Bio.SeqIO package¶

Module contents¶

Sequence input/output as SeqRecord objects.

Bio.SeqIO is also documented at SeqIO and by a whole chapter in our tutorial:

HTML Tutorial

PDF Tutorial

Input¶

The main function is Bio.SeqIO.parse(…) which takes an input file handle (or in recent versions of Biopython alternatively a filename as a string), and format string. This returns an iterator giving SeqRecord objects:

>>> from Bio import SeqIO
>>> for record in SeqIO.parse("Fasta/f002", "fasta"):
...     print("%s %i" % (record.id, len(record)))
gi|1348912|gb|G26680|G26680 633
gi|1348917|gb|G26685|G26685 413
gi|1592936|gb|G29385|G29385 471

Note that the parse() function will invoke the relevant parser for the format with its default settings. You may want more control, in which case you need to create a format specific sequence iterator directly.

Some of these parsers are wrappers around low-level parsers which build up SeqRecord objects for the consistent SeqIO interface. In cases where the run-time is critical, such as large FASTA or FASTQ files, calling these underlying parsers will be much faster - in this case these generator functions which return tuples of strings:

>>> from Bio.SeqIO.FastaIO import SimpleFastaParser
>>> from Bio.SeqIO.QualityIO import FastqGeneralIterator

Input - Single Records¶

If you expect your file to contain one-and-only-one record, then we provide the following ‘helper’ function which will return a single SeqRecord, or raise an exception if there are no records or more than one record:

>>> from Bio import SeqIO
>>> record = SeqIO.read("Fasta/f001", "fasta")
>>> print("%s %i" % (record.id, len(record)))
gi|3318709|pdb|1A91| 79

This style is useful when you expect a single record only (and would consider multiple records an error). For example, when dealing with GenBank files for bacterial genomes or chromosomes, there is normally only a single record. Alternatively, use this with a handle when downloading a single record from the internet.

However, if you just want the first record from a file containing multiple record, use the next() function on the iterator:

>>> from Bio import SeqIO
>>> record = next(SeqIO.parse("Fasta/f002", "fasta"))
>>> print("%s %i" % (record.id, len(record)))
gi|1348912|gb|G26680|G26680 633

The above code will work as long as the file contains at least one record. Note that if there is more than one record, the remaining records will be silently ignored.

Input - Multiple Records¶

For non-interlaced files (e.g. Fasta, GenBank, EMBL) with multiple records using a sequence iterator can save you a lot of memory (RAM). There is less benefit for interlaced file formats (e.g. most multiple alignment file formats). However, an iterator only lets you access the records one by one.

If you want random access to the records by number, turn this into a list:

>>> from Bio import SeqIO
>>> records = list(SeqIO.parse("Fasta/f002", "fasta"))
>>> len(records)
3
>>> print(records[1].id)
gi|1348917|gb|G26685|G26685

If you want random access to the records by a key such as the record id, turn the iterator into a dictionary:

>>> from Bio import SeqIO
>>> record_dict = SeqIO.to_dict(SeqIO.parse("Fasta/f002", "fasta"))
>>> len(record_dict)
3
>>> print(len(record_dict["gi|1348917|gb|G26685|G26685"]))
413

However, using list() or the to_dict() function will load all the records into memory at once, and therefore is not possible on very large files. Instead, for some file formats Bio.SeqIO provides an indexing approach providing dictionary like access to any record. For example,

>>> from Bio import SeqIO
>>> record_dict = SeqIO.index("Fasta/f002", "fasta")
>>> len(record_dict)
3
>>> print(len(record_dict["gi|1348917|gb|G26685|G26685"]))
413
>>> record_dict.close()

Many but not all of the supported input file formats can be indexed like this. For example “fasta”, “fastq”, “qual” and even the binary format “sff” work, but alignment formats like “phylip”, “clustalw” and “nexus” will not.

In most cases you can also use SeqIO.index to get the record from the file as a raw string (not a SeqRecord). This can be useful for example to extract a sub-set of records from a file where SeqIO cannot output the file format (e.g. the plain text SwissProt format, “swiss”) or where it is important to keep the output 100% identical to the input). For example,

>>> from Bio import SeqIO
>>> record_dict = SeqIO.index("Fasta/f002", "fasta")
>>> len(record_dict)
3
>>> print(record_dict.get_raw("gi|1348917|gb|G26685|G26685").decode())
>gi|1348917|gb|G26685|G26685 human STS STS_D11734.
CGGAGCCAGCGAGCATATGCTGCATGAGGACCTTTCTATCTTACATTATGGCTGGGAATCTTACTCTTTC
ATCTGATACCTTGTTCAGATTTCAAAATAGTTGTAGCCTTATCCTGGTTTTACAGATGTGAAACTTTCAA
GAGATTTACTGACTTTCCTAGAATAGTTTCTCTACTGGAAACCTGATGCTTTTATAAGCCATTGTGATTA
GGATGACTGTTACAGGCTTAGCTTTGTGTGAAANCCAGTCACCTTTCTCCTAGGTAATGAGTAGTGCTGT
TCATATTACTNTAAGTTCTATAGCATACTTGCNATCCTTTANCCATGCTTATCATANGTACCATTTGAGG
AATTGNTTTGCCCTTTTGGGTTTNTTNTTGGTAAANNNTTCCCGGGTGGGGGNGGTNNNGAAA

>>> print(record_dict["gi|1348917|gb|G26685|G26685"].format("fasta"))
>gi|1348917|gb|G26685|G26685 human STS STS_D11734.
CGGAGCCAGCGAGCATATGCTGCATGAGGACCTTTCTATCTTACATTATGGCTGGGAATC
TTACTCTTTCATCTGATACCTTGTTCAGATTTCAAAATAGTTGTAGCCTTATCCTGGTTT
TACAGATGTGAAACTTTCAAGAGATTTACTGACTTTCCTAGAATAGTTTCTCTACTGGAA
ACCTGATGCTTTTATAAGCCATTGTGATTAGGATGACTGTTACAGGCTTAGCTTTGTGTG
AAANCCAGTCACCTTTCTCCTAGGTAATGAGTAGTGCTGTTCATATTACTNTAAGTTCTA
TAGCATACTTGCNATCCTTTANCCATGCTTATCATANGTACCATTTGAGGAATTGNTTTG
CCCTTTTGGGTTTNTTNTTGGTAAANNNTTCCCGGGTGGGGGNGGTNNNGAAA

>>> record_dict.close()

Here the original file and what Biopython would output differ in the line wrapping. Also note that the get_raw method will return a bytes string, hence the use of decode to turn it into a (unicode) string.

Also note that the get_raw method will preserve the newline endings. This example FASTQ file uses Unix style endings (b”n” only),

>>> from Bio import SeqIO
>>> fastq_dict = SeqIO.index("Quality/example.fastq", "fastq")
>>> len(fastq_dict)
3
>>> raw = fastq_dict.get_raw("EAS54_6_R1_2_1_540_792")
>>> raw.count(b"\n")
4
>>> raw.count(b"\r\n")
0
>>> b"\r" in raw
False
>>> len(raw)
78
>>> fastq_dict.close()

Here is the same file but using DOS/Windows new lines (b”rn” instead),

>>> from Bio import SeqIO
>>> fastq_dict = SeqIO.index("Quality/example_dos.fastq", "fastq")
>>> len(fastq_dict)
3
>>> raw = fastq_dict.get_raw("EAS54_6_R1_2_1_540_792")
>>> raw.count(b"\n")
4
>>> raw.count(b"\r\n")
4
>>> b"\r\n" in raw
True
>>> len(raw)
82
>>> fastq_dict.close()

Because this uses two bytes for each new line, the file is longer than the Unix equivalent with only one byte.

Input - Alignments¶

You can read in alignment files as alignment objects using Bio.AlignIO. Alternatively, reading in an alignment file format via Bio.SeqIO will give you a SeqRecord for each row of each alignment:

>>> from Bio import SeqIO
>>> for record in SeqIO.parse("Clustalw/hedgehog.aln", "clustal"):
...     print("%s %i" % (record.id, len(record)))
gi|167877390|gb|EDS40773.1| 447
gi|167234445|ref|NP_001107837. 447
gi|74100009|gb|AAZ99217.1| 447
gi|13990994|dbj|BAA33523.2| 447
gi|56122354|gb|AAV74328.1| 447

Output¶

Use the function Bio.SeqIO.write(…), which takes a complete set of SeqRecord objects (either as a list, or an iterator), an output file handle (or in recent versions of Biopython an output filename as a string) and of course the file format:

from Bio import SeqIO
records = ...
SeqIO.write(records, "example.faa", "fasta")

Or, using a handle:

from Bio import SeqIO
records = ...
with open("example.faa", "w") as handle:
  SeqIO.write(records, handle, "fasta")

You are expected to call this function once (with all your records) and if using a handle, make sure you close it to flush the data to the hard disk.

Output - Advanced¶

The effect of calling write() multiple times on a single file will vary depending on the file format, and is best avoided unless you have a strong reason to do so.

If you give a filename, then each time you call write() the existing file will be overwritten. For sequential files formats (e.g. fasta, genbank) each “record block” holds a single sequence. For these files it would probably be safe to call write() multiple times by re-using the same handle.

However, trying this for certain alignment formats (e.g. phylip, clustal, stockholm) would have the effect of concatenating several multiple sequence alignments together. Such files are created by the PHYLIP suite of programs for bootstrap analysis, but it is clearer to do this via Bio.AlignIO instead.

Worse, many fileformats have an explicit header and/or footer structure (e.g. any XMl format, and most binary file formats like SFF). Here making multiple calls to write() will result in an invalid file.

Conversion¶

The Bio.SeqIO.convert(…) function allows an easy interface for simple file format conversions. Additionally, it may use file format specific optimisations so this should be the fastest way too.

In general however, you can combine the Bio.SeqIO.parse(…) function with the Bio.SeqIO.write(…) function for sequence file conversion. Using generator expressions or generator functions provides a memory efficient way to perform filtering or other extra operations as part of the process.

File Formats¶

When specifying the file format, use lowercase strings. The same format names are also used in Bio.AlignIO and include the following:

abi - Applied Biosystem’s sequencing trace format

abi-trim - Same as “abi” but with quality trimming with Mott’s algorithm

ace - Reads the contig sequences from an ACE assembly file.

cif-atom - Uses Bio.PDB.MMCIFParser to determine the (partial) protein sequence as it appears in the structure based on the atomic coordinates.

cif-seqres - Reads a macromolecular Crystallographic Information File (mmCIF) file to determine the complete protein sequence as defined by the _pdbx_poly_seq_scheme records.

embl - The EMBL flat file format. Uses Bio.GenBank internally.

fasta - The generic sequence file format where each record starts with an identifer line starting with a “>” character, followed by lines of sequence.

fasta-2line - Stricter interpretation of the FASTA format using exactly two lines per record (no line wrapping).

fastq - A “FASTA like” format used by Sanger which also stores PHRED sequence quality values (with an ASCII offset of 33).

fastq-sanger - An alias for “fastq” for consistency with BioPerl and EMBOSS

fastq-solexa - Original Solexa/Illumnia variant of the FASTQ format which encodes Solexa quality scores (not PHRED quality scores) with an ASCII offset of 64.

fastq-illumina - Solexa/Illumina 1.3 to 1.7 variant of the FASTQ format which encodes PHRED quality scores with an ASCII offset of 64 (not 33). Note as of version 1.8 of the CASAVA pipeline Illumina will produce FASTQ files using the standard Sanger encoding.

gck - Gene Construction Kit’s format.

genbank - The GenBank or GenPept flat file format.

gb - An alias for “genbank”, for consistency with NCBI Entrez Utilities

ig - The IntelliGenetics file format, apparently the same as the MASE alignment format.

imgt - An EMBL like format from IMGT where the feature tables are more indented to allow for longer feature types.

nib - UCSC’s nib file format for nucleotide sequences, which uses one nibble (4 bits) to represent each nucleotide, and stores two nucleotides in one byte.

pdb-seqres - Reads a Protein Data Bank (PDB) file to determine the complete protein sequence as it appears in the header (no dependencies).

pdb-atom - Uses Bio.PDB to determine the (partial) protein sequence as it appears in the structure based on the atom coordinate section of the file (requires NumPy for Bio.PDB).

phd - Output from PHRED, used by PHRAP and CONSED for input.

pir - A “FASTA like” format introduced by the National Biomedical Research Foundation (NBRF) for the Protein Information Resource (PIR) database, now part of UniProt.

seqxml - SeqXML, simple XML format described in Schmitt et al (2011).

sff - Standard Flowgram Format (SFF), typical output from Roche 454.

sff-trim - Standard Flowgram Format (SFF) with given trimming applied.

snapgene - SnapGene’s native format.

swiss - Plain text Swiss-Prot aka UniProt format.

tab - Simple two column tab separated sequence files, where each line holds a record’s identifier and sequence. For example, this is used as by Aligent’s eArray software when saving microarray probes in a minimal tab delimited text file.

qual - A “FASTA like” format holding PHRED quality values from sequencing DNA, but no actual sequences (usually provided in separate FASTA files).

uniprot-xml - The UniProt XML format (replacement for the SwissProt plain text format which we call “swiss”)

xdna - DNA Strider’s and SerialCloner’s native format.

Note that while Bio.SeqIO can read all the above file formats, it cannot write to all of them.

You can also use any file format supported by Bio.AlignIO, such as “nexus”, “phylip” and “stockholm”, which gives you access to the individual sequences making up each alignment as SeqRecords.

Bio.SeqIO.write(sequences, handle, format)¶

Write complete set of sequences to a file.

Arguments:

sequences - A list (or iterator) of SeqRecord objects, or a single SeqRecord.
handle - File handle object to write to, or filename as string.
format - lower case string describing the file format to write.

Note if providing a file handle, your code should close the handle after calling this function (to ensure the data gets flushed to disk).

Returns the number of records written (as an integer).

Bio.SeqIO.parse(handle, format, alphabet=None)¶

Turn a sequence file into an iterator returning SeqRecords.

Arguments:

handle - handle to the file, or the filename as a string (note older versions of Biopython only took a handle).
format - lower case string describing the file format.
alphabet - optional Alphabet object, useful when the sequence type cannot be automatically inferred from the file itself (e.g. format=”fasta” or “tab”)

Typical usage, opening a file to read in, and looping over the record(s):

>>> from Bio import SeqIO
>>> filename = "Fasta/sweetpea.nu"
>>> for record in SeqIO.parse(filename, "fasta"):
...    print("ID %s" % record.id)
...    print("Sequence length %i" % len(record))
...    print("Sequence alphabet %s" % record.seq.alphabet)
ID gi|3176602|gb|U78617.1|LOU78617
Sequence length 309
Sequence alphabet SingleLetterAlphabet()

For file formats like FASTA where the alphabet cannot be determined, it may be useful to specify the alphabet explicitly:

>>> from Bio import SeqIO
>>> from Bio.Alphabet import generic_dna
>>> filename = "Fasta/sweetpea.nu"
>>> for record in SeqIO.parse(filename, "fasta", generic_dna):
...    print("ID %s" % record.id)
...    print("Sequence length %i" % len(record))
...    print("Sequence alphabet %s" % record.seq.alphabet)
ID gi|3176602|gb|U78617.1|LOU78617
Sequence length 309
Sequence alphabet DNAAlphabet()

If you have a string ‘data’ containing the file contents, you must first turn this into a handle in order to parse it:

>>> data = ">Alpha\nACCGGATGTA\n>Beta\nAGGCTCGGTTA\n"
>>> from Bio import SeqIO
>>> from io import StringIO
>>> for record in SeqIO.parse(StringIO(data), "fasta"):
...     print("%s %s" % (record.id, record.seq))
Alpha ACCGGATGTA
Beta AGGCTCGGTTA

Use the Bio.SeqIO.read(…) function when you expect a single record only.

Bio.SeqIO.read(handle, format, alphabet=None)¶

Turn a sequence file into a single SeqRecord.

Arguments:

handle - handle to the file, or the filename as a string (note older versions of Biopython only took a handle).
format - string describing the file format.
alphabet - optional Alphabet object, useful when the sequence type cannot be automatically inferred from the file itself (e.g. format=”fasta” or “tab”)

This function is for use parsing sequence files containing exactly one record. For example, reading a GenBank file:

>>> from Bio import SeqIO
>>> record = SeqIO.read("GenBank/arab1.gb", "genbank")
>>> print("ID %s" % record.id)
ID AC007323.5
>>> print("Sequence length %i" % len(record))
Sequence length 86436
>>> print("Sequence alphabet %s" % record.seq.alphabet)
Sequence alphabet IUPACAmbiguousDNA()

If the handle contains no records, or more than one record, an exception is raised. For example:

>>> from Bio import SeqIO
>>> record = SeqIO.read("GenBank/cor6_6.gb", "genbank")
Traceback (most recent call last):
    ...
ValueError: More than one record found in handle

If however you want the first record from a file containing multiple records this function would raise an exception (as shown in the example above). Instead use:

>>> from Bio import SeqIO
>>> record = next(SeqIO.parse("GenBank/cor6_6.gb", "genbank"))
>>> print("First record's ID %s" % record.id)
First record's ID X55053.1

Use the Bio.SeqIO.parse(handle, format) function if you want to read multiple records from the handle.

Bio.SeqIO.to_dict(sequences, key_function=None)¶

Turn a sequence iterator or list into a dictionary.

Arguments:

sequences - An iterator that returns SeqRecord objects, or simply a list of SeqRecord objects.
key_function - Optional callback function which when given a SeqRecord should return a unique key for the dictionary.

e.g. key_function = lambda rec : rec.name or, key_function = lambda rec : rec.description.split()[0]

If key_function is omitted then record.id is used, on the assumption that the records objects returned are SeqRecords with a unique id.

If there are duplicate keys, an error is raised.

Since Python 3.7, the default dict class maintains key order, meaning this dictionary will reflect the order of records given to it. For CPython and PyPy, this was already implemented for Python 3.6, so effectively you can always assume the record order is preserved.

Example usage, defaulting to using the record.id as key:

>>> from Bio import SeqIO
>>> filename = "GenBank/cor6_6.gb"
>>> format = "genbank"
>>> id_dict = SeqIO.to_dict(SeqIO.parse(filename, format))
>>> print(list(id_dict))
['X55053.1', 'X62281.1', 'M81224.1', 'AJ237582.1', 'L31939.1', 'AF297471.1']
>>> print(id_dict["L31939.1"].description)
Brassica rapa (clone bif72) kin mRNA, complete cds

A more complex example, using the key_function argument in order to use a sequence checksum as the dictionary key:

>>> from Bio import SeqIO
>>> from Bio.SeqUtils.CheckSum import seguid
>>> filename = "GenBank/cor6_6.gb"
>>> format = "genbank"
>>> seguid_dict = SeqIO.to_dict(SeqIO.parse(filename, format),
...               key_function = lambda rec : seguid(rec.seq))
>>> for key, record in sorted(seguid_dict.items()):
...     print("%s %s" % (key, record.id))
/wQvmrl87QWcm9llO4/efg23Vgg AJ237582.1
BUg6YxXSKWEcFFH0L08JzaLGhQs L31939.1
SabZaA4V2eLE9/2Fm5FnyYy07J4 X55053.1
TtWsXo45S3ZclIBy4X/WJc39+CY M81224.1
l7gjJFE6W/S1jJn5+1ASrUKW/FA X62281.1
uVEYeAQSV5EDQOnFoeMmVea+Oow AF297471.1

This approach is not suitable for very large sets of sequences, as all the SeqRecord objects are held in memory. Instead, consider using the Bio.SeqIO.index() function (if it supports your particular file format).

Since Python 3.6, the default dict class maintains key order, meaning this dictionary will reflect the order of records given to it. As of Biopython 1.72, on older versions of Python we explicitly use an OrderedDict so that you can always assume the record order is preserved.

Bio.SeqIO.index(filename, format, alphabet=None, key_function=None)¶

Indexes a sequence file and returns a dictionary like object.

Arguments:

filename - string giving name of file to be indexed
format - lower case string describing the file format
alphabet - optional Alphabet object, useful when the sequence type cannot be automatically inferred from the file itself (e.g. format=”fasta” or “tab”)
key_function - Optional callback function which when given a SeqRecord identifier string should return a unique key for the dictionary.

This indexing function will return a dictionary like object, giving the SeqRecord objects as values.

As of Biopython 1.69, this will preserve the ordering of the records in file when iterating over the entries.

>>> from Bio import SeqIO
>>> records = SeqIO.index("Quality/example.fastq", "fastq")
>>> len(records)
3
>>> list(records)  # make a list of the keys
['EAS54_6_R1_2_1_413_324', 'EAS54_6_R1_2_1_540_792', 'EAS54_6_R1_2_1_443_348']
>>> print(records["EAS54_6_R1_2_1_540_792"].format("fasta"))
>EAS54_6_R1_2_1_540_792
TTGGCAGGCCAAGGCCGATGGATCA

>>> "EAS54_6_R1_2_1_540_792" in records
True
>>> print(records.get("Missing", None))
None
>>> records.close()

If the file is BGZF compressed, this is detected automatically. Ordinary GZIP files are not supported:

>>> from Bio import SeqIO
>>> records = SeqIO.index("Quality/example.fastq.bgz", "fastq")
>>> len(records)
3
>>> print(records["EAS54_6_R1_2_1_540_792"].seq)
TTGGCAGGCCAAGGCCGATGGATCA
>>> records.close()

When you call the index function, it will scan through the file, noting the location of each record. When you access a particular record via the dictionary methods, the code will jump to the appropriate part of the file and then parse that section into a SeqRecord.

Note that not all the input formats supported by Bio.SeqIO can be used with this index function. It is designed to work only with sequential file formats (e.g. “fasta”, “gb”, “fastq”) and is not suitable for any interlaced file format (e.g. alignment formats such as “clustal”).

For small files, it may be more efficient to use an in memory Python dictionary, e.g.

>>> from Bio import SeqIO
>>> records = SeqIO.to_dict(SeqIO.parse("Quality/example.fastq", "fastq"))
>>> len(records)
3
>>> list(records)  # make a list of the keys
['EAS54_6_R1_2_1_413_324', 'EAS54_6_R1_2_1_540_792', 'EAS54_6_R1_2_1_443_348']
>>> print(records["EAS54_6_R1_2_1_540_792"].format("fasta"))
>EAS54_6_R1_2_1_540_792
TTGGCAGGCCAAGGCCGATGGATCA

As with the to_dict() function, by default the id string of each record is used as the key. You can specify a callback function to transform this (the record identifier string) into your preferred key. For example:

>>> from Bio import SeqIO
>>> def make_tuple(identifier):
...     parts = identifier.split("_")
...     return int(parts[-2]), int(parts[-1])
>>> records = SeqIO.index("Quality/example.fastq", "fastq",
...                       key_function=make_tuple)
>>> len(records)
3
>>> list(records)  # make a list of the keys
[(413, 324), (540, 792), (443, 348)]
>>> print(records[(540, 792)].format("fasta"))
>EAS54_6_R1_2_1_540_792
TTGGCAGGCCAAGGCCGATGGATCA

>>> (540, 792) in records
True
>>> "EAS54_6_R1_2_1_540_792" in records
False
>>> print(records.get("Missing", None))
None
>>> records.close()

Another common use case would be indexing an NCBI style FASTA file, where you might want to extract the GI number from the FASTA identifier to use as the dictionary key.

Notice that unlike the to_dict() function, here the key_function does not get given the full SeqRecord to use to generate the key. Doing so would impose a severe performance penalty as it would require the file to be completely parsed while building the index. Right now this is usually avoided.

See Also: Bio.SeqIO.index_db() and Bio.SeqIO.to_dict()

Bio.SeqIO.index_db(index_filename, filenames=None, format=None, alphabet=None, key_function=None)¶

Index several sequence files and return a dictionary like object.

The index is stored in an SQLite database rather than in memory (as in the Bio.SeqIO.index(…) function).

Arguments:

index_filename - Where to store the SQLite index
filenames - list of strings specifying file(s) to be indexed, or when indexing a single file this can be given as a string. (optional if reloading an existing index, but must match)
format - lower case string describing the file format (optional if reloading an existing index, but must match)
alphabet - optional Alphabet object, useful when the sequence type cannot be automatically inferred from the file itself (e.g. format=”fasta” or “tab”)
key_function - Optional callback function which when given a SeqRecord identifier string should return a unique key for the dictionary.

This indexing function will return a dictionary like object, giving the SeqRecord objects as values:

>>> from Bio.Alphabet import generic_protein
>>> from Bio import SeqIO
>>> files = ["GenBank/NC_000932.faa", "GenBank/NC_005816.faa"]
>>> def get_gi(name):
...     parts = name.split("|")
...     i = parts.index("gi")
...     assert i != -1
...     return parts[i+1]
>>> idx_name = ":memory:" #use an in memory SQLite DB for this test
>>> records = SeqIO.index_db(idx_name, files, "fasta", generic_protein, get_gi)
>>> len(records)
95
>>> records["7525076"].description
'gi|7525076|ref|NP_051101.1| Ycf2 [Arabidopsis thaliana]'
>>> records["45478717"].description
'gi|45478717|ref|NP_995572.1| pesticin [Yersinia pestis biovar Microtus str. 91001]'
>>> records.close()

In this example the two files contain 85 and 10 records respectively.

BGZF compressed files are supported, and detected automatically. Ordinary GZIP compressed files are not supported.

See Also: Bio.SeqIO.index() and Bio.SeqIO.to_dict(), and the Python module glob which is useful for building lists of files.

Bio.SeqIO.convert(in_file, in_format, out_file, out_format, alphabet=None)¶

Convert between two sequence file formats, return number of records.

Arguments:

in_file - an input handle or filename
in_format - input file format, lower case string
out_file - an output handle or filename
out_format - output file format, lower case string
alphabet - optional alphabet to assume

NOTE - If you provide an output filename, it will be opened which will overwrite any existing file without warning.

The idea here is that while doing this will work:

from Bio import SeqIO
records = SeqIO.parse(in_handle, in_format)
count = SeqIO.write(records, out_handle, out_format)

it is shorter to write:

from Bio import SeqIO
count = SeqIO.convert(in_handle, in_format, out_handle, out_format)

Also, Bio.SeqIO.convert is faster for some conversions as it can make some optimisations.

For example, going from a filename to a handle:

>>> from Bio import SeqIO
>>> from io import StringIO
>>> handle = StringIO("")
>>> SeqIO.convert("Quality/example.fastq", "fastq", handle, "fasta")
3
>>> print(handle.getvalue())
>EAS54_6_R1_2_1_413_324
CCCTTCTTGTCTTCAGCGTTTCTCC
>EAS54_6_R1_2_1_540_792
TTGGCAGGCCAAGGCCGATGGATCA
>EAS54_6_R1_2_1_443_348
GTTGCTTCTGGCGTGGGTGGGGGGG

Bio.SeqIO package¶

Submodules¶

Module contents¶

Input¶

Input - Single Records¶

Input - Multiple Records¶

Input - Alignments¶

Output¶

Output - Advanced¶

Conversion¶

File Formats¶