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SffIO.py
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SffIO.py
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# Copyright 2009-2020 by Peter Cock. All rights reserved.
# Based on code contributed and copyright 2009 by Jose Blanca (COMAV-UPV).
#
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
"""Bio.SeqIO support for the binary Standard Flowgram Format (SFF) file format.
SFF was designed by 454 Life Sciences (Roche), the Whitehead Institute for
Biomedical Research and the Wellcome Trust Sanger Institute. SFF was also used
as the native output format from early versions of Ion Torrent's PGM platform
as well. You are expected to use this module via the Bio.SeqIO functions under
the format name "sff" (or "sff-trim" as described below).
For example, to iterate over the records in an SFF file,
>>> from Bio import SeqIO
>>> for record in SeqIO.parse("Roche/E3MFGYR02_random_10_reads.sff", "sff"):
... print("%s %i %s..." % (record.id, len(record), record.seq[:20]))
...
E3MFGYR02JWQ7T 265 tcagGGTCTACATGTTGGTT...
E3MFGYR02JA6IL 271 tcagTTTTTTTTGGAAAGGA...
E3MFGYR02JHD4H 310 tcagAAAGACAAGTGGTATC...
E3MFGYR02GFKUC 299 tcagCGGCCGGGCCTCTCAT...
E3MFGYR02FTGED 281 tcagTGGTAATGGGGGGAAA...
E3MFGYR02FR9G7 261 tcagCTCCGTAAGAAGGTGC...
E3MFGYR02GAZMS 278 tcagAAAGAAGTAAGGTAAA...
E3MFGYR02HHZ8O 221 tcagACTTTCTTCTTTACCG...
E3MFGYR02GPGB1 269 tcagAAGCAGTGGTATCAAC...
E3MFGYR02F7Z7G 219 tcagAATCATCCACTTTTTA...
Each SeqRecord object will contain all the annotation from the SFF file,
including the PHRED quality scores.
>>> print("%s %i" % (record.id, len(record)))
E3MFGYR02F7Z7G 219
>>> print("%s..." % record.seq[:10])
tcagAATCAT...
>>> print("%r..." % (record.letter_annotations["phred_quality"][:10]))
[22, 21, 23, 28, 26, 15, 12, 21, 28, 21]...
Notice that the sequence is given in mixed case, the central upper case region
corresponds to the trimmed sequence. This matches the output of the Roche
tools (and the 3rd party tool sff_extract) for SFF to FASTA.
>>> print(record.annotations["clip_qual_left"])
4
>>> print(record.annotations["clip_qual_right"])
134
>>> print(record.seq[:4])
tcag
>>> print("%s...%s" % (record.seq[4:20], record.seq[120:134]))
AATCATCCACTTTTTA...CAAAACACAAACAG
>>> print(record.seq[134:])
atcttatcaacaaaactcaaagttcctaactgagacacgcaacaggggataagacaaggcacacaggggataggnnnnnnnnnnn
The annotations dictionary also contains any adapter clip positions
(usually zero), and information about the flows. e.g.
>>> len(record.annotations)
12
>>> print(record.annotations["flow_key"])
TCAG
>>> print(record.annotations["flow_values"][:10])
(83, 1, 128, 7, 4, 84, 6, 106, 3, 172)
>>> print(len(record.annotations["flow_values"]))
400
>>> print(record.annotations["flow_index"][:10])
(1, 2, 3, 2, 2, 0, 3, 2, 3, 3)
>>> print(len(record.annotations["flow_index"]))
219
Note that to convert from a raw reading in flow_values to the corresponding
homopolymer stretch estimate, the value should be rounded to the nearest 100:
>>> print("%r..." % [int(round(value, -2)) // 100
... for value in record.annotations["flow_values"][:10]])
...
[1, 0, 1, 0, 0, 1, 0, 1, 0, 2]...
If a read name is exactly 14 alphanumeric characters, the annotations
dictionary will also contain meta-data about the read extracted by
interpreting the name as a 454 Sequencing System "Universal" Accession
Number. Note that if a read name happens to be exactly 14 alphanumeric
characters but was not generated automatically, these annotation records
will contain nonsense information.
>>> print(record.annotations["region"])
2
>>> print(record.annotations["time"])
[2008, 1, 9, 16, 16, 0]
>>> print(record.annotations["coords"])
(2434, 1658)
As a convenience method, you can read the file with SeqIO format name "sff-trim"
instead of "sff" to get just the trimmed sequences (without any annotation
except for the PHRED quality scores and anything encoded in the read names):
>>> from Bio import SeqIO
>>> for record in SeqIO.parse("Roche/E3MFGYR02_random_10_reads.sff", "sff-trim"):
... print("%s %i %s..." % (record.id, len(record), record.seq[:20]))
...
E3MFGYR02JWQ7T 260 GGTCTACATGTTGGTTAACC...
E3MFGYR02JA6IL 265 TTTTTTTTGGAAAGGAAAAC...
E3MFGYR02JHD4H 292 AAAGACAAGTGGTATCAACG...
E3MFGYR02GFKUC 295 CGGCCGGGCCTCTCATCGGT...
E3MFGYR02FTGED 277 TGGTAATGGGGGGAAATTTA...
E3MFGYR02FR9G7 256 CTCCGTAAGAAGGTGCTGCC...
E3MFGYR02GAZMS 271 AAAGAAGTAAGGTAAATAAC...
E3MFGYR02HHZ8O 150 ACTTTCTTCTTTACCGTAAC...
E3MFGYR02GPGB1 221 AAGCAGTGGTATCAACGCAG...
E3MFGYR02F7Z7G 130 AATCATCCACTTTTTAACGT...
Looking at the final record in more detail, note how this differs to the
example above:
>>> print("%s %i" % (record.id, len(record)))
E3MFGYR02F7Z7G 130
>>> print("%s..." % record.seq[:10])
AATCATCCAC...
>>> print("%r..." % record.letter_annotations["phred_quality"][:10])
[26, 15, 12, 21, 28, 21, 36, 28, 27, 27]...
>>> len(record.annotations)
4
>>> print(record.annotations["region"])
2
>>> print(record.annotations["coords"])
(2434, 1658)
>>> print(record.annotations["time"])
[2008, 1, 9, 16, 16, 0]
>>> print(record.annotations["molecule_type"])
DNA
You might use the Bio.SeqIO.convert() function to convert the (trimmed) SFF
reads into a FASTQ file (or a FASTA file and a QUAL file), e.g.
>>> from Bio import SeqIO
>>> from io import StringIO
>>> out_handle = StringIO()
>>> count = SeqIO.convert("Roche/E3MFGYR02_random_10_reads.sff", "sff",
... out_handle, "fastq")
...
>>> print("Converted %i records" % count)
Converted 10 records
The output FASTQ file would start like this:
>>> print("%s..." % out_handle.getvalue()[:50])
@E3MFGYR02JWQ7T
tcagGGTCTACATGTTGGTTAACCCGTACTGATT...
Bio.SeqIO.index() provides memory efficient random access to the reads in an
SFF file by name. SFF files can include an index within the file, which can
be read in making this very fast. If the index is missing (or in a format not
yet supported in Biopython) the file is indexed by scanning all the reads -
which is a little slower. For example,
>>> from Bio import SeqIO
>>> reads = SeqIO.index("Roche/E3MFGYR02_random_10_reads.sff", "sff")
>>> record = reads["E3MFGYR02JHD4H"]
>>> print("%s %i %s..." % (record.id, len(record), record.seq[:20]))
E3MFGYR02JHD4H 310 tcagAAAGACAAGTGGTATC...
>>> reads.close()
Or, using the trimmed reads:
>>> from Bio import SeqIO
>>> reads = SeqIO.index("Roche/E3MFGYR02_random_10_reads.sff", "sff-trim")
>>> record = reads["E3MFGYR02JHD4H"]
>>> print("%s %i %s..." % (record.id, len(record), record.seq[:20]))
E3MFGYR02JHD4H 292 AAAGACAAGTGGTATCAACG...
>>> reads.close()
You can also use the Bio.SeqIO.write() function with the "sff" format. Note
that this requires all the flow information etc, and thus is probably only
useful for SeqRecord objects originally from reading another SFF file (and
not the trimmed SeqRecord objects from parsing an SFF file as "sff-trim").
As an example, let's pretend this example SFF file represents some DNA which
was pre-amplified with a PCR primers AAAGANNNNN. The following script would
produce a sub-file containing all those reads whose post-quality clipping
region (i.e. the sequence after trimming) starts with AAAGA exactly (the non-
degenerate bit of this pretend primer):
>>> from Bio import SeqIO
>>> records = (record for record in
... SeqIO.parse("Roche/E3MFGYR02_random_10_reads.sff", "sff")
... if record.seq[record.annotations["clip_qual_left"]:].startswith("AAAGA"))
...
>>> count = SeqIO.write(records, "temp_filtered.sff", "sff")
>>> print("Selected %i records" % count)
Selected 2 records
Of course, for an assembly you would probably want to remove these primers.
If you want FASTA or FASTQ output, you could just slice the SeqRecord. However,
if you want SFF output we have to preserve all the flow information - the trick
is just to adjust the left clip position!
>>> from Bio import SeqIO
>>> def filter_and_trim(records, primer):
... for record in records:
... if record.seq[record.annotations["clip_qual_left"]:].startswith(primer):
... record.annotations["clip_qual_left"] += len(primer)
... yield record
...
>>> records = SeqIO.parse("Roche/E3MFGYR02_random_10_reads.sff", "sff")
>>> count = SeqIO.write(filter_and_trim(records, "AAAGA"),
... "temp_filtered.sff", "sff")
...
>>> print("Selected %i records" % count)
Selected 2 records
We can check the results, note the lower case clipped region now includes the "AAAGA"
sequence:
>>> for record in SeqIO.parse("temp_filtered.sff", "sff"):
... print("%s %i %s..." % (record.id, len(record), record.seq[:20]))
...
E3MFGYR02JHD4H 310 tcagaaagaCAAGTGGTATC...
E3MFGYR02GAZMS 278 tcagaaagaAGTAAGGTAAA...
>>> for record in SeqIO.parse("temp_filtered.sff", "sff-trim"):
... print("%s %i %s..." % (record.id, len(record), record.seq[:20]))
...
E3MFGYR02JHD4H 287 CAAGTGGTATCAACGCAGAG...
E3MFGYR02GAZMS 266 AGTAAGGTAAATAACAAACG...
>>> import os
>>> os.remove("temp_filtered.sff")
For a description of the file format, please see the Roche manuals and:
http://www.ncbi.nlm.nih.gov/Traces/trace.cgi?cmd=show&f=formats&m=doc&s=formats
"""
import re
import struct
from Bio import StreamModeError
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from .Interfaces import SequenceIterator
from .Interfaces import SequenceWriter
_null = b"\0"
_sff = b".sff"
_hsh = b".hsh"
_srt = b".srt"
_mft = b".mft"
_flag = b"\xff"
def _sff_file_header(handle):
"""Read in an SFF file header (PRIVATE).
Assumes the handle is at the start of the file, will read forwards
though the header and leave the handle pointing at the first record.
Returns a tuple of values from the header (header_length, index_offset,
index_length, number_of_reads, flows_per_read, flow_chars, key_sequence)
>>> with open("Roche/greek.sff", "rb") as handle:
... values = _sff_file_header(handle)
...
>>> print(values[0])
840
>>> print(values[1])
65040
>>> print(values[2])
256
>>> print(values[3])
24
>>> print(values[4])
800
>>> values[-1]
'TCAG'
"""
# file header (part one)
# use big endiean encdoing >
# magic_number I
# version 4B
# index_offset Q
# index_length I
# number_of_reads I
# header_length H
# key_length H
# number_of_flows_per_read H
# flowgram_format_code B
# [rest of file header depends on the number of flows and how many keys]
fmt = ">4s4BQIIHHHB"
assert 31 == struct.calcsize(fmt)
data = handle.read(31)
if not data:
raise ValueError("Empty file.")
elif len(data) < 31:
raise ValueError("File too small to hold a valid SFF header.")
try:
(
magic_number,
ver0,
ver1,
ver2,
ver3,
index_offset,
index_length,
number_of_reads,
header_length,
key_length,
number_of_flows_per_read,
flowgram_format,
) = struct.unpack(fmt, data)
except TypeError:
raise StreamModeError("SFF files must be opened in binary mode.") from None
if magic_number in [_hsh, _srt, _mft]:
# Probably user error, calling Bio.SeqIO.parse() twice!
raise ValueError("Handle seems to be at SFF index block, not start")
if magic_number != _sff: # 779314790
raise ValueError("SFF file did not start '.sff', but %r" % magic_number)
if (ver0, ver1, ver2, ver3) != (0, 0, 0, 1):
raise ValueError(
"Unsupported SFF version in header, %i.%i.%i.%i" % (ver0, ver1, ver2, ver3)
)
if flowgram_format != 1:
raise ValueError("Flowgram format code %i not supported" % flowgram_format)
if (index_offset != 0) ^ (index_length != 0):
raise ValueError(
"Index offset %i but index length %i" % (index_offset, index_length)
)
flow_chars = handle.read(number_of_flows_per_read).decode("ASCII")
key_sequence = handle.read(key_length).decode("ASCII")
# According to the spec, the header_length field should be the total number
# of bytes required by this set of header fields, and should be equal to
# "31 + number_of_flows_per_read + key_length" rounded up to the next value
# divisible by 8.
assert header_length % 8 == 0
padding = header_length - number_of_flows_per_read - key_length - 31
assert 0 <= padding < 8, padding
if handle.read(padding).count(_null) != padding:
import warnings
from Bio import BiopythonParserWarning
warnings.warn(
"Your SFF file is invalid, post header %i byte "
"null padding region contained data." % padding,
BiopythonParserWarning,
)
return (
header_length,
index_offset,
index_length,
number_of_reads,
number_of_flows_per_read,
flow_chars,
key_sequence,
)
def _sff_do_slow_index(handle):
"""Generate an index by scanning though all the reads in an SFF file (PRIVATE).
This is a slow but generic approach if we can't parse the provided index
(if present).
Will use the handle seek/tell functions.
"""
handle.seek(0)
(
header_length,
index_offset,
index_length,
number_of_reads,
number_of_flows_per_read,
flow_chars,
key_sequence,
) = _sff_file_header(handle)
# Now on to the reads...
read_header_fmt = ">2HI4H"
read_header_size = struct.calcsize(read_header_fmt)
# NOTE - assuming flowgram_format==1, which means struct type H
read_flow_fmt = ">%iH" % number_of_flows_per_read
read_flow_size = struct.calcsize(read_flow_fmt)
assert 1 == struct.calcsize(">B")
assert 1 == struct.calcsize(">s")
assert 1 == struct.calcsize(">c")
assert read_header_size % 8 == 0 # Important for padding calc later!
for read in range(number_of_reads):
record_offset = handle.tell()
if record_offset == index_offset:
# Found index block within reads, ignore it:
offset = index_offset + index_length
if offset % 8:
offset += 8 - (offset % 8)
assert offset % 8 == 0
handle.seek(offset)
record_offset = offset
# assert record_offset%8 == 0 # Worth checking, but slow
# First the fixed header
data = handle.read(read_header_size)
(
read_header_length,
name_length,
seq_len,
clip_qual_left,
clip_qual_right,
clip_adapter_left,
clip_adapter_right,
) = struct.unpack(read_header_fmt, data)
if read_header_length < 10 or read_header_length % 8 != 0:
raise ValueError(
"Malformed read header, says length is %i:\n%r"
% (read_header_length, data)
)
# now the name and any padding (remainder of header)
name = handle.read(name_length).decode()
padding = read_header_length - read_header_size - name_length
if handle.read(padding).count(_null) != padding:
import warnings
from Bio import BiopythonParserWarning
warnings.warn(
"Your SFF file is invalid, post name %i byte "
"padding region contained data" % padding,
BiopythonParserWarning,
)
assert record_offset + read_header_length == handle.tell()
# now the flowgram values, flowgram index, bases and qualities
size = read_flow_size + 3 * seq_len
handle.seek(size, 1)
# now any padding...
padding = size % 8
if padding:
padding = 8 - padding
if handle.read(padding).count(_null) != padding:
import warnings
from Bio import BiopythonParserWarning
warnings.warn(
"Your SFF file is invalid, post quality %i "
"byte padding region contained data" % padding,
BiopythonParserWarning,
)
# print("%s %s %i" % (read, name, record_offset))
yield name, record_offset
if handle.tell() % 8 != 0:
raise ValueError("After scanning reads, did not end on a multiple of 8")
def _sff_find_roche_index(handle):
"""Locate any existing Roche style XML meta data and read index (PRIVATE).
Makes a number of hard coded assumptions based on reverse engineered SFF
files from Roche 454 machines.
Returns a tuple of read count, SFF "index" offset and size, XML offset
and size, and the actual read index offset and size.
Raises a ValueError for unsupported or non-Roche index blocks.
"""
handle.seek(0)
(
header_length,
index_offset,
index_length,
number_of_reads,
number_of_flows_per_read,
flow_chars,
key_sequence,
) = _sff_file_header(handle)
assert handle.tell() == header_length
if not index_offset or not index_length:
raise ValueError("No index present in this SFF file")
# Now jump to the header...
handle.seek(index_offset)
fmt = ">4s4B"
fmt_size = struct.calcsize(fmt)
data = handle.read(fmt_size)
if not data:
raise ValueError(
"Premature end of file? Expected index of size %i at offest %i, found nothing"
% (index_length, index_offset)
)
if len(data) < fmt_size:
raise ValueError(
"Premature end of file? Expected index of size %i at offest %i, found %r"
% (index_length, index_offset, data)
)
magic_number, ver0, ver1, ver2, ver3 = struct.unpack(fmt, data)
if magic_number == _mft: # 778921588
# Roche 454 manifest index
# This is typical from raw Roche 454 SFF files (2009), and includes
# both an XML manifest and the sorted index.
if (ver0, ver1, ver2, ver3) != (49, 46, 48, 48):
# This is "1.00" as a string
raise ValueError(
"Unsupported version in .mft index header, %i.%i.%i.%i"
% (ver0, ver1, ver2, ver3)
)
fmt2 = ">LL"
fmt2_size = struct.calcsize(fmt2)
xml_size, data_size = struct.unpack(fmt2, handle.read(fmt2_size))
if index_length != fmt_size + fmt2_size + xml_size + data_size:
raise ValueError(
"Problem understanding .mft index header, %i != %i + %i + %i + %i"
% (index_length, fmt_size, fmt2_size, xml_size, data_size)
)
return (
number_of_reads,
header_length,
index_offset,
index_length,
index_offset + fmt_size + fmt2_size,
xml_size,
index_offset + fmt_size + fmt2_size + xml_size,
data_size,
)
elif magic_number == _srt: # 779317876
# Roche 454 sorted index
# I've had this from Roche tool sfffile when the read identifiers
# had nonstandard lengths and there was no XML manifest.
if (ver0, ver1, ver2, ver3) != (49, 46, 48, 48):
# This is "1.00" as a string
raise ValueError(
"Unsupported version in .srt index header, %i.%i.%i.%i"
% (ver0, ver1, ver2, ver3)
)
data = handle.read(4)
if data != _null * 4:
raise ValueError("Did not find expected null four bytes in .srt index")
return (
number_of_reads,
header_length,
index_offset,
index_length,
0,
0,
index_offset + fmt_size + 4,
index_length - fmt_size - 4,
)
elif magic_number == _hsh:
raise ValueError(
"Hash table style indexes (.hsh) in SFF files are not (yet) supported"
)
else:
raise ValueError(
"Unknown magic number %r in SFF index header:\n%r" % (magic_number, data)
)
def ReadRocheXmlManifest(handle):
"""Read any Roche style XML manifest data in the SFF "index".
The SFF file format allows for multiple different index blocks, and Roche
took advantage of this to define their own index block which also embeds
an XML manifest string. This is not a publicly documented extension to
the SFF file format, this was reverse engineered.
The handle should be to an SFF file opened in binary mode. This function
will use the handle seek/tell functions and leave the handle in an
arbitrary location.
Any XML manifest found is returned as a Python string, which you can then
parse as appropriate, or reuse when writing out SFF files with the
SffWriter class.
Returns a string, or raises a ValueError if an Roche manifest could not be
found.
"""
(
number_of_reads,
header_length,
index_offset,
index_length,
xml_offset,
xml_size,
read_index_offset,
read_index_size,
) = _sff_find_roche_index(handle)
if not xml_offset or not xml_size:
raise ValueError("No XML manifest found")
handle.seek(xml_offset)
return handle.read(xml_size).decode()
# This is a generator function!
def _sff_read_roche_index(handle):
"""Read any existing Roche style read index provided in the SFF file (PRIVATE).
Will use the handle seek/tell functions.
This works on ".srt1.00" and ".mft1.00" style Roche SFF index blocks.
Roche SFF indices use base 255 not 256, meaning we see bytes in range the
range 0 to 254 only. This appears to be so that byte 0xFF (character 255)
can be used as a marker character to separate entries (required if the
read name lengths vary).
Note that since only four bytes are used for the read offset, this is
limited to 255^4 bytes (nearly 4GB). If you try to use the Roche sfffile
tool to combine SFF files beyound this limit, they issue a warning and
omit the index (and manifest).
"""
(
number_of_reads,
header_length,
index_offset,
index_length,
xml_offset,
xml_size,
read_index_offset,
read_index_size,
) = _sff_find_roche_index(handle)
# Now parse the read index...
handle.seek(read_index_offset)
fmt = ">5B"
for read in range(number_of_reads):
# TODO - Be more aware of when the index should end?
data = handle.read(6)
while True:
more = handle.read(1)
if not more:
raise ValueError("Premature end of file!")
data += more
if more == _flag:
break
assert data[-1:] == _flag, data[-1:]
name = data[:-6].decode()
off4, off3, off2, off1, off0 = struct.unpack(fmt, data[-6:-1])
offset = off0 + 255 * off1 + 65025 * off2 + 16581375 * off3
if off4:
# Could in theory be used as a fifth piece of offset information,
# i.e. offset =+ 4228250625L*off4, but testing the Roche tools this
# is not the case. They simple don't support such large indexes.
raise ValueError("Expected a null terminator to the read name.")
yield name, offset
if handle.tell() != read_index_offset + read_index_size:
raise ValueError(
"Problem with index length? %i vs %i"
% (handle.tell(), read_index_offset + read_index_size)
)
_valid_UAN_read_name = re.compile(r"^[a-zA-Z0-9]{14}$")
def _sff_read_seq_record(
handle, number_of_flows_per_read, flow_chars, key_sequence, trim=False
):
"""Parse the next read in the file, return data as a SeqRecord (PRIVATE)."""
# Now on to the reads...
# the read header format (fixed part):
# read_header_length H
# name_length H
# seq_len I
# clip_qual_left H
# clip_qual_right H
# clip_adapter_left H
# clip_adapter_right H
# [rest of read header depends on the name length etc]
read_header_fmt = ">2HI4H"
read_header_size = struct.calcsize(read_header_fmt)
read_flow_fmt = ">%iH" % number_of_flows_per_read
read_flow_size = struct.calcsize(read_flow_fmt)
(
read_header_length,
name_length,
seq_len,
clip_qual_left,
clip_qual_right,
clip_adapter_left,
clip_adapter_right,
) = struct.unpack(read_header_fmt, handle.read(read_header_size))
if clip_qual_left:
clip_qual_left -= 1 # python counting
if clip_adapter_left:
clip_adapter_left -= 1 # python counting
if read_header_length < 10 or read_header_length % 8 != 0:
raise ValueError(
"Malformed read header, says length is %i" % read_header_length
)
# now the name and any padding (remainder of header)
name = handle.read(name_length).decode()
padding = read_header_length - read_header_size - name_length
if handle.read(padding).count(_null) != padding:
import warnings
from Bio import BiopythonParserWarning
warnings.warn(
"Your SFF file is invalid, post name %i "
"byte padding region contained data" % padding,
BiopythonParserWarning,
)
# now the flowgram values, flowgram index, bases and qualities
# NOTE - assuming flowgram_format==1, which means struct type H
flow_values = handle.read(read_flow_size) # unpack later if needed
temp_fmt = ">%iB" % seq_len # used for flow index and quals
flow_index = handle.read(seq_len) # unpack later if needed
seq = handle.read(seq_len) # Leave as bytes for Seq object
quals = list(struct.unpack(temp_fmt, handle.read(seq_len)))
# now any padding...
padding = (read_flow_size + seq_len * 3) % 8
if padding:
padding = 8 - padding
if handle.read(padding).count(_null) != padding:
import warnings
from Bio import BiopythonParserWarning
warnings.warn(
"Your SFF file is invalid, post quality %i "
"byte padding region contained data" % padding,
BiopythonParserWarning,
)
# Follow Roche and apply most aggressive of qual and adapter clipping.
# Note Roche seems to ignore adapter clip fields when writing SFF,
# and uses just the quality clipping values for any clipping.
clip_left = max(clip_qual_left, clip_adapter_left)
# Right clipping of zero means no clipping
if clip_qual_right:
if clip_adapter_right:
clip_right = min(clip_qual_right, clip_adapter_right)
else:
# Typical case with Roche SFF files
clip_right = clip_qual_right
elif clip_adapter_right:
clip_right = clip_adapter_right
else:
clip_right = seq_len
# Now build a SeqRecord
if trim:
if clip_left >= clip_right:
# Raise an error?
import warnings
from Bio import BiopythonParserWarning
warnings.warn(
"Overlapping clip values in SFF record, trimmed to nothing",
BiopythonParserWarning,
)
seq = ""
quals = []
else:
seq = seq[clip_left:clip_right].upper()
quals = quals[clip_left:clip_right]
# Don't record the clipping values, flow etc, they make no sense now:
annotations = {}
else:
if clip_left >= clip_right:
import warnings
from Bio import BiopythonParserWarning
warnings.warn(
"Overlapping clip values in SFF record", BiopythonParserWarning
)
seq = seq.lower()
else:
# This use of mixed case mimics the Roche SFF tool's FASTA output
seq = (
seq[:clip_left].lower()
+ seq[clip_left:clip_right].upper()
+ seq[clip_right:].lower()
)
annotations = {
"flow_values": struct.unpack(read_flow_fmt, flow_values),
"flow_index": struct.unpack(temp_fmt, flow_index),
"flow_chars": flow_chars,
"flow_key": key_sequence,
"clip_qual_left": clip_qual_left,
"clip_qual_right": clip_qual_right,
"clip_adapter_left": clip_adapter_left,
"clip_adapter_right": clip_adapter_right,
}
if re.match(_valid_UAN_read_name, name):
annotations["time"] = _get_read_time(name)
annotations["region"] = _get_read_region(name)
annotations["coords"] = _get_read_xy(name)
annotations["molecule_type"] = "DNA"
record = SeqRecord(
Seq(seq), id=name, name=name, description="", annotations=annotations
)
# Dirty trick to speed up this line:
# record.letter_annotations["phred_quality"] = quals
dict.__setitem__(record._per_letter_annotations, "phred_quality", quals)
# Return the record and then continue...
return record
_powers_of_36 = [36 ** i for i in range(6)]
def _string_as_base_36(string):
"""Interpret a string as a base-36 number as per 454 manual (PRIVATE)."""
total = 0
for c, power in zip(string[::-1], _powers_of_36):
# For reference: ord('0') = 48, ord('9') = 57
# For reference: ord('A') = 65, ord('Z') = 90
# For reference: ord('a') = 97, ord('z') = 122
if 48 <= ord(c) <= 57:
val = ord(c) - 22 # equivalent to: - ord('0') + 26
elif 65 <= ord(c) <= 90:
val = ord(c) - 65
elif 97 <= ord(c) <= 122:
val = ord(c) - 97
else:
# Invalid character
val = 0
total += val * power
return total
def _get_read_xy(read_name):
"""Extract coordinates from last 5 characters of read name (PRIVATE)."""
number = _string_as_base_36(read_name[9:])
return divmod(number, 4096)
_time_denominators = [
13 * 32 * 24 * 60 * 60,
32 * 24 * 60 * 60,
24 * 60 * 60,
60 * 60,
60,
]
def _get_read_time(read_name):
"""Extract time from first 6 characters of read name (PRIVATE)."""
time_list = []
remainder = _string_as_base_36(read_name[:6])
for denominator in _time_denominators:
this_term, remainder = divmod(remainder, denominator)
time_list.append(this_term)
time_list.append(remainder)
time_list[0] += 2000
return time_list
def _get_read_region(read_name):
"""Extract region from read name (PRIVATE)."""
return int(read_name[8])
def _sff_read_raw_record(handle, number_of_flows_per_read):
"""Extract the next read in the file as a raw (bytes) string (PRIVATE)."""
read_header_fmt = ">2HI"
read_header_size = struct.calcsize(read_header_fmt)
read_flow_fmt = ">%iH" % number_of_flows_per_read
read_flow_size = struct.calcsize(read_flow_fmt)
raw = handle.read(read_header_size)
read_header_length, name_length, seq_len = struct.unpack(read_header_fmt, raw)
if read_header_length < 10 or read_header_length % 8 != 0:
raise ValueError(
"Malformed read header, says length is %i" % read_header_length
)
# now the four clip values (4H = 8 bytes), and read name
raw += handle.read(8 + name_length)
# and any padding (remainder of header)
padding = read_header_length - read_header_size - 8 - name_length
pad = handle.read(padding)
if pad.count(_null) != padding:
import warnings
from Bio import BiopythonParserWarning
warnings.warn(
"Your SFF file is invalid, post name %i "
"byte padding region contained data" % padding,
BiopythonParserWarning,
)
raw += pad
# now the flowgram values, flowgram index, bases and qualities
raw += handle.read(read_flow_size + seq_len * 3)
padding = (read_flow_size + seq_len * 3) % 8
# now any padding...
if padding:
padding = 8 - padding
pad = handle.read(padding)
if pad.count(_null) != padding:
import warnings
from Bio import BiopythonParserWarning
warnings.warn(
"Your SFF file is invalid, post quality %i "
"byte padding region contained data" % padding,
BiopythonParserWarning,
)
raw += pad
# Return the raw bytes
return raw
class _AddTellHandle:
"""Wrapper for handles which do not support the tell method (PRIVATE).
Intended for use with things like network handles where tell (and reverse
seek) are not supported. The SFF file needs to track the current offset in
order to deal with the index block.
"""
def __init__(self, handle):
self._handle = handle
self._offset = 0
def read(self, length):
data = self._handle.read(length)
self._offset += len(data)
return data
def tell(self):
return self._offset
def seek(self, offset):
if offset < self._offset:
raise RuntimeError("Can't seek backwards")
self._handle.read(offset - self._offset)
def close(self):
return self._handle.close()
class SffIterator(SequenceIterator):
"""Parser for Standard Flowgram Format (SFF) files."""
def __init__(self, source, alphabet=None, trim=False):
"""Iterate over Standard Flowgram Format (SFF) reads (as SeqRecord objects).
- source - path to an SFF file, e.g. from Roche 454 sequencing,
or a file-like object opened in binary mode.
- alphabet - optional alphabet, unused. Leave as None.
- trim - should the sequences be trimmed?
The resulting SeqRecord objects should match those from a paired FASTA
and QUAL file converted from the SFF file using the Roche 454 tool
ssfinfo. i.e. The sequence will be mixed case, with the trim regions
shown in lower case.
This function is used internally via the Bio.SeqIO functions:
>>> from Bio import SeqIO
>>> for record in SeqIO.parse("Roche/E3MFGYR02_random_10_reads.sff", "sff"):
... print("%s %i" % (record.id, len(record)))
...
E3MFGYR02JWQ7T 265
E3MFGYR02JA6IL 271
E3MFGYR02JHD4H 310
E3MFGYR02GFKUC 299
E3MFGYR02FTGED 281
E3MFGYR02FR9G7 261
E3MFGYR02GAZMS 278
E3MFGYR02HHZ8O 221
E3MFGYR02GPGB1 269
E3MFGYR02F7Z7G 219
You can also call it directly:
>>> with open("Roche/E3MFGYR02_random_10_reads.sff", "rb") as handle:
... for record in SffIterator(handle):
... print("%s %i" % (record.id, len(record)))
...
E3MFGYR02JWQ7T 265
E3MFGYR02JA6IL 271
E3MFGYR02JHD4H 310
E3MFGYR02GFKUC 299
E3MFGYR02FTGED 281
E3MFGYR02FR9G7 261
E3MFGYR02GAZMS 278
E3MFGYR02HHZ8O 221
E3MFGYR02GPGB1 269
E3MFGYR02F7Z7G 219
Or, with the trim option:
>>> with open("Roche/E3MFGYR02_random_10_reads.sff", "rb") as handle:
... for record in SffIterator(handle, trim=True):
... print("%s %i" % (record.id, len(record)))
...
E3MFGYR02JWQ7T 260
E3MFGYR02JA6IL 265
E3MFGYR02JHD4H 292
E3MFGYR02GFKUC 295
E3MFGYR02FTGED 277
E3MFGYR02FR9G7 256
E3MFGYR02GAZMS 271
E3MFGYR02HHZ8O 150
E3MFGYR02GPGB1 221
E3MFGYR02F7Z7G 130
"""
if alphabet is not None:
raise ValueError("The alphabet argument is no longer supported")
super().__init__(source, mode="b", fmt="SFF")
self.trim = trim
def parse(self, handle):
"""Start parsing the file, and return a SeqRecord generator."""
try:
if 0 != handle.tell():
raise ValueError("Not at start of file, offset %i" % handle.tell())
except AttributeError:
# Probably a network handle or something like that
handle = _AddTellHandle(handle)
records = self.iterate(handle)