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Description

Samtools provides a function "faidx" (FAsta InDeX), which creates a small flat index file ".fai" allowing for fast random access to any subsequence in the indexed FASTA file, while loading a minimal amount of the file in to memory. This python module implements pure Python classes for indexing, retrieval, and in-place modification of FASTA files using a samtools compatible index. The pyfaidx module is API compatible with the pygr seqdb module. A command-line script "faidx" is installed alongside the pyfaidx module, and facilitates complex manipulation of FASTA files without any programming knowledge.

If you use pyfaidx in your publication, please cite:

Shirley MD, Ma Z, Pedersen B, Wheelan S. Efficient "pythonic" access to FASTA files using pyfaidx. PeerJ PrePrints 3:e1196. 2015.

Installation

This package is tested under Linux and macOS using Python 3.7+, and and is available from the PyPI:

pip install pyfaidx  # add --user if you don't have root

or download a release and:

pip install .

If using pip install --user make sure to add /home/$USER/.local/bin to your $PATH (on linux) or /Users/$USER/Library/Python/{python version}/bin (on macOS) if you want to run the faidx script.

Python 2.6 and 2.7 users may choose to use a package version from v0.7.2 or earier.

Usage

>>> from pyfaidx import Fasta
>>> genes = Fasta('tests/data/genes.fasta')
>>> genes
Fasta("tests/data/genes.fasta")  # set strict_bounds=True for bounds checking

Acts like a dictionary.

>>> genes.keys()
('AB821309.1', 'KF435150.1', 'KF435149.1', 'NR_104216.1', 'NR_104215.1', 'NR_104212.1', 'NM_001282545.1', 'NM_001282543.1', 'NM_000465.3', 'NM_001282549.1', 'NM_001282548.1', 'XM_005249645.1', 'XM_005249644.1', 'XM_005249643.1', 'XM_005249642.1', 'XM_005265508.1', 'XM_005265507.1', 'XR_241081.1', 'XR_241080.1', 'XR_241079.1')

>>> genes['NM_001282543.1'][200:230]
>NM_001282543.1:201-230
CTCGTTCCGCGCCCGCCATGGAACCGGATG

>>> genes['NM_001282543.1'][200:230].seq
'CTCGTTCCGCGCCCGCCATGGAACCGGATG'

>>> genes['NM_001282543.1'][200:230].name
'NM_001282543.1'

# Start attributes are 1-based
>>> genes['NM_001282543.1'][200:230].start
201

# End attributes are 0-based
>>> genes['NM_001282543.1'][200:230].end
230

>>> genes['NM_001282543.1'][200:230].fancy_name
'NM_001282543.1:201-230'

>>> len(genes['NM_001282543.1'])
5466

Note that start and end coordinates of Sequence objects are [1, 0]. This can be changed to [0, 0] by passing one_based_attributes=False to Fasta or Faidx. This argument only affects the Sequence .start/.end attributes, and has no effect on slicing coordinates.

Indexes like a list:

>>> genes[0][:50]
>AB821309.1:1-50
ATGGTCAGCTGGGGTCGTTTCATCTGCCTGGTCGTGGTCACCATGGCAAC

Slices just like a string:

>>> genes['NM_001282543.1'][200:230][:10]
>NM_001282543.1:201-210
CTCGTTCCGC

>>> genes['NM_001282543.1'][200:230][::-1]
>NM_001282543.1:230-201
GTAGGCCAAGGTACCGCCCGCGCCTTGCTC

>>> genes['NM_001282543.1'][200:230][::3]
>NM_001282543.1:201-230
CGCCCCTACA

>>> genes['NM_001282543.1'][:]
>NM_001282543.1:1-5466
CCCCGCCCCT........
  • Slicing start and end coordinates are 0-based, just like Python sequences.

Complements and reverse complements just like DNA

>>> genes['NM_001282543.1'][200:230].complement
>NM_001282543.1 (complement):201-230
GAGCAAGGCGCGGGCGGTACCTTGGCCTAC

>>> genes['NM_001282543.1'][200:230].reverse
>NM_001282543.1:230-201
GTAGGCCAAGGTACCGCCCGCGCCTTGCTC

>>> -genes['NM_001282543.1'][200:230]
>NM_001282543.1 (complement):230-201
CATCCGGTTCCATGGCGGGCGCGGAACGAG

Fasta objects can also be accessed using method calls:

>>> genes.get_seq('NM_001282543.1', 201, 210)
>NM_001282543.1:201-210
CTCGTTCCGC

>>> genes.get_seq('NM_001282543.1', 201, 210, rc=True)
>NM_001282543.1 (complement):210-201
GCGGAACGAG

Spliced sequences can be retrieved from a list of [start, end] coordinates: TODO update this section

# new in v0.5.1
segments = [[1, 10], [50, 70]]
>>> genes.get_spliced_seq('NM_001282543.1', segments)
>gi|543583786|ref|NM_001282543.1|:1-70
CCCCGCCCCTGGTTTCGAGTCGCTGGCCTGC

Custom key functions provide cleaner access:

>>> from pyfaidx import Fasta
>>> genes = Fasta('tests/data/genes.fasta', key_function = lambda x: x.split('.')[0])
>>> genes.keys()
dict_keys(['NR_104212', 'NM_001282543', 'XM_005249644', 'XM_005249645', 'NR_104216', 'XM_005249643', 'NR_104215', 'KF435150', 'AB821309', 'NM_001282549', 'XR_241081', 'KF435149', 'XR_241079', 'NM_000465', 'XM_005265508', 'XR_241080', 'XM_005249642', 'NM_001282545', 'XM_005265507', 'NM_001282548'])
>>> genes['NR_104212'][:10]
>NR_104212:1-10
CCCCGCCCCT

You can specify a character to split names on, which will generate additional entries:

>>> from pyfaidx import Fasta
>>> genes = Fasta('tests/data/genes.fasta', split_char='.', duplicate_action="first") # default duplicate_action="stop"
>>> genes.keys()
dict_keys(['.1', 'NR_104212', 'NM_001282543', 'XM_005249644', 'XM_005249645', 'NR_104216', 'XM_005249643', 'NR_104215', 'KF435150', 'AB821309', 'NM_001282549', 'XR_241081', 'KF435149', 'XR_241079', 'NM_000465', 'XM_005265508', 'XR_241080', 'XM_005249642', 'NM_001282545', 'XM_005265507', 'NM_001282548'])

If your key_function or split_char generates duplicate entries, you can choose what action to take:

# new in v0.4.9
>>> genes = Fasta('tests/data/genes.fasta', split_char="|", duplicate_action="longest")
>>> genes.keys()
dict_keys(['gi', '563317589', 'dbj', 'AB821309.1', '', '557361099', 'gb', 'KF435150.1', '557361097', 'KF435149.1', '543583796', 'ref', 'NR_104216.1', '543583795', 'NR_104215.1', '543583794', 'NR_104212.1', '543583788', 'NM_001282545.1', '543583786', 'NM_001282543.1', '543583785', 'NM_000465.3', '543583740', 'NM_001282549.1', '543583738', 'NM_001282548.1', '530384540', 'XM_005249645.1', '530384538', 'XM_005249644.1', '530384536', 'XM_005249643.1', '530384534', 'XM_005249642.1', '530373237','XM_005265508.1', '530373235', 'XM_005265507.1', '530364726', 'XR_241081.1', '530364725', 'XR_241080.1', '530364724', 'XR_241079.1'])

Filter functions (returning True) limit the index:

# new in v0.3.8
>>> from pyfaidx import Fasta
>>> genes = Fasta('tests/data/genes.fasta', filt_function = lambda x: x[0] == 'N')
>>> genes.keys()
dict_keys(['NR_104212', 'NM_001282543', 'NR_104216', 'NR_104215', 'NM_001282549', 'NM_000465', 'NM_001282545', 'NM_001282548'])
>>> genes['XM_005249644']
KeyError: XM_005249644 not in tests/data/genes.fasta.

Or just get a Python string:

>>> from pyfaidx import Fasta
>>> genes = Fasta('tests/data/genes.fasta', as_raw=True)
>>> genes
Fasta("tests/data/genes.fasta", as_raw=True)

>>> genes['NM_001282543.1'][200:230]
CTCGTTCCGCGCCCGCCATGGAACCGGATG

You can make sure that you always receive an uppercase sequence, even if your fasta file has lower case

>>> from pyfaidx import Fasta
>>> reference = Fasta('tests/data/genes.fasta.lower', sequence_always_upper=True)
>>> reference['gi|557361099|gb|KF435150.1|'][1:70]

>gi|557361099|gb|KF435150.1|:2-70
TGACATCATTTTCCACCTCTGCTCAGTGTTCAACATCTGACAGTGCTTGCAGGATCTCTCCTGGACAAA

You can also perform line-based iteration, receiving the sequence lines as they appear in the FASTA file:

>>> from pyfaidx import Fasta
>>> genes = Fasta('tests/data/genes.fasta')
>>> for line in genes['NM_001282543.1']:
...   print(line)
CCCCGCCCCTCTGGCGGCCCGCCGTCCCAGACGCGGGAAGAGCTTGGCCGGTTTCGAGTCGCTGGCCTGC
AGCTTCCCTGTGGTTTCCCGAGGCTTCCTTGCTTCCCGCTCTGCGAGGAGCCTTTCATCCGAAGGCGGGA
CGATGCCGGATAATCGGCAGCCGAGGAACCGGCAGCCGAGGATCCGCTCCGGGAACGAGCCTCGTTCCGC
...

Sequence names are truncated on any whitespace. This is a limitation of the indexing strategy. However, full names can be recovered:

# new in v0.3.7
>>> from pyfaidx import Fasta
>>> genes = Fasta('tests/data/genes.fasta')
>>> for record in genes:
...   print(record.name)
...   print(record.long_name)
...
gi|563317589|dbj|AB821309.1|
gi|563317589|dbj|AB821309.1| Homo sapiens FGFR2-AHCYL1 mRNA for FGFR2-AHCYL1 fusion kinase protein, complete cds
gi|557361099|gb|KF435150.1|
gi|557361099|gb|KF435150.1| Homo sapiens MDM4 protein variant Y (MDM4) mRNA, complete cds, alternatively spliced
gi|557361097|gb|KF435149.1|
gi|557361097|gb|KF435149.1| Homo sapiens MDM4 protein variant G (MDM4) mRNA, complete cds
...

# new in v0.4.9
>>> from pyfaidx import Fasta
>>> genes = Fasta('tests/data/genes.fasta', read_long_names=True)
>>> for record in genes:
...   print(record.name)
...
gi|563317589|dbj|AB821309.1| Homo sapiens FGFR2-AHCYL1 mRNA for FGFR2-AHCYL1 fusion kinase protein, complete cds
gi|557361099|gb|KF435150.1| Homo sapiens MDM4 protein variant Y (MDM4) mRNA, complete cds, alternatively spliced
gi|557361097|gb|KF435149.1| Homo sapiens MDM4 protein variant G (MDM4) mRNA, complete cds

Records can be accessed efficiently as numpy arrays:

# new in v0.5.4
>>> from pyfaidx import Fasta
>>> import numpy as np
>>> genes = Fasta('tests/data/genes.fasta')
>>> np.asarray(genes['NM_001282543.1'])
array(['C', 'C', 'C', ..., 'A', 'A', 'A'], dtype='|S1')

Sequence can be buffered in memory using a read-ahead buffer for fast sequential access:

>>> from timeit import timeit
>>> fetch = "genes['NM_001282543.1'][200:230]"
>>> read_ahead = "import pyfaidx; genes = pyfaidx.Fasta('tests/data/genes.fasta', read_ahead=10000)"
>>> no_read_ahead = "import pyfaidx; genes = pyfaidx.Fasta('tests/data/genes.fasta')"
>>> string_slicing = "genes = {}; genes['NM_001282543.1'] = 'N'*10000"

>>> timeit(fetch, no_read_ahead, number=10000)
0.2204863309962093
>>> timeit(fetch, read_ahead, number=10000)
0.1121859749982832
>>> timeit(fetch, string_slicing, number=10000)
0.0033553699977346696

Read-ahead buffering can reduce runtime by 1/2 for sequential accesses to buffered regions.

If you want to modify the contents of your FASTA file in-place, you can use the mutable argument. Any portion of the FastaRecord can be replaced with an equivalent-length string. Warning: This will change the contents of your file immediately and permanently:

>>> genes = Fasta('tests/data/genes.fasta', mutable=True)
>>> type(genes['NM_001282543.1'])
<class 'pyfaidx.MutableFastaRecord'>

>>> genes['NM_001282543.1'][:10]
>NM_001282543.1:1-10
CCCCGCCCCT
>>> genes['NM_001282543.1'][:10] = 'NNNNNNNNNN'
>>> genes['NM_001282543.1'][:15]
>NM_001282543.1:1-15
NNNNNNNNNNCTGGC

The FastaVariant class provides a way to integrate single nucleotide variant calls to generate a consensus sequence.

# new in v0.4.0
>>> consensus = FastaVariant('tests/data/chr22.fasta', 'tests/data/chr22.vcf.gz', het=True, hom=True)
RuntimeWarning: Using sample NA06984 genotypes.

>>> consensus['22'].variant_sites
(16042793, 21833121, 29153196, 29187373, 29187448, 29194610, 29821295, 29821332, 29993842, 32330460, 32352284)

>>> consensus['22'][16042790:16042800]
>22:16042791-16042800
TCGTAGGACA

>>> Fasta('tests/data/chr22.fasta')['22'][16042790:16042800]
>22:16042791-16042800
TCATAGGACA

>>> consensus = FastaVariant('tests/data/chr22.fasta', 'tests/data/chr22.vcf.gz', sample='NA06984', het=True, hom=True, call_filter='GT == "0/1"')
>>> consensus['22'].variant_sites
(16042793, 29187373, 29187448, 29194610, 29821332)

You can also specify paths using pathlib.Path objects.

#new in v0.7.1
>>> from pyfaidx import Fasta
>>> from pathlib import Path
>>> genes = Fasta(Path('tests/data/genes.fasta'))
>>> genes
Fasta("tests/data/genes.fasta")

Accessing fasta files from filesystem_spec filesystems:

# new in v0.7.0
# pip install fsspec s3fs
>>> import fsspec
>>> from pyfaidx import Fasta
>>> of = fsspec.open("s3://broad-references/hg19/v0/Homo_sapiens_assembly19.fasta", anon=True)
>>> genes = Fasta(of)

It also provides a command-line script:

cli script: faidx

Fetch sequences from FASTA. If no regions are specified, all entries in the
input file are returned. Input FASTA file must be consistently line-wrapped,
and line wrapping of output is based on input line lengths.

positional arguments:
  fasta                 FASTA file
  regions               space separated regions of sequence to fetch e.g.
                        chr1:1-1000

optional arguments:
  -h, --help            show this help message and exit
  -b BED, --bed BED     bed file of regions (zero-based start coordinate)
  -o OUT, --out OUT     output file name (default: stdout)
  -i {bed,chromsizes,nucleotide,transposed}, --transform {bed,chromsizes,nucleotide,transposed} transform the requested regions into another format. default: None
  -c, --complement      complement the sequence. default: False
  -r, --reverse         reverse the sequence. default: False
  -a SIZE_RANGE, --size-range SIZE_RANGE
                        selected sequences are in the size range [low, high]. example: 1,1000 default: None
  -n, --no-names        omit sequence names from output. default: False
  -f, --full-names      output full names including description. default: False
  -x, --split-files     write each region to a separate file (names are derived from regions)
  -l, --lazy            fill in --default-seq for missing ranges. default: False
  -s DEFAULT_SEQ, --default-seq DEFAULT_SEQ
                        default base for missing positions and masking. default: None
  -d DELIMITER, --delimiter DELIMITER
                        delimiter for splitting names to multiple values (duplicate names will be discarded). default: None
  -e HEADER_FUNCTION, --header-function HEADER_FUNCTION
                        python function to modify header lines e.g: "lambda x: x.split("|")[0]". default: lambda x: x.split()[0]
  -u {stop,first,last,longest,shortest}, --duplicates-action {stop,first,last,longest,shortest}
                        entry to take when duplicate sequence names are encountered. default: stop
  -g REGEX, --regex REGEX
                        selected sequences are those matching regular expression. default: .*
  -v, --invert-match    selected sequences are those not matching 'regions' argument. default: False
  -m, --mask-with-default-seq
                        mask the FASTA file using --default-seq default: False
  -M, --mask-by-case    mask the FASTA file by changing to lowercase. default: False
  -e HEADER_FUNCTION, --header-function HEADER_FUNCTION
                        python function to modify header lines e.g: "lambda x: x.split("|")[0]". default: None
  --no-rebuild          do not rebuild the .fai index even if it is out of date. default: False
  --version             print pyfaidx version number

Examples:

$ faidx -v tests/data/genes.fasta
### Creates an .fai index, but supresses sequence output using --invert-match ###

$ faidx tests/data/genes.fasta NM_001282543.1:201-210 NM_001282543.1:300-320
>NM_001282543.1:201-210
CTCGTTCCGC
>NM_001282543.1:300-320
GTAATTGTGTAAGTGACTGCA

$ faidx --full-names tests/data/genes.fasta NM_001282543.1:201-210
>NM_001282543.1| Homo sapiens BRCA1 associated RING domain 1 (BARD1), transcript variant 2, mRNA
CTCGTTCCGC

$ faidx --no-names tests/data/genes.fasta NM_001282543.1:201-210 NM_001282543.1:300-320
CTCGTTCCGC
GTAATTGTGTAAGTGACTGCA

$ faidx --complement tests/data/genes.fasta NM_001282543.1:201-210
>NM_001282543.1:201-210 (complement)
GAGCAAGGCG

$ faidx --reverse tests/data/genes.fasta NM_001282543.1:201-210
>NM_001282543.1:210-201
CGCCTTGCTC

$ faidx --reverse --complement tests/data/genes.fasta NM_001282543.1:201-210
>NM_001282543.1:210-201 (complement)
GCGGAACGAG

$ faidx tests/data/genes.fasta NM_001282543.1
>NM_001282543.1:1-5466
CCCCGCCCCT........
..................
..................
..................

$ faidx --regex "^NM_00128254[35]" genes.fasta
>NM_001282543.1
..................
..................
..................
>NM_001282545.1
..................
..................
..................

$ faidx --lazy tests/data/genes.fasta NM_001282543.1:5460-5480
>NM_001282543.1:5460-5480
AAAAAAANNNNNNNNNNNNNN

$ faidx --lazy --default-seq='Q' tests/data/genes.fasta NM_001282543.1:5460-5480
>NM_001282543.1:5460-5480
AAAAAAAQQQQQQQQQQQQQQ

$ faidx tests/data/genes.fasta --bed regions.bed
...

$ faidx --transform chromsizes tests/data/genes.fasta
AB821309.1  3510
KF435150.1  481
KF435149.1  642
NR_104216.1 4573
NR_104215.1 5317
NR_104212.1 5374
...

$ faidx --transform bed tests/data/genes.fasta
AB821309.1  1    3510
KF435150.1  1    481
KF435149.1  1    642
NR_104216.1 1   4573
NR_104215.1 1   5317
NR_104212.1 1   5374
...

$ faidx --transform nucleotide tests/data/genes.fasta
name        start   end     A       T       C       G       N
AB821309.1  1       3510    955     774     837     944     0
KF435150.1  1       481     149     120     103     109     0
KF435149.1  1       642     201     163     129     149     0
NR_104216.1 1       4573    1294    1552    828     899     0
NR_104215.1 1       5317    1567    1738    968     1044    0
NR_104212.1 1       5374    1581    1756    977     1060    0
...

faidx --transform transposed tests/data/genes.fasta
AB821309.1  1       3510    ATGGTCAGCTGGGGTCGTTTCATC...
KF435150.1  1       481     ATGACATCATTTTCCACCTCTGCT...
KF435149.1  1       642     ATGACATCATTTTCCACCTCTGCT...
NR_104216.1 1       4573    CCCCGCCCCTCTGGCGGCCCGCCG...
NR_104215.1 1       5317    CCCCGCCCCTCTGGCGGCCCGCCG...
NR_104212.1 1       5374    CCCCGCCCCTCTGGCGGCCCGCCG...
...

$ faidx --split-files tests/data/genes.fasta
$ ls
AB821309.1.fasta    NM_001282549.1.fasta    XM_005249645.1.fasta
KF435149.1.fasta    NR_104212.1.fasta       XM_005265507.1.fasta
KF435150.1.fasta    NR_104215.1.fasta       XM_005265508.1.fasta
NM_000465.3.fasta   NR_104216.1.fasta       XR_241079.1.fasta
NM_001282543.1.fasta        XM_005249642.1.fasta    XR_241080.1.fasta
NM_001282545.1.fasta        XM_005249643.1.fasta    XR_241081.1.fasta
NM_001282548.1.fasta        XM_005249644.1.fasta

$ faidx --delimiter='_' tests/data/genes.fasta 000465.3
>000465.3
CCCCGCCCCTCTGGCGGCCCGCCGTCCCAGACGCGGGAAGAGCTTGGCCGGTTTCGAGTCGCTGGCCTGC
AGCTTCCCTGTGGTTTCCCGAGGCTTCCTTGCTTCCCGCTCTGCGAGGAGCCTTTCATCCGAAGGCGGGA
.......

$ faidx --size-range 5500,6000 -i chromsizes tests/data/genes.fasta
NM_000465.3 5523

$ faidx -m --bed regions.bed tests/data/genes.fasta
### Modifies tests/data/genes.fasta by masking regions using --default-seq character ###

$ faidx -M --bed regions.bed tests/data/genes.fasta
### Modifies tests/data/genes.fasta by masking regions using lowercase characters ###

$ faidx -e "lambda x: x.split('.')[0]" tests/data/genes.fasta -i bed
AB821309    1       3510
KF435150    1       481
KF435149    1       642
NR_104216   1       4573
NR_104215   1       5317
.......

Similar syntax as samtools faidx

A lower-level Faidx class is also available:

>>> from pyfaidx import Faidx
>>> fa = Faidx('genes.fa')  # can return str with as_raw=True
>>> fa.index
OrderedDict([('AB821309.1', IndexRecord(rlen=3510, offset=12, lenc=70, lenb=71)), ('KF435150.1', IndexRecord(rlen=481, offset=3585, lenc=70, lenb=71)),... ])

>>> fa.index['AB821309.1'].rlen
3510

fa.fetch('AB821309.1', 1, 10)  # these are 1-based genomic coordinates
>AB821309.1:1-10
ATGGTCAGCT
  • If the FASTA file is not indexed, when Faidx is initialized the build_index method will automatically run, and the index will be written to "filename.fa.fai" with write_fai(). where "filename.fa" is the original FASTA file.
  • Start and end coordinates are 1-based.

Support for compressed FASTA

pyfaidx can create and read .fai indices for FASTA files that have been compressed using the bgzip tool from samtools. bgzip writes compressed data in a BGZF format. BGZF is gzip compatible, consisting of multiple concatenated gzip blocks, each with an additional gzip header making it possible to build an index for rapid random access. I.e., files compressed with bgzip are valid gzip and so can be read by gunzip. See this description for more details on bgzip.

Changelog

Please see the releases for a comprehensive list of version changes.

Known issues

I try to fix as many bugs as possible, but most of this work is supported by a single developer. Please check the known issues for bugs relevant to your work. Pull requests are welcome.

Contributing

Create a new Pull Request with one feature. If you add a new feature, please create also the relevant test.

To get test running on your machine:

  • Create a new virtualenv and install the dev-requirements.txt.

    pip install -r dev-requirements.txt

  • Download the test data running:

    python tests/data/download_gene_fasta.py

  • Run the tests with

    pytests

Acknowledgements

This project is freely licensed by the author, Matthew Shirley, and was completed under the mentorship and financial support of Drs. Sarah Wheelan and Vasan Yegnasubramanian at the Sidney Kimmel Comprehensive Cancer Center in the Department of Oncology.