class _BamReaderBase(ReaderBase):
"""
The BamReader class provides a high-level interface to PacBio BAM
files. If a PacBio BAM index (bam.pbi file) is present and the
user instantiates the BamReader using the reference FASTA as the
second argument, the BamReader will provide an interface
compatible with CmpH5Reader.
"""
def _loadReferenceInfo(self):
refRecords = self.peer.header["SQ"]
refNames = [r["SN"] for r in refRecords]
refLengths = [r["LN"] for r in refRecords]
refMD5s = [r["M5"] for r in refRecords]
refIds = map(self.peer.get_tid, refNames)
nRefs = len(refRecords)
if nRefs > 0:
self._referenceInfoTable = np.rec.fromrecords(zip(
refIds,
refIds,
refNames,
refNames,
refLengths,
refMD5s,
np.zeros(nRefs, dtype=np.uint32),
np.zeros(nRefs, dtype=np.uint32)),
dtype=[('ID', '<i8'), ('RefInfoID', '<i8'),
('Name', 'O'), ('FullName', 'O'),
('Length', '<i8'), ('MD5', 'O'),
('StartRow', '<u4'), ('EndRow', '<u4')])
self._referenceDict = {}
self._referenceDict.update(zip(refIds, self._referenceInfoTable))
self._referenceDict.update(zip(refNames, self._referenceInfoTable))
else:
self._referenceInfoTable = None
self._referenceDict = None
def _loadReadGroupInfo(self):
rgs = self.peer.header["RG"]
readGroupTable_ = []
self._featureNameMappings = {} # RGID -> ("abstract feature name" -> actual feature name)
for rg in rgs:
rgID = rgAsInt(rg["ID"])
rgName = rg["PU"]
ds = dict([pair.split("=") for pair in rg["DS"].split(";") if pair != ""])
# spec: we only consider first two components of basecaller version
# in "chem" lookup
basecallerVersion = ".".join(ds["BASECALLERVERSION"].split(".")[0:2])
triple = ds["BINDINGKIT"], ds["SEQUENCINGKIT"], basecallerVersion
rgChem = decodeTriple(*triple)
rgReadType = ds["READTYPE"]
rgFrameRate = ds["FRAMERATEHZ"]
readGroupTable_.append((rgID, rgName, rgReadType, rgChem, rgFrameRate))
# Look for the features manifest entries within the DS tag,
# and build an "indirection layer", i.e. to get from
# "Ipd" to "Ipd:Frames"
# (This is a bit messy. Can we separate the manifest from
# the rest of the DS content?)
featureNameMapping = { key.split(":")[0] : key
for key in ds.keys()
if key in PULSE_FEATURE_TAGS }
self._featureNameMappings[rgID] = featureNameMapping
self._readGroupTable = np.rec.fromrecords(
readGroupTable_,
dtype=[("ID" , np.int32),
("MovieName" , "O"),
("ReadType" , "O"),
("SequencingChemistry", "O"),
("FrameRate", float)])
assert len(set(self._readGroupTable.ID)) == len(self._readGroupTable), \
"First 8 chars of read group IDs must be unique!"
self._readGroupDict = { rg.ID : rg
for rg in self._readGroupTable }
# The pulse features "available" to clients of this file are the intersection
# of pulse features available from each read group.
self._pulseFeaturesAvailable = set.intersection(
*[set(mapping.keys()) for mapping in self._featureNameMappings.values()])
def _loadProgramInfo(self):
pgRecords = [ (pg["ID"], pg.get("VN", None), pg.get("CL", None))
for pg in self.peer.header.get("PG", []) ]
if len(pgRecords) > 0:
self._programTable = np.rec.fromrecords(
pgRecords,
dtype=[("ID" , "O"),
("Version", "O"),
("CommandLine", "O")])
else:
self._programTable = None
def _loadReferenceFasta(self, referenceFastaFname):
ft = FastaTable(referenceFastaFname)
# Verify that this FASTA is in agreement with the BAM's
# reference table---BAM should be a subset.
fastaIdsAndLens = set((c.id, len(c)) for c in ft)
bamIdsAndLens = set((c.Name, c.Length) for c in self.referenceInfoTable)
if not bamIdsAndLens.issubset(fastaIdsAndLens):
raise ReferenceMismatch, "FASTA file must contain superset of reference contigs in BAM"
self.referenceFasta = ft
def _checkFileCompatibility(self):
# Verify that this is a "pacbio" BAM file of version at least
# 3.0.1
try:
checkedVersion = self.version
if "b" in checkedVersion:
raise Exception()
else:
major, minor, patch = checkedVersion.split('.')
assert major >= 3
assert minor >= 0
assert patch >= 1
except:
raise IncompatibleFile(
"This BAM file is incompatible with this API " +
"(only PacBio BAM files version >= 3.0.1 are supported)")
def __init__(self, fname, referenceFastaFname=None):
self.filename = fname = abspath(expanduser(fname))
self.peer = AlignmentFile(fname, "rb", check_sq=False)
self._checkFileCompatibility()
self._loadReferenceInfo()
self._loadReadGroupInfo()
self._loadProgramInfo()
self.referenceFasta = None
if referenceFastaFname is not None:
if self.isUnmapped:
raise ValueError, "Unmapped BAM file--reference FASTA should not be given as argument to BamReader"
self._loadReferenceFasta(referenceFastaFname)
@property
def isIndexLoaded(self):
return self.index is not None
@property
def isReferenceLoaded(self):
return self.referenceFasta is not None
@property
def isUnmapped(self):
return not(self.isMapped)
@property
def isMapped(self):
return len(self.peer.header["SQ"]) > 0
@property
def alignmentIndex(self):
raise UnavailableFeature("BAM has no alignment index")
@property
def movieNames(self):
return set([mi.MovieName for mi in self.readGroupTable])
@property
def readGroupTable(self):
return self._readGroupTable
def readGroupInfo(self, readGroupId):
return self._readGroupDict[readGroupId]
@property
def sequencingChemistry(self):
"""
List of the sequencing chemistries by movie. Order is
unspecified.
"""
return list(self.readGroupTable.SequencingChemistry)
@property
def referenceInfoTable(self):
return self._referenceInfoTable
#TODO: standard? how about subread instead? why capitalize ccs?
# can we standardize this? is cDNA an additional possibility
@property
def readType(self):
"""
Either "standard", "CCS", "mixed", or "unknown", to represent the
type of PacBio reads aligned in this BAM file.
"""
readTypes = self.readGroupTable.ReadType
if all(readTypes == "SUBREAD"):
return "standard"
elif all(readTypes == "CCS"):
return "CCS"
elif all((readTypes == "CCS") | (readTypes == "SUBREAD")):
return "mixed"
else:
return "unknown"
@property
def version(self):
return self.peer.header["HD"]["pb"]
def versionAtLeast(self, minimalVersion):
raise Unimplemented()
def softwareVersion(self, programName):
raise Unimplemented()
@property
def isSorted(self):
return self.peer.header["HD"]["SO"] == "coordinate"
@property
def isBarcoded(self):
raise Unimplemented()
@property
def isEmpty(self):
return (len(self) == 0)
def referenceInfo(self, key):
return self._referenceDict[key]
def atOffset(self, offset):
self.peer.seek(offset)
return BamAlignment(self, next(self.peer))
def hasPulseFeature(self, featureName):
return featureName in self._pulseFeaturesAvailable
def pulseFeaturesAvailable(self):
return self._pulseFeaturesAvailable
@property
def barcode(self):
raise Unimplemented()
@property
def barcodeName(self):
raise Unimplemented()
@property
def barcodes(self):
raise Unimplemented()
@requiresBai
def __len__(self):
return self.peer.mapped + self.peer.unmapped
def close(self):
if hasattr(self, "file") and self.file is not None:
self.file.close()
self.file = None
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.close()
class _BamReaderBase(ReaderBase):
"""
The BamReader class provides a high-level interface to PacBio BAM
files. If a PacBio BAM index (bam.pbi file) is present and the
user instantiates the BamReader using the reference FASTA as the
second argument, the BamReader will provide an interface
compatible with CmpH5Reader.
"""
def _loadReferenceInfo(self):
refRecords = self.peer.header["SQ"]
refNames = [r["SN"] for r in refRecords]
refLengths = [r["LN"] for r in refRecords]
refMD5s = [r["M5"] for r in refRecords]
refIds = map(self.peer.get_tid, refNames)
nRefs = len(refRecords)
if nRefs > 0:
self._referenceInfoTable = np.rec.fromrecords(
zip(refIds, refIds, refNames, refNames, refLengths, refMD5s,
np.zeros(nRefs, dtype=np.uint32),
np.zeros(nRefs, dtype=np.uint32)),
dtype=[('ID', '<i8'), ('RefInfoID', '<i8'), ('Name', 'O'),
('FullName', 'O'), ('Length', '<i8'), ('MD5', 'O'),
('StartRow', '<u4'), ('EndRow', '<u4')])
self._referenceDict = {}
self._referenceDict.update(zip(refIds, self._referenceInfoTable))
self._referenceDict.update(zip(refNames, self._referenceInfoTable))
else:
self._referenceInfoTable = None
self._referenceDict = None
def _loadReadGroupInfo(self):
rgs = self.peer.header["RG"]
readGroupTable_ = []
# RGID -> ("abstract feature name" -> actual feature name)
self._baseFeatureNameMappings = {}
self._pulseFeatureNameMappings = {}
for rg in rgs:
rgID = rgAsInt(rg["ID"])
rgName = rg["PU"]
ds = dict([
pair.split("=") for pair in rg["DS"].split(";") if pair != ""
])
# spec: we only consider first two components of basecaller version
# in "chem" lookup
basecallerVersion = ".".join(
ds["BASECALLERVERSION"].split(".")[0:2])
triple = ds["BINDINGKIT"], ds["SEQUENCINGKIT"], basecallerVersion
rgChem = decodeTriple(*triple)
rgReadType = ds["READTYPE"]
rgFrameRate = ds["FRAMERATEHZ"]
# Look for the features manifest entries within the DS tag,
# and build an "indirection layer", i.e. to get from
# "Ipd" to "Ipd:Frames"
# (This is a bit messy. Can we separate the manifest from
# the rest of the DS content?)
baseFeatureNameMapping = {
key.split(":")[0]: key
for key in ds.keys() if key in BASE_FEATURE_TAGS
}
pulseFeatureNameMapping = {
key.split(":")[0]: key
for key in ds.keys() if key in PULSE_FEATURE_TAGS
}
self._baseFeatureNameMappings[rgID] = baseFeatureNameMapping
self._pulseFeatureNameMappings[rgID] = pulseFeatureNameMapping
readGroupTable_.append(
(rgID, rgName, rgReadType, rgChem, rgFrameRate,
frozenset(baseFeatureNameMapping.iterkeys())))
self._readGroupTable = np.rec.fromrecords(readGroupTable_,
dtype=[
("ID", np.int32),
("MovieName", "O"),
("ReadType", "O"),
("SequencingChemistry",
"O"),
("FrameRate", float),
("BaseFeatures", "O")
])
assert len(set(self._readGroupTable.ID)) == len(self._readGroupTable), \
"First 8 chars of read group IDs must be unique!"
self._readGroupDict = {rg.ID: rg for rg in self._readGroupTable}
# The base/pulse features "available" to clients of this file are the intersection
# of features available from each read group.
self._baseFeaturesAvailable = set.intersection(*[
set(mapping.keys())
for mapping in self._baseFeatureNameMappings.values()
])
self._pulseFeaturesAvailable = set.intersection(*[
set(mapping.keys())
for mapping in self._pulseFeatureNameMappings.values()
])
def _loadProgramInfo(self):
pgRecords = [(pg["ID"], pg.get("VN", None), pg.get("CL", None))
for pg in self.peer.header.get("PG", [])]
if len(pgRecords) > 0:
self._programTable = np.rec.fromrecords(pgRecords,
dtype=[("ID", "O"),
("Version", "O"),
("CommandLine", "O")
])
else:
self._programTable = None
def _loadReferenceFasta(self, referenceFastaFname):
ft = FastaTable(referenceFastaFname)
# Verify that this FASTA is in agreement with the BAM's
# reference table---BAM should be a subset.
fastaIdsAndLens = set((c.id, len(c)) for c in ft)
bamIdsAndLens = set(
(c.Name, c.Length) for c in self.referenceInfoTable)
if not bamIdsAndLens.issubset(fastaIdsAndLens):
raise ReferenceMismatch, "FASTA file must contain superset of reference contigs in BAM"
self.referenceFasta = ft
def _checkFileCompatibility(self):
# Verify that this is a "pacbio" BAM file of version at least
# 3.0.1
badVersionException = IncompatibleFile(
"This BAM file is incompatible with this API " +
"(only PacBio BAM files version >= 3.0.1 are supported)")
checkedVersion = self.version
if "b" in checkedVersion:
raise badVersionException
else:
major, minor, patch = checkedVersion.split('.')
if not (major, minor, patch) >= (3, 0, 1):
raise badVersionException
def __init__(self, fname, referenceFastaFname=None):
self.filename = fname = abspath(expanduser(fname))
self.peer = AlignmentFile(fname, "rb", check_sq=False)
self._checkFileCompatibility()
self._loadReferenceInfo()
self._loadReadGroupInfo()
self._loadProgramInfo()
self.referenceFasta = None
if referenceFastaFname is not None:
if self.isUnmapped:
raise ValueError, "Unmapped BAM file--reference FASTA should not be given as argument to BamReader"
self._loadReferenceFasta(referenceFastaFname)
@property
def isIndexLoaded(self):
return self.index is not None
@property
def isReferenceLoaded(self):
return self.referenceFasta is not None
@property
def isUnmapped(self):
return not (self.isMapped)
@property
def isMapped(self):
return len(self.peer.header["SQ"]) > 0
@property
def alignmentIndex(self):
raise UnavailableFeature("BAM has no alignment index")
@property
def movieNames(self):
return set([mi.MovieName for mi in self.readGroupTable])
@property
def readGroupTable(self):
return self._readGroupTable
def readGroupInfo(self, readGroupId):
return self._readGroupDict[readGroupId]
@property
def sequencingChemistry(self):
"""
List of the sequencing chemistries by movie. Order is
unspecified.
"""
return list(self.readGroupTable.SequencingChemistry)
@property
def referenceInfoTable(self):
return self._referenceInfoTable
#TODO: standard? how about subread instead? why capitalize ccs?
# can we standardize this? is cDNA an additional possibility
@property
def readType(self):
"""
Either "standard", "CCS", "mixed", or "unknown", to represent the
type of PacBio reads aligned in this BAM file.
"""
readTypes = self.readGroupTable.ReadType
if all(readTypes == "SUBREAD"):
return "standard"
elif all(readTypes == "CCS"):
return "CCS"
elif all((readTypes == "CCS") | (readTypes == "SUBREAD")):
return "mixed"
else:
return "unknown"
@property
def version(self):
return self.peer.header["HD"]["pb"]
def versionAtLeast(self, minimalVersion):
raise Unimplemented()
def softwareVersion(self, programName):
raise Unimplemented()
@property
def isSorted(self):
return self.peer.header["HD"]["SO"] == "coordinate"
@property
def isBarcoded(self):
raise Unimplemented()
@property
def isEmpty(self):
return (len(self) == 0)
def referenceInfo(self, key):
return self._referenceDict[key]
def atOffset(self, offset):
self.peer.seek(offset)
return BamAlignment(self, next(self.peer))
def hasBaseFeature(self, featureName):
return featureName in self._baseFeaturesAvailable
def baseFeaturesAvailable(self):
return self._baseFeaturesAvailable
def hasPulseFeature(self, featureName):
return featureName in self._pulseFeaturesAvailable
def pulseFeaturesAvailable(self):
return self._pulseFeaturesAvailable
def hasPulseFeatures(self):
"""
Is this BAM file a product of running analysis with the
PacBio-internal analysis mode enabled?
"""
return self.hasPulseFeature("PulseCall")
@property
def barcode(self):
raise Unimplemented()
@property
def barcodeName(self):
raise Unimplemented()
@property
def barcodes(self):
raise Unimplemented()
@requiresBai
def __len__(self):
return self.peer.mapped + self.peer.unmapped
def close(self):
if hasattr(self, "file") and self.file is not None:
self.file.close()
self.file = None
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.close()
