This page describes
Bio.SeqIO, the standard Sequence Input/Output
interface for BioPython 1.43 and later. For implementation details, see
SeqIO development page.
Python novices might find Peter’s introductory Biopython Workshop useful which start with working with sequence files using SeqIO.
There is a whole chapter in the
Bio.SeqIO, and although there is some overlap it is well worth reading
in addition to this WIKI page. There is also the API
(which you can read online, or from within Python with the help
Bio.SeqIO provides a simple uniform interface to input and output
assorted sequence file formats (including multiple sequence alignments),
but will only deal with sequences as
objects. There is a sister interface
for working directly with sequence alignment files as Alignment objects.
Note that the inclusion of
Bio.AlignIO) in Biopython does lead to some
duplication or choice in how to deal with some file formats. For
Bio.Nexus will also read sequences from Nexus files - but
Bio.Nexus can also do much more, for example reading any phylogenetic
trees in a Nexus file.
My vision is that for manipulating sequence data you should try
Bio.SeqIO as your first choice. Unless you have some very specific
requirements, I hope this should suffice.
This table lists the file formats that
Bio.SeqIO can read, write and
index, with the Biopython version where this was first supported (or
git to indicate this is supported in our latest in
development code). The format name is a simple lowercase string. Where
possible we use the same name as BioPerl’s
|abi||1.58||No||N/A||Reads the ABI “Sanger” capillary sequence traces files, including the PHRED quality scores for the base calls. This allows ABI to FASTQ conversion. Note each ABI file contains one and only one sequence (so there is no point in indexing the file).|
|abi-trim||1.71||No||N/A||Same as “abi” but with quality trimming with Mott’s algorithm.|
|ace||1.47||No||1.52||Reads the contig sequences from an ACE assembly file. Uses Bio.Sequencing.Ace internally|
|cif-atom||1.73||No||No||Uses Bio.PDB.MMCIFParser to determine the (partial) protein sequence as it appears in the structure based on the atomic coordinates.|
|cif-seqres||1.73||No||No||Reads a macromolecular Crystallographic Information File (mmCIF) file to determine the complete protein sequence as defined by the
|clustal||1.43||1.43||No||The alignment format of Clustal X and Clustal W.|
|embl||1.43||1.54||1.52||The EMBL flat file format. Uses Bio.GenBank internally.|
|fasta||1.43||1.43||1.52||This refers to the input FASTA file format introduced for Bill Pearson’s FASTA tool, where each record starts with a “>” line.|
|fasta-2line||1.71||1.71||No||FASTA format variant with no line wrapping and exactly two lines per record.|
|fastq-sanger or fastq||1.50||1.50||1.52||FASTQ files are a bit like FASTA files but also include sequencing qualities. In Biopython, “fastq” (or the alias “fastq-sanger”) refers to Sanger style FASTQ files which encode PHRED qualities using an ASCII offset of 33. See also the incompatible “fastq-solexa” and “fastq-illumina” variants used in early Solexa/Illumina pipelines, Illumina pipeline 1.8 produces Sanger FASTQ.|
|fastq-solexa||1.50||1.50||1.52||In Biopython, “fastq-solexa” refers to the original Solexa/Illumina style FASTQ files which encode Solexa qualities using an ASCII offset of 64. See also what we call the “fastq-illumina” format.|
|fastq-illumina||1.51||1.51||1.52||In Biopython, “fastq-illumina” refers to early Solexa/Illumina style FASTQ files (from pipeline version 1.3 to 1.7) which encode PHRED qualities using an ASCII offset of 64. For good quality reads, PHRED and Solexa scores are approximately equal, so the “fastq-solexa” and “fastq-illumina” variants are almost equivalent.|
|gck||1.75||No||No||The native format used by Gene Construction Kit.|
|genbank or gb||1.43||1.48 / 1.51||1.52||The GenBank or GenPept flat file format. Uses
|ig||1.47||No||1.52||This refers to the IntelliGenetics file format, apparently the same as the MASE alignment format.|
|imgt||1.56||1.56||1.56||This refers to the IMGT variant of the EMBL plain text file format.|
|nexus||1.43||1.48||No||The NEXUS multiple alignment format, also known as PAUP format. Uses
|pdb-seqres||1.61||No||No||Reads a Protein Data Bank (PDB) file to determine the complete protein sequence as it appears in the header (no dependency on
|phd||1.46||1.52||1.52||PHD files are output from PHRED, used by PHRAP and CONSED for input. Uses
|phylip||1.43||1.43||No||PHYLIP files. Truncates names at 10 characters.|
|pir||1.48||1.71||1.52||A “FASTA like” format introduced by the National Biomedical Research Foundation (NBRF) for the Protein Information Resource (PIR) database, now part of UniProt.|
|seqxml||1.58||1.58||No||Simple sequence XML file format.|
|sff||1.54||1.54||1.54||Standard Flowgram Format (SFF) binary files produced by Roche 454 and IonTorrent/IonProton sequencing machines.|
|sff-trim||1.54||No||1.54||Standard Flowgram Format applying the trimming listed in the file.|
|snapgene||1.75||No||No||The native format used by SnapGene.|
|stockholm||1.43||1.43||No||The Stockholm alignment format is also known as PFAM format.|
|swiss||1.43||No||1.52||Swiss-Prot aka UniProt format. Uses
|tab||1.48||1.48||1.52||Simple two column tab separated sequence files, where each line holds a record’s identifier and sequence. For example, this is used by Aligent’s eArray software when saving microarray probes in a minimal tab delimited text file.|
|qual||1.50||1.50||1.52||Qual files are a bit like FASTA files but instead of the sequence, record space separated integer sequencing values as PHRED quality scores. A matched pair of FASTA and QUAL files are often used as an alternative to a single FASTQ file.|
|uniprot-xml||1.56||No||1.56||UniProt XML format, successor to the plain text Swiss-Prot format.|
|xdna||1.75||1.75||No||The native format used by Christian Marck’s DNA Strider and Serial Cloner.|
Bio.SeqIO you can treat sequence alignment file formats just like
any other sequence file, but the new
module is designed to work with such alignment files directly. You can
also convert a set of
SeqRecord objects from any
file format into an alignment - provided they are all the same length.
Note that when using
Bio.SeqIO to write sequences to an alignment file
format, all the (gapped) sequences should be the same length.
The main function is
Bio.SeqIO.parse() which takes a file handle
(or filename) and format name, and returns a
This lets you do things like:
from Bio import SeqIO for record in SeqIO.parse("example.fasta", "fasta"): print(record.id)
or using a handle:
from Bio import SeqIO with open("example.fasta") as handle: for record in SeqIO.parse(handle, "fasta"): print(record.id)
In the above example, we opened the file using the built-in python
with- statement makes sure that the file is properly
closed after reading it. That should all happen automatically if you
just use the filename instead.
Note that you must specify the file format explicitly, unlike BioPerl’s SeqIO which can try to guess using the file name extension and/or the file contents. See Explicit is better than implicit (The Zen of Python).
If you had a different type of file, for example a Clustalw alignment
file such as
which contains seven sequences, the only difference is you specify
"clustal" instead of
from Bio import SeqIO with open("opuntia.aln") as handle: for record in SeqIO.parse(handle, "clustal"): print(record.id)
Iterators are great for when you only need the records one by one, in
the order found in the file. For some tasks you may need to have random
access to the records in any order. In this situation, use the built in
list() function to turn the iterator into a list:
from Bio import SeqIO records = list(SeqIO.parse("example.fasta", "fasta")) print(records.id) # first record print(records[-1].id) # last record
Another common task is to index your records by some identifier. For
small files we have a function
Bio.SeqIO.to_dict() to turn a
SeqRecord iterator (or list) into a dictionary
from Bio import SeqIO record_dict = SeqIO.to_dict(SeqIO.parse("example.fasta", "fasta")) print(record_dict["gi:12345678"]) # use any record ID
Bio.SeqIO.to_dict() will use the record ID as the
dictionary key by default, but you can specify any mapping you like with
its optional argument,
For larger files, it isn’t possible to hold everything in memory, so
Bio.SeqIO.to_dict is not suitable. Biopython 1.52 inwards
Bio.SeqIO.index function for this situation, but you
might also consider
from Bio import SeqIO record_dict = SeqIO.index("example.fasta", "fasta") print(record_dict["gi:12345678"]) # use any record ID
Biopython 1.45 introduced another function,
Bio.SeqIO.parse() will expect a handle and format. It is for
use when the handle contains one and only one record, which is returned
as a single
SeqRecord object. If there are no
records, or more than one, then an exception is raised:
from Bio import SeqIO record = SeqIO.read("single.fasta", "fasta")
For the related situation where you just want the first record (and are
happy to ignore any subsequent records), you can use the built-in python
from Bio import SeqIO first_record = next(SeqIO.parse("example.fasta", "fasta"))
For writing records to a file use the function
which takes a
SeqRecord iterator (or list),
output handle (or filename) and format string:
from Bio import SeqIO sequences = ... # add code here with open("example.fasta", "w") as output_handle: SeqIO.write(sequences, output_handle, "fasta")
from Bio import SeqIO sequences = ... # add code here SeqIO.write(sequences, "example.fasta", "fasta")
There are more examples in the following section on converting between file formats.
Note that if you are writing to an alignment file format, all your sequences must be the same length.
If you supply the sequences as a
iterator, then for sequential file formats like Fasta or GenBank, the
records can be written one by one. Because only one record is created
at a time, very little memory is required. See the example below
filtering a set of records.
On the other hand, for interlaced or non-sequential file formats like
Bio.SeqIO.write() function will be forced to
automatically convert an iterator into a list. This will destroy any
potential memory saving from using an generator/iterator approach.
Suppose you have a GenBank file which you want to turn into a Fasta
file. For example, lets consider the file
which is included in the Biopython unit tests under the GenBank
You could read the file like this, using the
from Bio import SeqIO with open("cor6_6.gb") as input_handle: for record in SeqIO.parse(input_handle, "genbank"): print(record)
Notice that this file contains six records. Now instead of printing the
records, let’s pass the
SeqRecord iterator to the
function, to turn this GenBank file into a Fasta file:
from Bio import SeqIO with open("cor6_6.gb") as input_handle, open( "cor6_6.fasta", "w" ) as output_handle: sequences = SeqIO.parse(input_handle, "genbank") count = SeqIO.write(sequences, output_handle, "fasta") print("Converted %i records" % count)
Or more concisely using the
Bio.SeqIO.convert() function (in
Biopython 1.52 or later), just:
from Bio import SeqIO count = SeqIO.convert("cor6_6.gb", "genbank", "cor6_6.fasta", "fasta") print("Converted %i records" % count)
In this example the GenBank file started like this:
LOCUS ATCOR66M 513 bp mRNA PLN 02-MAR-1992 DEFINITION A.thaliana cor6.6 mRNA. ACCESSION X55053 VERSION X55053.1 GI:16229 ...
The resulting Fasta file looks like this:
>X55053.1 A.thaliana cor6.6 mRNA. AACAAAACACACATCAAAAACGATTTTACAAGAAAAAAATA... ...
Note that all the Fasta file can store is the identifier, description and sequence.
By changing the format strings, that code could be used to convert between any supported file formats.
While you may simply want to convert a file (as shown above), a more realistic example is to manipulate or filter the data in some way.
For example, let’s save all the “short” sequences of less than 300 nucleotides to a Fasta file:
from Bio import SeqIO short_sequences =  # Setup an empty list for record in SeqIO.parse("cor6_6.gb", "genbank"): if len(record.seq) < 300: # Add this record to our list short_sequences.append(record) print("Found %i short sequences" % len(short_sequences)) SeqIO.write(short_sequences, "short_seqs.fasta", "fasta")
If you know about list comprehensions then you could have written the above example like this instead:
from Bio import SeqIO input_seq_iterator = SeqIO.parse("cor6_6.gb", "genbank") # Build a list of short sequences: short_sequences = [record for record in input_seq_iterator if len(record.seq) < 300] print("Found %i short sequences" % len(short_sequences)) SeqIO.write(short_sequences, "short_seqs.fasta", "fasta")
I’m not convinced this is actually any easier to understand, but it is shorter.
However,if you are dealing with very large files with thousands of records, you could benefit from using a generator expression instead. This avoids creating the entire list of desired records in memory:
from Bio import SeqIO input_seq_iterator = SeqIO.parse("cor6_6.gb", "genbank") short_seq_iterator = (record for record in input_seq_iterator if len(record.seq) < 300) SeqIO.write(short_seq_iterator, "short_seqs.fasta", "fasta")
Remember that for sequential file formats like Fasta or GenBank,
Bio.SeqIO.write() will accept a
SeqRecord iterator. The
advantage of the code above is that only one record will be in memory at
any one time.
However, as explained in the output section, for non-sequential file
formats like Clustal
Bio.SeqIO.write() is forced to automatically
turn the iterator into a list, so this advantage is lost.
If this is all confusing, don’t panic and just ignore the fancy stuff. For moderately sized datasets having too many records in memory at once (e.g. in lists) is probably not going to be a problem.
In this example, we’ll use
Bio.SeqIO with the
Bio.SeqUtils.CheckSum module (in Biopython 1.44 or later). First of
all, we’ll just print out the checksum for each sequence in the GenBank
from Bio import SeqIO from Bio.SeqUtils.CheckSum import seguid for record in SeqIO.parse("ls_orchid.gbk", "genbank"): print(record.id + "_" + seguid(record.seq))
You should get this output:
Z78533.1_JUEoWn6DPhgZ9nAyowsgtoD9TTo Z78532.1_MN/s0q9zDoCVEEc+k/IFwCNF2pY ... Z78439.1_H+JfaShya/4yyAj7IbMqgNkxdxQ
Now lets use the checksum function and
Bio.SeqIO.to_dict() to build
SeqRecord dictionary using the SEGUID as the
keys. The trick here is to use the Python lambda syntax to create a
temporary function to get the SEGUID for each
SeqRecord - we can’t use
seguid() function directly as it only works on
Seq objects or strings.
from Bio import SeqIO from Bio.SeqUtils.CheckSum import seguid seguid_dict = SeqIO.to_dict( SeqIO.parse("ls_orchid.gbk", "genbank"), lambda rec: seguid(rec.seq) ) record = seguid_dict["MN/s0q9zDoCVEEc+k/IFwCNF2pY"] print(record.id) print(record.description)
Giving this output:
Z78439.1 P.barbatum 5.8S rRNA gene and ITS1 and ITS2 DNA.
This script will read a Genbank file with a whole mitochondrial genome
(e.g. the tobacco mitochondrion, Nicotiana tabacum mitochondrion
create 500 records containing random fragments of this genome, and save
them as a fasta file. These subsequences are created using a random
starting points and a fixed length of 200.
from Bio import SeqIO from Bio.SeqRecord import SeqRecord from random import randint # There should be one and only one record, the entire genome: mito_record = SeqIO.read("NC_006581.gbk", "genbank") mito_frags =  limit = len(mito_record.seq) for i in range(0, 500): start = randint(0, limit - 200) end = start + 200 mito_frag = mito_record.seq[start:end] record = SeqRecord(mito_frag, "fragment_%i" % (i + 1), "", "") mito_frags.append(record) SeqIO.write(mito_frags, "mitofrags.fasta", "fasta")
That should give something like this as the output file,
>fragment_1 TGGGCCTCATATTTATCCTATATACCATGTTCGTATGGTGGCGCGATGTTCTACGTGAAT CCACGTTCGAAGGACATCATACCAAAGTCGTACAATTAGGACCTCGATATGGTTTTATTC TGTTTATCGTATCGGAGGTTATGTTCTTTTTTGCTCTTTTTCGGGCTTCTTCTCATTCTT CTTTGGCACCTACGGTAGAG ... >fragment_500 ACCCAGTGCCGCTACCCACTTCTACTAAGGCTGAGCTTAATAGGAGCAAGAGACTTGGAG GCAACAACCAGAATGAAATATTATTTAATCGTGGAAATGCCATGTCAGGCGCACCTATCA GAATCGGAACAGACCAATTACCAGATCCACCTATCATCGCCGGCATAACCATAAAAAAGA TCATTAAAAAAGCGTGAGCC
Sometimes you won’t want to write your
object(s) to a file, but to a string. For example, you might be
preparing output for display as part of a webpage. If you want to write
multiple records to a single string, use
StringIO to create a
string-based handle. The
(PDF) has an
example of this in the
For the special case where you want a single record as a string in a given file format, Biopython 1.48 added a new format method:
from Bio import SeqIO mito_record = SeqIO.read("NC_006581.gbk", "genbank") print(mito_record.format("fasta"))
The format method will take any output format supported by
where the file format can be used for a single record (e.g.
Note that we don’t recommend you use this for file output - using
Bio.SeqIO.write() is faster and more general.
If you are having problems with
Bio.SeqIO, please join the
discussion mailing list (see mailing lists).
If you think you’ve found a bug, please report it on the project’s GitHub page.