, but this is another easy flag that distinguishes from abinit
stop_set['orfname'] = stop_set['gstop'].apply(lambda x: '%s_%d_stop' % (tfam, x))
orf_strength_df = pd.concat((orf_strength_df, stop_set), ignore_index=True)
orf_profs = []
indices = []
for (tid, tcoord, tstop) in orf_strength_df[['tid', 'tcoord', 'tstop']].itertuples(False):
if tcoord != tstop: # not a histop
tlen = tlens[tid]
if tcoord+startnt[0] < 0:
startadj = -startnt[0]-tcoord # number of nts to remove from the start due to short 5' UTR; guaranteed > 0
else:
startadj = 0
if tstop+stopnt[1] > tlen:
stopadj = tstop+stopnt[1]-tlen # number of nts to remove from the end due to short 3' UTR; guaranteed > 0
else:
stopadj = 0
curr_indices = tid_indices[tid][tcoord+startnt[0]+startadj:tstop+stopnt[1]-stopadj]
orf_profs.append(_orf_profile(tstop-tcoord)[:, startadj:tstop-tcoord+stopnt[1]-startnt[0]-stopadj].ravel())
else: # histop
curr_indices = tid_indices[tid][tstop-6:tstop]
orf_profs.append(stopprof[:, -6:].ravel())
indices.append(np.concatenate([nnt*i+curr_indices for i in xrange(len(rdlens))]))
# need to tile the indices for each read length
if len(indices[-1]) != len(orf_profs[-1]):
raise AssertionError('ORF length does not match index length')
orf_matrix = scipy.sparse.csc_matrix((np.concatenate(orf_profs),
np.concatenate(indices),
np.cumsum([0]+[len(curr_indices) for curr_indices in indices])),
shape=(nnt*len(rdlens), len(orf_strength_df)))
# better to make it a sparse matrix, even though nnls requires a dense matrix, because of linear algebra to come
nonzero_orfs = np.flatnonzero(orf_matrix.T.dot(counts) > 0)
if len(nonzero_orfs) == 0: # no possibility of anything coming up
return failure_return
orf_matrix = orf_matrix[:, nonzero_orfs]
orf_strength_df = orf_strength_df.iloc[nonzero_orfs] # don't bother fitting ORFs with zero reads throughout their entire length
(orf_strs, resid) = nnls(orf_matrix.toarray(), counts)
min_str = 1e-6 # allow for machine rounding error
usable_orfs = orf_strs > min_str
if not usable_orfs.any():
return failure_return
orf_strength_df = orf_strength_df[usable_orfs]
orf_matrix = orf_matrix[:, usable_orfs] # remove entries for zero-strength ORFs or transcripts
orf_strs = orf_strs[usable_orfs]
orf_strength_df['orf_strength'] = orf_strs
covmat = resid*resid*np.linalg.inv(orf_matrix.T.dot(orf_matrix).toarray())/(nnt*len(rdlens)-len(orf_strength_df))
# homoscedastic version (assume equal variance at all positions)
# resids = counts-orf_matrix.dot(orf_strs)
# simple_covmat = np.linalg.inv(orf_matrix.T.dot(orf_matrix).toarray())
# covmat = simple_covmat.dot(orf_matrix.T.dot(scipy.sparse.dia_matrix((resids*resids, 0), (len(resids), len(resids))))
# .dot(orf_matrix).dot(simple_covmat))
# # heteroscedastic version (Eicker-Huber-White robust estimator)
orf_strength_df['W_orf'] = orf_strength_df['orf_strength']*orf_strength_df['orf_strength']/np.diag(covmat)
orf_strength_df.set_index('orfname', inplace=True)
elongating_orfs = ~(orf_strength_df['gstop'] == orf_strength_df['gcoord'])
if opts.startonly: # count abortive initiation events towards start strength in this case
include_starts = (orf_strength_df['tcoord'] != orf_strength_df['tstop'])
gcoord_grps = orf_strength_df[include_starts].groupby('gcoord')
# even if we are willing to count abinit towards start strength, we certainly shouldn't count histop
covmat_starts = covmat[np.ix_(include_starts.values, include_starts.values)]
orf_strs_starts = orf_strs[include_starts.values]
else:
gcoord_grps = orf_strength_df[elongating_orfs].groupby('gcoord')
covmat_starts = covmat[np.ix_(elongating_orfs.values, elongating_orfs.values)]
orf_strs_starts = orf_strs[elongating_orfs.values]
start_strength_df = pd.DataFrame.from_items([('tfam', tfam),
('chrom', orf_set['chrom'].iloc[0]),
('strand', orf_set['strand'].iloc[0]),
('codon', gcoord_grps['codon'].first()),
('start_strength', gcoord_grps['orf_strength'].aggregate(np.sum))])
start_strength_df['W_start'] = pd.Series({gcoord: orf_strs_starts[rownums].dot(np.linalg.inv(covmat_starts[np.ix_(rownums, rownums)]))
.dot(orf_strs_starts[rownums]) for (gcoord, rownums) in gcoord_grps.indices.iteritems()})
if not opts.startonly:
# count histop towards the stop codon - but still exclude abinit
include_stops = (elongating_orfs | (orf_strength_df['tcoord'] == orf_strength_df['tstop']))
gstop_grps = orf_strength_df[include_stops].groupby('gstop')
covmat_stops = covmat[np.ix_(include_stops.values, include_stops.values)]
orf_strs_stops = orf_strs[include_stops.values]
stop_strength_df = pd.DataFrame.from_items([('tfam', tfam),
('chrom', orf_set['chrom'].iloc[0]),
('strand', orf_set['strand'].iloc[0]),
('stop_strength', gstop_grps['orf_strength'].aggregate(np.sum))])
stop_strength_df['W_stop'] = pd.Series({gstop: orf_strs_stops[rownums].dot(np.linalg.inv(covmat_stops[np.ix_(rownums, rownums)]))
.dot(orf_strs_stops[rownums]) for (gstop, rownums) in gstop_grps.indices.iteritems()})
# # nohistop
# gstop_grps = orf_strength_df[elongating_orfs].groupby('gstop')
# covmat_stops = covmat[np.ix_(elongating_orfs.values, elongating_orfs.values)]
# orf_strs_stops = orf_strs[elongating_orfs.values]
# stop_strength_df['stop_strength_nohistop'] = gstop_grps['orf_strength'].aggregate(np.sum)
# stop_strength_df['W_stop_nohistop'] = pd.Series({gstop:orf_strs_stops[rownums].dot(np.linalg.inv(covmat_stops[np.ix_(rownums,rownums)]))
# .dot(orf_strs_stops[rownums]) for (gstop, rownums) in gstop_grps.indices.iteritems()})
return orf_strength_df, start_strength_df, stop_strength_df
else:
return orf_strength_df, start_strength_df
def _regress_tfam(orf_set, gnd):
"""Performs non-negative least squares regression on all of the ORFs in a transcript family, using profiles constructed via _orf_profile()
Also calculates Wald statistics for each orf and start codon, and for each stop codon if opts.startonly is False"""
tfam = orf_set['tfam'].iat[0]
strand = orf_set['strand'].iat[0]
chrom = orf_set['chrom'].iat[0]
tids = orf_set['tid'].drop_duplicates().tolist()
all_tfam_genpos = set()
tid_genpos = {}
tlens = {}
for (i, tid) in enumerate(tids):
currtrans = SegmentChain.from_bed(bedlinedict[tid])
curr_pos_set = currtrans.get_position_set()
tlens[tid] = len(curr_pos_set)
tid_genpos[tid] = curr_pos_set
all_tfam_genpos.update(curr_pos_set)
tfam_segs = SegmentChain(*positionlist_to_segments(chrom, strand, list(all_tfam_genpos)))
all_tfam_genpos = np.array(sorted(all_tfam_genpos))
if strand == '-':
all_tfam_genpos = all_tfam_genpos[::-1]
nnt = len(all_tfam_genpos)
tid_indices = {tid: np.flatnonzero(np.in1d(all_tfam_genpos, list(curr_tid_genpos), assume_unique=True))
for (tid, curr_tid_genpos) in tid_genpos.iteritems()}
hashed_counts = get_hashed_counts(tfam_segs, gnd)
counts = np.zeros((len(rdlens), nnt), dtype=np.float64) # even though they are integer-valued, will need to do float arithmetic
for (i, rdlen) in enumerate(rdlens):
for nmis in range(1+opts.max5mis):
counts[i, :] += hashed_counts[(rdlen, nmis)]
counts = counts.ravel()
if opts.startcount:
# Only include ORFS for which there is at least some minimum reads within one nucleotide of the start codon
offsetmat = np.tile(nnt*np.arange(len(rdlens)), 3) # offsets for each cond, expecting three positions to check for each
# try:
orf_set = orf_set[[(counts[(start_idxes.repeat(len(rdlens))+offsetmat)].sum() >= opts.startcount) for start_idxes in
[tid_indices[tid][tcoord-1:tcoord+2] for (tid, tcoord, tstop) in orf_set[['tid', 'tcoord', 'tstop']].itertuples(False)]]]
if orf_set.empty:
return failure_return
orf_strength_df = orf_set.sort_values('tcoord', ascending=False).drop_duplicates('orfname').reset_index(drop=True)
abort_set = orf_set.drop_duplicates('gcoord').copy()
abort_set['gstop'] = abort_set['gcoord'] # should maybe be +/-3, but then need to worry about splicing - and this is an easy flag
abort_set['tstop'] = abort_set['tcoord']+3 # stop after the first codon
abort_set['orfname'] = abort_set['gcoord'].apply(lambda x: '%s_%d_abort' % (tfam, x))
orf_strength_df = pd.concat((orf_strength_df, abort_set), ignore_index=True)
if not opts.startonly: # if marking full ORFs, include histop model
stop_set = orf_set.drop_duplicates('gstop').copy()
stop_set['gcoord'] = stop_set['gstop'] # this is an easy flag
stop_set['tcoord'] = stop_set['tstop'] # should probably be -3 nt, but this is another easy flag that distinguishes from abinit
stop_set['orfname'] = stop_set['gstop'].apply(lambda x: '%s_%d_stop' % (tfam, x))
orf_strength_df = pd.concat((orf_strength_df, stop_set), ignore_index=True)
orf_profs = []
indices = []
for (tid, tcoord, tstop) in orf_strength_df[['tid', 'tcoord', 'tstop']].itertuples(False):
if tcoord != tstop: # not a histop
tlen = tlens[tid]
if tcoord+startnt[0] < 0:
startadj = -startnt[0]-tcoord # number of nts to remove from the start due to short 5' UTR; guaranteed > 0
else:
startadj = 0
if tstop+stopnt[1] > tlen:
stopadj = tstop+stopnt[1]-tlen # number of nts to remove from the end due to short 3' UTR; guaranteed > 0
else:
stopadj = 0
curr_indices = tid_indices[tid][tcoord+startnt[0]+startadj:tstop+stopnt[1]-stopadj]
orf_profs.append(_orf_profile(tstop-tcoord)[:, startadj:tstop-tcoord+stopnt[1]-startnt[0]-stopadj].ravel())
else: # histop
curr_indices = tid_indices[tid][tstop-6:tstop]
orf_profs.append(stopprof[:, -6:].ravel())
indices.append(np.concatenate([nnt*i+curr_indices for i in xrange(len(rdlens))]))
# need to tile the indices for each read length
if len(indices[-1]) != len(orf_profs[-1]):
raise AssertionError('ORF length does not match index length')
orf_matrix = scipy.sparse.csc_matrix((np.concatenate(orf_profs),
np.concatenate(indices),
np.cumsum([0]+[len(curr_indices) for curr_indices in indices])),
shape=(nnt*len(rdlens), len(orf_strength_df)))
# better to make it a sparse matrix, even though nnls requires a dense matrix, because of linear algebra to come
nonzero_orfs = np.flatnonzero(orf_matrix.T.dot(counts) > 0)
if len(nonzero_orfs) == 0: # no possibility of anything coming up
return failure_return
orf_matrix = orf_matrix[:, nonzero_orfs]
orf_strength_df = orf_strength_df.iloc[nonzero_orfs] # don't bother fitting ORFs with zero reads throughout their entire length
(orf_strs, resid) = nnls(orf_matrix.toarray(), counts)
min_str = 1e-6 # allow for machine rounding error
usable_orfs = orf_strs > min_str
if not usable_orfs.any():
return failure_return
orf_strength_df = orf_strength_df[usable_orfs]
orf_matrix = orf_matrix[:, usable_orfs] # remove entries for zero-strength ORFs or transcripts
orf_strs = orf_strs[usable_orfs]
orf_strength_df['orf_strength'] = orf_strs
covmat = resid*resid*np.linalg.inv(orf_matrix.T.dot(orf_matrix).toarray())/(nnt*len(rdlens)-len(orf_strength_df))
# homoscedastic version (assume equal variance at all positions)
# resids = counts-orf_matrix.dot(orf_strs)
# simple_covmat = np.linalg.inv(orf_matrix.T.dot(orf_matrix).toarray())
# covmat = simple_covmat.dot(orf_matrix.T.dot(scipy.sparse.dia_matrix((resids*resids, 0), (len(resids), len(resids))))
# .dot(orf_matrix).dot(simple_covmat))
# # heteroscedastic version (Eicker-Huber-White robust estimator)
orf_strength_df['W_orf'] = orf_strength_df['orf_strength']*orf_strength_df['orf_strength']/np.diag(covmat)
orf_strength_df.set_index('orfname', inplace=True)
elongating_orfs = ~(orf_strength_df['gstop'] == orf_strength_df['gcoord'])
if opts.startonly: # count abortive initiation events towards start strength in this case
include_starts = (orf_strength_df['tcoord'] != orf_strength_df['tstop'])
if not include_starts.any():
return failure_return # no need to keep going if there weren't any useful starts
gcoord_grps = orf_strength_df[include_starts].groupby('gcoord')
# even if we are willing to count abinit towards start strength, we certainly shouldn't count histop
covmat_starts = covmat[np.ix_(include_starts.values, include_starts.values)]
orf_strs_starts = orf_strs[include_starts.values]
else:
if not elongating_orfs.any():
return failure_return
gcoord_grps = orf_strength_df[elongating_orfs].groupby('gcoord')
covmat_starts = covmat[np.ix_(elongating_orfs.values, elongating_orfs.values)]
orf_strs_starts = orf_strs[elongating_orfs.values]
start_strength_df = pd.DataFrame.from_items([('tfam', tfam),
('chrom', orf_set['chrom'].iloc[0]),
('strand', orf_set['strand'].iloc[0]),
('codon', gcoord_grps['codon'].first()),
('start_strength', gcoord_grps['orf_strength'].aggregate(np.sum))])
start_strength_df['W_start'] = pd.Series({gcoord: orf_strs_starts[rownums].dot(np.linalg.inv(covmat_starts[np.ix_(rownums, rownums)]))
.dot(orf_strs_starts[rownums]) for (gcoord, rownums) in gcoord_grps.indices.iteritems()})
if not opts.startonly:
# count histop towards the stop codon - but still exclude abinit
include_stops = (elongating_orfs | (orf_strength_df['tcoord'] == orf_strength_df['tstop']))
gstop_grps = orf_strength_df[include_stops].groupby('gstop')
covmat_stops = covmat[np.ix_(include_stops.values, include_stops.values)]
orf_strs_stops = orf_strs[include_stops.values]
stop_strength_df = pd.DataFrame.from_items([('tfam', tfam),
('chrom', orf_set['chrom'].iloc[0]),
('strand', orf_set['strand'].iloc[0]),
('stop_strength', gstop_grps['orf_strength'].aggregate(np.sum))])
stop_strength_df['W_stop'] = pd.Series({gstop: orf_strs_stops[rownums].dot(np.linalg.inv(covmat_stops[np.ix_(rownums, rownums)]))
.dot(orf_strs_stops[rownums]) for (gstop, rownums) in gstop_grps.indices.iteritems()})
# # nohistop
# gstop_grps = orf_strength_df[elongating_orfs].groupby('gstop')
# covmat_stops = covmat[np.ix_(elongating_orfs.values, elongating_orfs.values)]
# orf_strs_stops = orf_strs[elongating_orfs.values]
# stop_strength_df['stop_strength_nohistop'] = gstop_grps['orf_strength'].aggregate(np.sum)
# stop_strength_df['W_stop_nohistop'] = pd.Series({gstop:orf_strs_stops[rownums].dot(np.linalg.inv(covmat_stops[np.ix_(rownums,rownums)]))
# .dot(orf_strs_stops[rownums]) for (gstop, rownums) in gstop_grps.indices.iteritems()})
return orf_strength_df, start_strength_df, stop_strength_df
else:
return orf_strength_df, start_strength_df
def _identify_tfam_orfs(tup):
"""Identify all of the possible ORFs within a family of transcripts. Relevant information such as genomic start and stop positions, amino acid
length, and initiation codon will be collected for each ORF. Additionally, each ORF will be assigned a unique 'orfname', such that if it occurs
on multiple transcripts, it can be recognized as the same ORF."""
(tfam, tids) = tup
currtfam = SegmentChain.from_bed(tfambedlines[tfam])
chrom = currtfam.chrom
strand = currtfam.strand
tfam_genpos = np.array(currtfam.get_position_list())
if strand == '-':
tfam_genpos = tfam_genpos[::-1]
tmask = np.empty(
(len(tids), len(tfam_genpos)),
dtype=np.bool) # True if transcript covers that position, False if not
tfam_orfs = []
tidx_lookup = {}
for tidx, tid in enumerate(tids):
tidx_lookup[tid] = tidx
curr_trans = Transcript.from_bed(bedlinedict[tid])
tmask[tidx, :] = np.in1d(tfam_genpos,
curr_trans.get_position_list(),
assume_unique=True)
trans_orfs = _find_all_orfs(curr_trans.get_sequence(genome).upper())
if trans_orfs:
(startpos, stoppos, codons) = zip(*trans_orfs)
startpos = np.array(startpos, dtype='i4')
stoppos = np.array(stoppos, dtype='i4')
gcoords = np.array(
[curr_trans.get_genomic_coordinate(x)[1] for x in startpos],
dtype='i4')
stop_present = (stoppos > 0)
gstops = np.zeros(len(trans_orfs), dtype='i4')
gstops[stop_present] = \
np.array([curr_trans.get_genomic_coordinate(x - 1)[1] for x in stoppos[stop_present]]) + (1 if strand == '+' else -1)
# the decrementing/incrementing stuff preserves half-openness regardless of strand
AAlens = np.zeros(len(trans_orfs), dtype='i4')
AAlens[stop_present] = (stoppos[stop_present] -
startpos[stop_present]) / 3 - 1
tfam_orfs.append(
pd.DataFrame.from_items([('tfam', tfam), ('tid', tid),
('tcoord', startpos),
('tstop', stoppos), ('chrom', chrom),
('gcoord', gcoords),
('gstop', gstops), ('strand', strand),
('codon', codons), ('AAlen', AAlens),
('orfname', '')]))
if any(x is not None for x in tfam_orfs):
orf_pos_dict = {}
tfam_orfs = pd.concat(tfam_orfs, ignore_index=True)
for ((gcoord, AAlen), gcoord_grp) in tfam_orfs.groupby(
['gcoord', 'AAlen']): # group by genomic start position and length
if len(gcoord_grp) == 1:
tfam_orfs.loc[gcoord_grp.index,
'orfname'] = _name_orf(tfam, gcoord, AAlen)
else:
orf_gcoords = np.vstack(
np.flatnonzero(tmask[tidx_lookup[tid], :])[tcoord:tstop]
for (tid, tcoord, tstop) in gcoord_grp[
['tid', 'tcoord', 'tstop']].itertuples(False))
if (orf_gcoords == orf_gcoords[0, :]).all(
): # all of the grouped ORFs are identical, so should receive the same name
orfname = _name_orf(tfam, gcoord, AAlen)
tfam_orfs.loc[gcoord_grp.index, 'orfname'] = orfname
orf_pos_dict[orfname] = tfam_genpos[orf_gcoords[0, :]]
else:
named_so_far = 0
unnamed = np.ones(len(gcoord_grp), dtype=np.bool)
basename = _name_orf(tfam, gcoord, AAlen)
while unnamed.any():
next_gcoords = orf_gcoords[unnamed, :][0, :]
identicals = (orf_gcoords == next_gcoords).all(1)
orfname = '%s_%d' % (basename, named_so_far)
tfam_orfs.loc[gcoord_grp.index[identicals],
'orfname'] = orfname
orf_pos_dict[orfname] = tfam_genpos[next_gcoords]
unnamed[identicals] = False
named_so_far += 1
# Now that the ORFs have been found and named, figure out their orftype
tfam_orfs['annot_start'] = False
tfam_orfs[
'annot_stop'] = False # start out assuming all are False; replace with True as needed
tfam_orfs['orftype'] = 'new'
tfam_orfs['untyped'] = tfam_orfs['tstop'] > 0
tfam_orfs.loc[~tfam_orfs['untyped'],
'orftype'] = 'nonstop' # no stop codon
if tfam in tfams_with_annots:
cds_info = []
all_annot_pos = set()
for (annot_fidx, (annot_tfam_lookup,
annot_tid_lookup)) in enumerate(
zip(annot_tfam_lookups, annot_tid_lookups)):
if tfam in annot_tfam_lookup:
for (annot_tidx,
annot_tid) in enumerate(annot_tfam_lookup[tfam]):
curr_trans = Transcript.from_bed(
annot_tid_lookup[annot_tid])
if curr_trans.cds_start is not None and curr_trans.cds_end is not None:
curr_cds_pos_set = curr_trans.get_cds(
).get_position_set()
curr_len = len(curr_cds_pos_set)
if curr_len % 3 == 0:
curr_gcoord = curr_trans.get_genomic_coordinate(
curr_trans.cds_start)[1]
curr_gstop = curr_trans.get_genomic_coordinate(
curr_trans.cds_end -
1)[1] + (1 if strand == '+' else -1)
in_tfam = curr_cds_pos_set.issubset(
tfam_genpos)
cds_info.append(
(curr_gcoord, curr_gstop,
(curr_len - 3) / 3, in_tfam, annot_fidx,
annot_tid, curr_cds_pos_set))
all_annot_pos.update(curr_cds_pos_set)
if cds_info: # False means no annotated CDSs or none are multiples of 3 in length
cds_info = pd.DataFrame(cds_info, columns=['gcoord', 'gstop', 'AAlen', 'in_tfam', 'annot_fidx', 'annot_tid', 'pos']) \
.groupby(['gcoord', 'gstop', 'AAlen', 'in_tfam'], as_index=False) \
.apply(lambda x: x if len(x) == 1 else x[[not any(pos == x['pos'].iat[j] for j in xrange(i))
for (i, pos) in enumerate(x['pos'])]]) \
.set_index(['annot_fidx', 'annot_tid'])
# this operation organizes cds_info into a dataframe and effectively drops duplicates
# pandas drop_duplicates() is incompatible with sets so have to do it this manual way
# the combination of annot_fidx (the number of the file if more than one annotation file provided) and annot_tid should be a unique ID
tfam_orfs['annot_start'] = tfam_orfs['gcoord'].isin(
cds_info['gcoord'])
tfam_orfs['annot_stop'] = tfam_orfs['gstop'].isin(
cds_info['gstop'])
def _get_orf_pos(orfname, tid=None, tcoord=None, tstop=None):
"""Helper function that identifies the genomic coordinates of an ORF (in stranded order) and caches them by orfname"""
if orfname in orf_pos_dict:
return orf_pos_dict[orfname]
else:
if tid is None or tcoord is None or tstop is None:
(tid, tcoord, tstop) = tfam_orfs.loc[
tfam_orfs['orfname'] == orfname,
['tid', 'tcoord', 'tstop']].iloc[0]
res = tfam_genpos[np.flatnonzero(
tmask[tidx_lookup[tid], :])[tcoord:tstop]]
orf_pos_dict[orfname] = res
return res
# ANNOTATED and XISO
cds_info['found'] = False
possible_annot = tfam_orfs.drop_duplicates('orfname').merge(
cds_info[cds_info['in_tfam']].reset_index())
# merges on gcoord, gstop, and len - need to reset_index to preserve annot_fidx and annot_tid
for ((orfname, tid, tcoord, tstop),
cds_grp) in possible_annot.groupby(
['orfname', 'tid', 'tcoord', 'tstop']):
orf_pos = _get_orf_pos(orfname, tid, tcoord, tstop)
for (annot_fidx, annot_tid, cds_pos_set) in cds_grp[[
'annot_fidx', 'annot_tid', 'pos'
]].itertuples(False):
if cds_pos_set.issubset(orf_pos):
tfam_orfs.loc[tfam_orfs['orfname'] == orfname,
['orftype', 'untyped']] = [
'annotated', False
]
cds_info.loc[(annot_fidx, annot_tid),
'found'] = True
break
else:
tfam_orfs.loc[tfam_orfs['orfname'] == orfname,
['orftype', 'untyped']] = [
'Xiso', False
]
# matching start and stop but differing in between
if tfam_orfs['untyped'].any():
tfam_orfs.loc[tfam_orfs['orfname'].isin(
tfam_orfs[tfam_orfs['untyped']].
merge(cds_info[['gcoord', 'gstop']])['orfname']),
['orftype', 'untyped']] = ['Xiso', False]
# matching start and stop, but must differ somewhere, otherwise would have been identified as annotated (Xiso => "exact isoform")
# SISO
tfam_orfs.loc[tfam_orfs['annot_start']
& tfam_orfs['annot_stop'] & tfam_orfs['untyped'],
['orftype', 'untyped']] = ['Siso', False]
# start and stop each match at least one CDS, but not the same one (Siso => "spliced isoform")
# CISO
tfam_orfs.loc[tfam_orfs['annot_start'] & tfam_orfs['untyped'],
['orftype', 'untyped']] = ['Ciso', False]
# start is annotated, but stop is not - so must be on a new transcript (Ciso => "C-terminal isoform")
# TRUNCATION
if tfam_orfs['untyped'].any():
found_matched_stop = tfam_orfs[tfam_orfs['untyped']].merge(
tfam_orfs[tfam_orfs['orftype'] == 'annotated'],
on=['tid', 'tstop'],
suffixes=('', '_annot'))
tfam_orfs.loc[
tfam_orfs['orfname'].isin(found_matched_stop.loc[
found_matched_stop['tcoord'] >
found_matched_stop['tcoord_annot'], 'orfname']),
['orftype', 'untyped']] = ['truncation', False]
# on the same transcript with an annotated CDS, with matching stop codon, initiating downstream - must be a truncation
# still some missing truncations, if the original CDS was not on a transcript in the present transcriptome
if tfam_orfs['untyped'].any() and not cds_info['found'].all():
possible_truncs = tfam_orfs[tfam_orfs['untyped']].drop_duplicates('orfname') \
.merge(cds_info.loc[~cds_info['found'], ['gstop', 'pos', 'AAlen']], on='gstop', suffixes=('', '_annot'))
possible_truncs = possible_truncs[
possible_truncs['AAlen'] <
possible_truncs['AAlen_annot']]
for ((orfname, tid, tcoord, tstop, gcoord), cds_pos_sets) in \
possible_truncs.groupby(['orfname', 'tid', 'tcoord', 'tstop', 'gcoord'])['pos']:
orf_pos = _get_orf_pos(orfname, tid, tcoord, tstop)
if strand == '-':
if any(
cds_pos_set.issuperset(orf_pos) and all(
pos in orf_pos for pos in cds_pos_set
if pos <= gcoord)
for cds_pos_set in cds_pos_sets):
tfam_orfs.loc[tfam_orfs['orfname'] == orfname,
['orftype', 'untyped']] = [
'truncation', False
]
else:
if any(
cds_pos_set.issuperset(orf_pos) and all(
pos in orf_pos for pos in cds_pos_set
if pos >= gcoord)
for cds_pos_set in cds_pos_sets):
tfam_orfs.loc[tfam_orfs['orfname'] == orfname,
['orftype', 'untyped']] = [
'truncation', False
]
# matching stop codon, contained within, and all positions in the annotation past the orf start codon are included in the orf
# EXTENSION
if tfam_orfs['untyped'].any():
found_matched_stop = tfam_orfs[tfam_orfs['untyped']].merge(
tfam_orfs[tfam_orfs['orftype'] == 'annotated'],
on=['tid', 'tstop'],
suffixes=('', '_annot'))
assert (found_matched_stop['tcoord'] <
found_matched_stop['tcoord_annot']).all(
) # other possibilities should be done by now
tfam_orfs.loc[tfam_orfs['orfname'].
isin(found_matched_stop['orfname']),
['orftype', 'untyped']] = [
'extension', False
]
# on the same transcript with an annotated CDS, with matching stop codon, initiating upstream - must be an extension
# no possibility for an "unfound" extension - if the extension is in the transcriptome, the CDS it comes from must be as well
# (except for a few edge cases e.g. annotated CDS is a CUG initiator, but not considering CUG ORFs)
# NISO
tfam_orfs.loc[tfam_orfs['annot_stop'] & (tfam_orfs['untyped']),
['orftype', 'untyped']] = ['Niso', False]
# stop is annotated, but start is not, and it's not a truncation or extension - so must be an isoform (Niso => "N-terminal isoform")
# NCISO
if tfam_orfs['untyped'].any():
orf_codons = []
for (orfname, tid, tcoord, tstop) in \
tfam_orfs.loc[tfam_orfs['untyped'], ['orfname', 'tid', 'tcoord', 'tstop']].drop_duplicates('orfname').itertuples(False):
orf_codons.append(
pd.DataFrame(
_get_orf_pos(orfname, tid, tcoord,
tstop).reshape((-1, 3))))
orf_codons[-1]['orfname'] = orfname
orf_codons = pd.concat(orf_codons, ignore_index=True)
if strand == '-':
annot_codons = pd.DataFrame(
np.vstack([
np.reshape(sorted(cds_pos_set, reverse=True),
(-1, 3))
for cds_pos_set in cds_info['pos']
if len(cds_pos_set) % 3 == 0
])).drop_duplicates()
else:
annot_codons = pd.DataFrame(
np.vstack([
np.reshape(sorted(cds_pos_set, reverse=False),
(-1, 3))
for cds_pos_set in cds_info['pos']
if len(cds_pos_set) % 3 == 0
])).drop_duplicates()
tfam_orfs.loc[tfam_orfs['orfname'].isin(
orf_codons.merge(annot_codons)['orfname']),
['orftype', 'untyped']] = ['NCiso', False]
# ORFs that have at least one full codon overlapping (in-frame) with a CDS are isoforms (NCiso => "N- and C-terminal isoform")
# Note that these must already differ at N- and C- termini, otherwise they would already have been classified
# INTERNAL
if tfam_orfs['untyped'].any():
sametrans = tfam_orfs[tfam_orfs['untyped']].merge(
tfam_orfs[tfam_orfs['orftype'] == 'annotated'],
on='tid',
suffixes=('', '_annot'))
sametrans_internal = (
sametrans['tcoord'] > sametrans['tcoord_annot']) & (
sametrans['tstop'] < sametrans['tstop_annot'])
tfam_orfs.loc[tfam_orfs['orfname'].isin(sametrans.loc[
sametrans_internal, 'orfname']),
['orftype', 'untyped']] = [
'internal', False
]
# ORFs completely contained within a CDS on the same transcript, and not containing any full codon overlaps, must be internal
# Still could be other ORFs internal to a CDS on a transcript not in the current transcriptome - need to check manually
if tfam_orfs['untyped'].any() and not cds_info['found'].all():
for (orfname, gcoord, gstop) in \
tfam_orfs.loc[tfam_orfs['untyped'], ['orfname', 'gcoord', 'gstop']].drop_duplicates('orfname').itertuples(False):
orf_pos = _get_orf_pos(
orfname) # should be cached by now
if strand == '-':
if any(
cds_pos_set.issuperset(orf_pos) and all(
pos in orf_pos for pos in cds_pos_set
if gcoord >= pos > gstop)
for cds_pos_set in cds_info.loc[
(~cds_info['found'])
& (cds_info['gcoord'] > gcoord)
& (cds_info['gstop'] < gstop), 'pos']):
tfam_orfs.loc[tfam_orfs['orfname'] == orfname,
['orftype', 'untyped']] = [
'internal', False
]
else:
if any(
cds_pos_set.issuperset(orf_pos) and all(
pos in orf_pos for pos in cds_pos_set
if gcoord <= pos < gstop)
for cds_pos_set in cds_info.loc[
(~cds_info['found'])
& (cds_info['gcoord'] < gcoord)
& (cds_info['gstop'] > gstop), 'pos']):
tfam_orfs.loc[tfam_orfs['orfname'] == orfname,
['orftype', 'untyped']] = [
'internal', False
]
# contained within, and all positions in the annotation between the orf start and stop codons are included in the orf
# STOP_OVERLAP
if tfam_orfs['untyped'].any():
sametrans = tfam_orfs[tfam_orfs['untyped']].merge(
tfam_orfs[tfam_orfs['orftype'] == 'annotated'],
on='tid',
suffixes=('', '_annot'))
sametrans_stopover = (
sametrans['tcoord'] > sametrans['tcoord_annot']) & (
sametrans['tcoord'] < sametrans['tstop_annot'])
tfam_orfs.loc[tfam_orfs['orfname'].isin(sametrans.loc[
sametrans_stopover, 'orfname']),
['orftype', 'untyped']] = [
'stop_overlap', False
]
# starts within a CDS and not an internal - must be a stop_overlap
# do not need to check for unfounds - requiring that stop_overlap must be on same transcript as cds
# START_OVERLAP
if tfam_orfs['untyped'].any():
sametrans = tfam_orfs[tfam_orfs['untyped']].merge(
tfam_orfs[tfam_orfs['orftype'] == 'annotated'],
on='tid',
suffixes=('', '_annot'))
sametrans_startover = (
sametrans['tstop'] > sametrans['tcoord_annot']) & (
sametrans['tstop'] < sametrans['tstop_annot'])
tfam_orfs.loc[tfam_orfs['orfname'].isin(sametrans.loc[
sametrans_startover, 'orfname']),
['orftype', 'untyped']] = [
'start_overlap', False
]
# ends within a CDS and not an internal - must be a start_overlap
# do not need to check for unfounds - requiring that start_overlap must be on same transcript as cds
# LOOF
if tfam_orfs['untyped'].any():
sametrans = tfam_orfs[tfam_orfs['untyped']].merge(
tfam_orfs[tfam_orfs['orftype'] == 'annotated'],
on='tid',
suffixes=('', '_annot'))
sametrans_loof = (
sametrans['tcoord'] < sametrans['tcoord_annot']) & (
sametrans['tstop'] > sametrans['tstop_annot'])
tfam_orfs.loc[tfam_orfs['orfname'].isin(sametrans.loc[
sametrans_loof, 'orfname']),
['orftype', 'untyped']] = ['LOOF', False]
# starts upstream of a CDS and ends downstream of it - must be a LOOF ("long out-of-frame")
# don't need to check for unfounds because the CDS must be on the same transcript as the ORF if the ORF completely contains it
# UPSTREAM
if tfam_orfs['untyped'].any():
sametrans = tfam_orfs[tfam_orfs['untyped']].merge(
tfam_orfs[tfam_orfs['orftype'] == 'annotated'],
on='tid',
suffixes=('', '_annot'))
sametrans_upstream = (sametrans['tstop'] <=
sametrans['tcoord_annot'])
tfam_orfs.loc[tfam_orfs['orfname'].isin(sametrans.loc[
sametrans_upstream, 'orfname']),
['orftype', 'untyped']] = [
'upstream', False
]
# ends upstream of a CDS - must be an upstream (uORF)
# cannot check manually for unfounds because those are not on well-defined transcripts
# DOWNSTREAM
if tfam_orfs['untyped'].any():
sametrans = tfam_orfs[tfam_orfs['untyped']].merge(
tfam_orfs[tfam_orfs['orftype'] == 'annotated'],
on='tid',
suffixes=('', '_annot'))
sametrans_downstream = (sametrans['tstop_annot'] <=
sametrans['tcoord'])
tfam_orfs.loc[tfam_orfs['orfname'].isin(sametrans.loc[
sametrans_downstream, 'orfname']),
['orftype', 'untyped']] = [
'downstream', False
]
# starts downstream of a CDS - must be a downstream ORF
# cannot check manually for unfounds because those are not on well-defined transcripts
# NEW_ISO and GISO
for orfname in tfam_orfs.loc[tfam_orfs['untyped'],
'orfname'].drop_duplicates():
if all_annot_pos.isdisjoint(_get_orf_pos(orfname)):
tfam_orfs.loc[tfam_orfs['orfname'] == orfname,
['orftype', 'untyped']] = [
'new_iso', False
]
# no overlaps whatsoever with any annotated CDS, but in a tfam that has annotations: new_iso
else:
tfam_orfs.loc[tfam_orfs['orfname'] == orfname,
['orftype', 'untyped']] = [
'Giso', False
]
# overlaps out-of-frame with a CDS, and not on the same transcript with a CDS: Giso => "genomic isoform"
assert not tfam_orfs['untyped'].any()
return tfam_orfs.drop('untyped', axis=1)
else:
return None
