Edit this page on GitHub

Introduction to SeqIO

This page describes Bio.SeqIO, the standard Sequence Input/Output interface for BioPython 1.43 and later. For implementation details, see the 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 Tutorial (PDF) on Bio.SeqIO, and although there is some overlap it is well worth reading in addition to this WIKI page. There is also the API documentation (which you can read online, or from within Python with the help command).


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 SeqRecord objects. There is a sister interface Bio.AlignIO for working directly with sequence alignment files as Alignment objects.

The design was partly inspired by the simplicity of BioPerl’s SeqIO. In the long term we hope to match BioPerl’s impressive list of supported sequence file formats and multiple alignment formats.

Note that the inclusion of Bio.SeqIO (and Bio.AlignIO) in Biopython does lead to some duplication or choice in how to deal with some file formats. For example, 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.


File Formats

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 SeqIO and EMBOSS.

Format name Read Write Index Notes
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 _pdbx_poly_seq_scheme records.
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 Bio.GenBank internally for parsing. Biopython 1.48 to 1.50 wrote basic GenBank files with only minimal annotation, while 1.51 onwards will also write the features table.
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 Bio.Nexus internally.
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 Bio.PDB and NumPy).
pdb-atom 1.61 No No 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).
phd 1.46 1.52 1.52 PHD files are output from PHRED, used by PHRAP and CONSED for input. Uses Bio.Sequencing.Phd internally.
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 Bio.SwissProt internally. See also the UniProt XML format.
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.

With Bio.SeqIO you can treat sequence alignment file formats just like any other sequence file, but the new Bio.AlignIO 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.

Sequence Input

The main function is Bio.SeqIO.parse() which takes a file handle (or filename) and format name, and returns a SeqRecord iterator. This lets you do things like:

from Bio import SeqIO

for record in SeqIO.parse("example.fasta", "fasta"):

or using a handle:

from Bio import SeqIO

with open("example.fasta") as handle:
    for record in SeqIO.parse(handle, "fasta"):

In the above example, we opened the file using the built-in python function open. The 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 opuntia.aln which contains seven sequences, the only difference is you specify "clustal" instead of "fasta":

from Bio import SeqIO

with open("opuntia.aln") as handle:
    for record in SeqIO.parse(handle, "clustal"):

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 python list() function to turn the iterator into a list:

from Bio import SeqIO

records = list(SeqIO.parse("example.fasta", "fasta"))
print(records[0].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 (in memory):

from Bio import SeqIO

record_dict = SeqIO.to_dict(SeqIO.parse("example.fasta", "fasta"))
print(record_dict["gi:12345678"])  # use any record ID

The function 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, key_function.

For larger files, it isn’t possible to hold everything in memory, so Bio.SeqIO.to_dict is not suitable. Biopython 1.52 inwards includes the Bio.SeqIO.index function for this situation, but you might also consider BioSQL.

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.read(), which like 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 function next:

from Bio import SeqIO

first_record = next(SeqIO.parse("example.fasta", "fasta"))

Sequence Output

For writing records to a file use the function Bio.SeqIO.write(), 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 SeqRecord 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 Clustal, the 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.

File Format Conversion

Suppose you have a GenBank file which you want to turn into a Fasta file. For example, lets consider the file cor6_6.gb which is included in the Biopython unit tests under the GenBank directory.

You could read the file like this, using the Bio.SeqIO.parse() function:

from Bio import SeqIO

with open("cor6_6.gb") as input_handle:
    for record in SeqIO.parse(input_handle, "genbank"):

Notice that this file contains six records. Now instead of printing the records, let’s pass the SeqRecord iterator to the Bio.SeqIO.write() 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.
VERSION     X55053.1  GI:16229

The resulting Fasta file looks like this:

>X55053.1 A.thaliana cor6.6 mRNA.

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.


Input/Output Example - Filtering by sequence length

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

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.

Using the SEGUID checksum

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 file ls_orchid.gbk:

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:


Now lets use the checksum function and Bio.SeqIO.to_dict() to build a 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 the 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"]

Giving this output:

P.barbatum 5.8S rRNA gene and ITS1 and ITS2 DNA.

Random subsequences

This script will read a Genbank file with a whole mitochondrial genome (e.g. the tobacco mitochondrion, Nicotiana tabacum mitochondrion NC_006581), 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), "", "")

SeqIO.write(mito_frags, "mitofrags.fasta", "fasta")

That should give something like this as the output file,


Writing to a string

Sometimes you won’t want to write your SeqRecord 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 Tutorial (PDF) has an example of this in the SeqIO chapter.

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")

The format method will take any output format supported by Bio.SeqIO where the file format can be used for a single record (e.g. "fasta", "tab" or "genbank").

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.