def get_nominal_annotations(self):
"""Get nominal essentials and non-essentails in cerevisiae.
Returns
-------
(set, set)
A pair sets, denoting the essential and non-essential genes, using
their standard names.
"""
viable_filepath = Shared.get_dependency("cerevisiae", "cerevisiae_viable_annotations.txt")
inviable_filepath = Shared.get_dependency("cerevisiae", "cerevisiae_inviable_annotations.txt")
viable_table = pd.read_csv(viable_filepath, skiprows=8, delimiter="\t")
inviable_table = pd.read_csv(inviable_filepath, skiprows=8, delimiter="\t")
annotated_as_viable = set(self.feature_db.get_feature_by_name(f) for f in set(viable_table[viable_table["Mutant Information"] == "null"]["Gene"])) - set([None])
annotated_as_inviable = set(self.feature_db.get_feature_by_name(f) for f in set(inviable_table[inviable_table["Mutant Information"] == "null"]["Gene"])) - set([None])
# TODO: the dubious genes shouldn't be filtered here.
consensus_viable_orfs = [f for f in annotated_as_viable if f.is_orf and f.feature_qualifier != "Dubious"]
consensus_inviable_orfs = [f for f in annotated_as_inviable if f.is_orf and f.feature_qualifier != "Dubious"]
return (set(f.standard_name for f in consensus_inviable_orfs),
set(f.standard_name for f in consensus_viable_orfs))
def get_calb_orths_in_sp():
pom_db = GenomicFeatures.default_pom_db()
ortholog_table = pd.read_csv(Shared.get_dependency("albicans", "C_albicans_SC5314_S_pombe_orthologs.txt"),
skiprows=8,
delimiter='\t',
header=None,
usecols=['albicans standard name', 'pombe standard name'],
names=['albicans standard name', 'albicans common name', 'albicans alb_db id',
'pombe standard name', 'pombe common name', 'pombe alb_db id'])
# TODO: we probably don't want to use the hit table, though the InParanoid
# table is very stringent.
best_hit_table = pd.read_csv(Shared.get_dependency("albicans", "C_albicans_SC5314_S_pombe_best_hits.txt"),
skiprows=8,
delimiter='\t',
header=None,
usecols=['albicans standard name', 'pombe standard name'],
names=['albicans standard name', 'albicans common name', 'albicans alb_db id',
'pombe standard name', 'pombe common name', 'pombe alb_db id'])
joined_table = pd.concat([ortholog_table, best_hit_table])
result = {}
for alb_feature in GenomicFeatures.default_alb_db().get_all_features():
ortholog_row = joined_table[joined_table["albicans standard name"] == alb_feature.standard_name]
if ortholog_row.empty:
continue
pom_feature = pom_db.get_feature_by_name(ortholog_row["pombe standard name"].iloc[0])
if pom_feature:
result[alb_feature.standard_name] = pom_feature.name
return result
def _get_spom_essentials(self):
viability_table = pd.read_csv(Shared.get_dependency("pombe/FYPOviability.tsv"),
header=None,
delimiter='\t',
names=["pombe standard name", "essentiality"])
return set(r[0] for _ix, r in viability_table.iterrows() if r[1] == "inviable"), \
set(r[0] for _ix, r in viability_table.iterrows() if r[1] == "viable")
def get_calb_orths_in_sp():
pom_db = GenomicFeatures.default_pom_db()
ortholog_table = pd.read_csv(
Shared.get_dependency("albicans",
"C_albicans_SC5314_S_pombe_orthologs.txt"),
skiprows=8,
delimiter='\t',
header=None,
usecols=['albicans standard name', 'pombe standard name'],
names=[
'albicans standard name', 'albicans common name',
'albicans alb_db id', 'pombe standard name', 'pombe common name',
'pombe alb_db id'
])
# TODO: we probably don't want to use the hit table, though the InParanoid
# table is very stringent.
best_hit_table = pd.read_csv(
Shared.get_dependency("albicans",
"C_albicans_SC5314_S_pombe_best_hits.txt"),
skiprows=8,
delimiter='\t',
header=None,
usecols=['albicans standard name', 'pombe standard name'],
names=[
'albicans standard name', 'albicans common name',
'albicans alb_db id', 'pombe standard name', 'pombe common name',
'pombe alb_db id'
])
joined_table = pd.concat([ortholog_table, best_hit_table])
result = {}
for alb_feature in GenomicFeatures.default_alb_db().get_all_features():
ortholog_row = joined_table[joined_table["albicans standard name"] ==
alb_feature.standard_name]
if ortholog_row.empty:
continue
pom_feature = pom_db.get_feature_by_name(
ortholog_row["pombe standard name"].iloc[0])
if pom_feature:
result[alb_feature.standard_name] = pom_feature.name
return result
def analyze_deletions(bam_file, threshold=50):
bam_reader = pysam.AlignmentFile(bam_file, "rb")
fasta_file = Shared.get_dependency(
os.path.join(
"albicans", "reference genome",
"C_albicans_SC5314_version_A22-s07-m01-r08_chromosomes_HapA.fasta")
)
chrom_names = []
chrom_lens = {}
for record in SeqIO.parse(fasta_file, "fasta"):
chrom_names.append(record.id)
chrom_lens[record.id] = RangeSet([(1, len(record))])
seen = {chrom: [] for chrom in chrom_names}
for read in bam_reader.fetch():
chrom_name = bam_reader.getrname(read.reference_id)
if "chrM" in chrom_name:
continue
seen[chrom_name].append(
(read.reference_start + 1, read.reference_end - 1 + 1))
unseen = {
chrom: chrom_lens[chrom] - RangeSet(seen[chrom])
for chrom in chrom_names
}
write_ranges(
unseen,
"/Users/bermanlab/dev/transposon-pipeline/dependencies/albicans/deleted_regions.csv"
)
ranges = {
chrom: [r for r in unseen[chrom] if r[1] - r[0] >= threshold]
for chrom in chrom_names
}
pprint(ranges)
print "Total unseen:", sum(r.coverage for r in unseen.values())
print "Total filtered unseen:", sum(
sum(r[1] - r[0] + 1 for r in rs) for rs in ranges.values())
for chrom in chrom_names:
print chrom
print "Total subranges:", len(unseen[chrom])
print "Total length:", unseen[chrom].coverage
print "Ignored long subranges:", len(ranges[chrom])
print "Total length:", sum(r[1] - r[0] + 1 for r in ranges[chrom])
print "\n"
import GenomicFeatures
alb_db = GenomicFeatures.default_alb_db()
for r in unseen[chrom]:
fs = alb_db.get_features_at_range(chrom, r)
if fs:
print chrom, r, ", ".join(f.standard_name for f in fs)
print "\n"
def _get_spom_essentials(self):
viability_table = pd.read_csv(
Shared.get_dependency("pombe/FYPOviability.tsv"),
header=None,
delimiter='\t',
names=["pombe standard name", "essentiality"])
return set(r[0] for _ix, r in viability_table.iterrows() if r[1] == "inviable"), \
set(r[0] for _ix, r in viability_table.iterrows() if r[1] == "viable")
def _get_homologous_regions(self):
ranges = self._read_range_data(
Shared.get_dependency(
os.path.join("albicans", "homologous_regions.csv")))
return {
chrom:
RangeSet(r for r in range_set
if r[1] - r[0] + 1 >= self._ignore_region_threshold)
for chrom, range_set in ranges.iteritems()
}
def get_nominal_annotations(self):
"""Get nominal essentials and non-essentails in cerevisiae.
Returns
-------
(set, set)
A pair sets, denoting the essential and non-essential genes, using
their standard names.
"""
viable_filepath = Shared.get_dependency(
"cerevisiae", "cerevisiae_viable_annotations.txt")
inviable_filepath = Shared.get_dependency(
"cerevisiae", "cerevisiae_inviable_annotations.txt")
viable_table = pd.read_csv(viable_filepath, skiprows=8, delimiter="\t")
inviable_table = pd.read_csv(inviable_filepath,
skiprows=8,
delimiter="\t")
annotated_as_viable = set(
self.feature_db.get_feature_by_name(f)
for f in set(viable_table[viable_table["Mutant Information"] ==
"null"]["Gene"])) - set([None])
annotated_as_inviable = set(
self.feature_db.get_feature_by_name(f)
for f in set(inviable_table[inviable_table["Mutant Information"] ==
"null"]["Gene"])) - set([None])
# TODO: the dubious genes shouldn't be filtered here.
consensus_viable_orfs = [
f for f in annotated_as_viable
if f.is_orf and f.feature_qualifier != "Dubious"
]
consensus_inviable_orfs = [
f for f in annotated_as_inviable
if f.is_orf and f.feature_qualifier != "Dubious"
]
return (set(f.standard_name for f in consensus_inviable_orfs),
set(f.standard_name for f in consensus_viable_orfs))
def analyze_deletions(bam_file, threshold=50):
bam_reader = pysam.AlignmentFile(bam_file, "rb")
fasta_file = Shared.get_dependency(os.path.join("albicans", "reference genome", "C_albicans_SC5314_version_A22-s07-m01-r08_chromosomes_HapA.fasta"))
chrom_names = []
chrom_lens = {}
for record in SeqIO.parse(fasta_file, "fasta"):
chrom_names.append(record.id)
chrom_lens[record.id] = RangeSet([(1, len(record))])
seen = {chrom: [] for chrom in chrom_names}
for read in bam_reader.fetch():
chrom_name = bam_reader.getrname(read.reference_id)
if "chrM" in chrom_name:
continue
seen[chrom_name].append((read.reference_start+1, read.reference_end-1+1))
unseen = {chrom: chrom_lens[chrom] - RangeSet(seen[chrom]) for chrom in chrom_names}
write_ranges(unseen, "/Users/bermanlab/dev/transposon-pipeline/dependencies/albicans/deleted_regions.csv")
ranges = {chrom: [r for r in unseen[chrom] if r[1] - r[0] >= threshold] for chrom in chrom_names}
pprint(ranges)
print "Total unseen:", sum(r.coverage for r in unseen.values())
print "Total filtered unseen:", sum(sum(r[1]-r[0]+1 for r in rs) for rs in ranges.values())
for chrom in chrom_names:
print chrom
print "Total subranges:", len(unseen[chrom])
print "Total length:", unseen[chrom].coverage
print "Ignored long subranges:", len(ranges[chrom])
print "Total length:", sum(r[1]-r[0]+1 for r in ranges[chrom])
print "\n"
import GenomicFeatures
alb_db = GenomicFeatures.default_alb_db()
for r in unseen[chrom]:
fs = alb_db.get_features_at_range(chrom, r)
if fs:
print chrom, r, ", ".join(f.standard_name for f in fs)
print "\n"
(Adaptor cleaning works the same as with R1.)
-d --delete-originals Delete input FASTQ files.
-k --keep-clean-fqs Keep the cleaned FASTQ files.
-p --primer-check Check primer specificity if percent transposon in reads is low.
-h --help Show this help message and exit
'''
TnPrimerAndTail = 'GTATTTTACCGACCGTTACCGACCGTTTTCATCCCTA'
TnRev = 'TAGGGATGAAAACGGTCGGTAACGGTCGGTAAAATAC'
PrimerOnly = 'GTATTTTACCGACCGTTACCGACC'
PrimerRev = 'GGTCGGTAACGGTCGGTAAAATAC'
AdapterSeq = 'AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC'
# NB: bowtie2 requires spaces to be escapes with a backslash for the -x parameter.
CInd = Shared.get_dependency(
"albicans", "reference genome",
"C_albicans_SC5314_version_A22-s07-m01-r08_chromosomes_HapA").replace(
' ', '\ ')
CORES = 4 # Cores on the machine = how many threads should the external tools utilize
def GetCmdPath(Program):
"""Gets the path to a desired program file on given computer.
Parameters
----------
Program : string
Name of program wish to have path for.
Returns
-------
CmdPath : string
import Shared
usage = '''CreateHitFile.py
-i --in-dir [str] Input directory with .bam files to parse. Defaults to current directory if left unspecified.
-o --out-dir [str] Output directory to which the hit file will be writen. Defaults to current directory if left unspecified.
-q --min-mapq [int] Map Quality - hits to parse from the bam file (default is 20)
-m --merge-dist [int] Hits to merge with at most x nt distance between two hits. Default is 2
Example: Hits in positions 1 and 3 (3-1=2) will be united into a single hit
-h --help Show this help message and exit
'''
# TODO: move to config file.
ChrFile = Shared.get_dependency('albicans', 'reference genome', 'C_albicans_SC5314_version_A22-s07-m01-r08_chromosomes_HapA.fasta')
FeatureFName = Shared.get_dependency('albicans', 'reference genome', 'C_albicans_SC5314_version_A22-s07-m01-r08_chromosomal_feature.tab')
ChrFeatCols = ['FeatureName', 'GeneName','Aliases','FeatureType','Chromosome','StartCoord','StopCoord','Strand','PrimaryCGDID','SecondaryCGDID',\
'Description','DateCreated','SeqCoordVerDate','Blank1','Blank2','GeneNameReserDate','ReservedIsstandardName','SC_ortholog']
def FindHitsPerSample(SamAlign, ChrFeatMap, Sep0N = 2,MapQ=10):
"""Goes through Sam file, checks for high confidence alignment, unites unique positions if they can be aligned with adjunct positions.
Parameters
----------
SamAlign : x
ChrFeatMap : x
SepON : integer
MapQ : integer
Setting for map quality. Default of 10 is equal to 1% chance of occurring in another position.
def _get_deleted_regions(self):
ranges = self._read_range_data(Shared.get_dependency(os.path.join("albicans", "deleted_regions.csv")))
return {chrom: RangeSet(r for r in range_set if r[1] - r[0] + 1 >= self._ignore_region_threshold) for chrom, range_set in ranges.iteritems()}
import pysam
import itertools
import Shared
usage = '''CreateHitFile.py
-i --in-dir [str] Input directory with .bam files to parse. Defaults to current directory if left unspecified.
-o --out-dir [str] Output directory to which the hit file will be writen. Defaults to current directory if left unspecified.
-q --min-mapq [int] Map Quality - hits to parse from the bam file (default is 20)
-m --merge-dist [int] Hits to merge with at most x nt distance between two hits. Default is 2
Example: Hits in positions 1 and 3 (3-1=2) will be united into a single hit
-h --help Show this help message and exit
'''
# TODO: move to config file.
ChrFile = Shared.get_dependency(
'albicans', 'reference genome',
'C_albicans_SC5314_version_A22-s07-m01-r08_chromosomes_HapA.fasta')
FeatureFName = Shared.get_dependency(
'albicans', 'reference genome',
'C_albicans_SC5314_version_A22-s07-m01-r08_chromosomal_feature.tab')
ChrFeatCols = ['FeatureName', 'GeneName','Aliases','FeatureType','Chromosome','StartCoord','StopCoord','Strand','PrimaryCGDID','SecondaryCGDID',\
'Description','DateCreated','SeqCoordVerDate','Blank1','Blank2','GeneNameReserDate','ReservedIsstandardName','SC_ortholog']
def FindHitsPerSample(SamAlign, ChrFeatMap, Sep0N=2, MapQ=10):
"""Goes through Sam file, checks for high confidence alignment, unites unique positions if they can be aligned with adjunct positions.
Parameters
----------
SamAlign : x
def _get_genes_with_paralogs(self):
return Organism._get_genes_with_paralogs(self, Shared.get_dependency(os.path.join("pombe", "hasParalogs_sp.txt")))
def _get_genes_with_paralogs(self):
return Organism._get_genes_with_paralogs(
self,
Shared.get_dependency(
os.path.join("albicans", "hasParalogs_ca.txt")))
def _get_homologous_regions(self):
ranges = self._read_range_data(Shared.get_dependency(os.path.join("cerevisiae", "homologous_regions.csv")))
return {chrom: RangeSet(r for r in range_set if r[1] - r[0] + 1 >= self._ignore_region_threshold) for chrom, range_set in ranges.iteritems()}
def _get_genes_with_paralogs(self):
return Organism._get_genes_with_paralogs(self, Shared.get_dependency(os.path.join("albicans", "hasParalogs_ca.txt")))
-r --reverse-strand Search with reverse complement sequence for R2 files.
(Adaptor cleaning works the same as with R1.)
-d --delete-originals Delete input FASTQ files.
-k --keep-clean-fqs Keep the cleaned FASTQ files.
-p --primer-check Check primer specificity if percent transposon in reads is low.
-h --help Show this help message and exit
'''
TnPrimerAndTail = 'GTATTTTACCGACCGTTACCGACCGTTTTCATCCCTA'
TnRev = 'TAGGGATGAAAACGGTCGGTAACGGTCGGTAAAATAC'
PrimerOnly = 'GTATTTTACCGACCGTTACCGACC'
PrimerRev = 'GGTCGGTAACGGTCGGTAAAATAC'
AdapterSeq = 'AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC'
# NB: bowtie2 requires spaces to be escapes with a backslash for the -x parameter.
CInd = Shared.get_dependency("albicans", "reference genome", "C_albicans_SC5314_version_A22-s07-m01-r08_chromosomes_HapA").replace(' ', '\ ')
CORES = 4 # Cores on the machine = how many threads should the external tools utilize
def GetCmdPath(Program):
"""Gets the path to a desired program file on given computer.
Parameters
----------
Program : string
Name of program wish to have path for.
Returns
-------
CmdPath : string
Path for calling program as a command in the POSIX terminal.
"""
def _get_genes_with_paralogs(self):
return Organism._get_genes_with_paralogs(
self,
Shared.get_dependency(os.path.join("pombe", "hasParalogs_sp.txt")))