def t_key_whitespace(self):
"""Fix keys with problematic whitespace.
"""
tfile = os.path.join(self._test_dir, "spaces.gff3")
for i, line_info in enumerate(GFF.parse_simple(tfile)):
if i > 2:
assert line_info["quals"]["foo"] == ["bar"]
def t_simple_parsing_nesting(self):
"""Simple parsing for lines with nesting, using the simplified API.
"""
test_gff = os.path.join(self._test_dir, "transcripts.gff3")
num_lines = 0
for line_info in GFF.parse_simple(test_gff):
num_lines += 1
assert num_lines == 16, num_lines
def build_ensembl_transcript_index(self, ensembl_input_gtfs, ensembl_input_fastas, output_filename, protocol="file", protein_id_mapping_file=None):
"""Create the transcript index (using shove) for ensembl. Key is transcript ID.
Note: This method will hold the entire transcript index in RAM.
:param ensembl_input_gtfs: (list)
:param ensembl_input_fastas: (list) sequence data for transcripts corresponding to what is in the gtfs
:param output_filename:
:param protocol: shove protocol. Usually "file" or "sqlite"
"""
# Example code taken from http://biopython.org/wiki/GFF_Parsing
shove = Shove(protocol + "://" + output_filename, "memory://")
logging.getLogger(__name__).info("Transcript index being created: " + protocol + "://" + output_filename)
# Get the transcript ID to protein ID mapping
tx_to_protein_mapping = self._create_tx_id_to_protein_id_mapping(protein_id_mapping_file)
seq_dict = {}
for in_seq_file in ensembl_input_fastas:
in_seq_handle = open(in_seq_file)
seq_dict.update(self._create_seq_dict(in_seq_handle))
in_seq_handle.close()
logging.getLogger(__name__).info("Parsed fasta file: " + in_seq_file)
for file_ctr, in_file in enumerate(ensembl_input_gtfs):
in_handle = open(in_file)
seq_dict_keys = seq_dict.keys()
ctr = 0
for rec in GFF.parse_simple(in_file): #(in_handle, base_dict=seq_dict):
# transcript id seems to always be a list of length 1
if len(rec['quals']['transcript_id']) > 1:
logging.getLogger(__name__).warn("ensembl records had more than one transcript id: " + str(rec['quals']['transcript_id']))
self._convertGFFRecordToTranscript(rec, seq_dict, seq_dict_keys, tx_to_protein_mapping)
ctr += 1
if (ctr % 10000) == 0:
logging.getLogger(__name__).info("Added " + str(ctr) + " lines of gtf " + str(file_ctr+1) + " of " + str(len(ensembl_input_gtfs)) + " (" + in_file + ") into internal transcript index.")
in_handle.close()
logging.getLogger(__name__).info("Finished " + str(ctr) + " lines of gtf (" + in_file + ")")
logging.getLogger(__name__).info("Populating final db with internal transcript index.")
transcript_index_keys = self._transcript_index.keys()
for i,k in enumerate(transcript_index_keys):
# Populate the protein sequence
protein_sequence = self._determine_protein_seq(self._transcript_index[k])
self._transcript_index[k].set_protein_seq(protein_sequence)
shove[k] = self._transcript_index[k]
if i % 10000 == 0:
logging.getLogger(__name__).info("Saved %0.1f%% of transcript index to disk with protein sequence." % (float(i*100)/float(len(transcript_index_keys))))
logging.getLogger(__name__).info("Transcript index created " + str(len(shove.keys())) + " transcripts: " + protocol + "://" + output_filename)
shove.close()
def parse_tx_in_region(tx_file, region):
want = ["gene_id", "transcript_id", "FPKM"]
se_range = set(range(region["start"], region["end"]))
limit_info = {"gff_id": [region["space"]], "gff_type": ["transcript"]}
for rec in GFF.parse_simple(tx_file, limit_info=limit_info):
s, e = rec["location"]
if s in se_range or e in se_range:
out = {"chr": rec["rec_id"], "start": s, "end": e}
for n in want:
out[n] = rec["quals"][n][0]
yield out
def parse_tx_in_region(tx_file, region):
want = ["gene_id", "transcript_id", "FPKM"]
se_range = set(range(region["start"], region["end"]))
limit_info = {"gff_id": [region["space"]],
"gff_type": ["transcript"]}
for rec in GFF.parse_simple(tx_file, limit_info=limit_info):
s, e = rec["location"]
if s in se_range or e in se_range:
out = {"chr": rec["rec_id"], "start": s, "end": e}
for n in want:
out[n] = rec["quals"][n][0]
yield out
def build_ensembl_transcript_index(self,
ensembl_input_gtfs,
ensembl_input_fastas,
output_filename,
protocol="file",
protein_id_mapping_file=None):
"""Create the transcript index (using shove) for ensembl. Key is transcript ID.
Note: This method will hold the entire transcript index in RAM.
:param ensembl_input_gtfs: (list)
:param ensembl_input_fastas: (list) sequence data for transcripts corresponding to what is in the gtfs
:param output_filename:
:param protocol: shove protocol. Usually "file" or "sqlite"
"""
# Example code taken from http://biopython.org/wiki/GFF_Parsing
shove = Shove(protocol + "://" + output_filename, "memory://")
logging.getLogger(__name__).info("Transcript index being created: " +
protocol + "://" + output_filename)
# Get the transcript ID to protein ID mapping
tx_to_protein_mapping = self._create_tx_id_to_protein_id_mapping(
protein_id_mapping_file)
seq_dict = {}
for in_seq_file in ensembl_input_fastas:
in_seq_handle = open(in_seq_file)
seq_dict.update(self._create_seq_dict(in_seq_handle))
in_seq_handle.close()
logging.getLogger(__name__).info("Parsed fasta file: " +
in_seq_file)
for file_ctr, in_file in enumerate(ensembl_input_gtfs):
in_handle = open(in_file)
seq_dict_keys = seq_dict.keys()
ctr = 0
for rec in GFF.parse_simple(
in_file): #(in_handle, base_dict=seq_dict):
# transcript id seems to always be a list of length 1
if len(rec['quals']['transcript_id']) > 1:
logging.getLogger(__name__).warn(
"ensembl records had more than one transcript id: " +
str(rec['quals']['transcript_id']))
self._convertGFFRecordToTranscript(rec, seq_dict,
seq_dict_keys,
tx_to_protein_mapping)
ctr += 1
if (ctr % 10000) == 0:
logging.getLogger(__name__).info(
"Added " + str(ctr) + " lines of gtf " +
str(file_ctr + 1) + " of " +
str(len(ensembl_input_gtfs)) + " (" + in_file +
") into internal transcript index.")
in_handle.close()
logging.getLogger(__name__).info("Finished " + str(ctr) +
" lines of gtf (" + in_file + ")")
logging.getLogger(__name__).info(
"Populating final db with internal transcript index.")
transcript_index_keys = self._transcript_index.keys()
for i, k in enumerate(transcript_index_keys):
# Populate the protein sequence
protein_sequence = self._determine_protein_seq(
self._transcript_index[k])
self._transcript_index[k].set_protein_seq(protein_sequence)
shove[k] = self._transcript_index[k]
if i % 10000 == 0:
logging.getLogger(__name__).info(
"Saved %0.1f%% of transcript index to disk with protein sequence."
% (float(i * 100) / float(len(transcript_index_keys))))
logging.getLogger(__name__).info("Transcript index created " +
str(len(shove.keys())) +
" transcripts: " + protocol + "://" +
output_filename)
shove.close()
