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A high-performance, Pythonic language for bioinformatics

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Seq — a language for bioinformatics

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A strongly-typed and statically-compiled high-performance Pythonic language!

Seq is a programming language for computational genomics and bioinformatics. With a Python-compatible syntax and a host of domain-specific features and optimizations, Seq makes writing high-performance genomics software as easy as writing Python code, and achieves performance comparable to (and in many cases better than) C/C++.

Think of Seq as a strongly-typed and statically-compiled Python: all the bells and whistles of Python, boosted with a strong type system, without any performance overhead.

Seq is able to outperform Python code by up to 160x. Seq can further beat equivalent C/C++ code by up to 2x without any manual interventions, and also natively supports parallelism out of the box. Implementation details and benchmarks are discussed in our paper.

Learn more by following the tutorial or from the cookbook.


Seq is a Python-compatible language, and the vast majority of Python programs should work without any modifications:

def check_prime(n):
    if n > 1:
        for i in range(2, n):
            if n % i == 0:
                return False
        return True
        return False

n = 1009 print n, 'is', 'a' if check_prime(n) else 'not a', 'prime'

Here is an example showcasing Seq's bioinformatics features:

s = s'ACGTACGT'    # sequence literal
print s[2:5]       # subsequence
print ~s           # reverse complement
kmer = Kmer[8](s)  # convert to k-mer
type K2 = Kmer[2]  # type definition

iterate over length-3 subsequences

with step 2

for sub in s.split(3, step=2): print sub[-1] # last base

# iterate over 2-mers with step 1
for kmer in sub.kmers[K2](step=1):
    print ~kmer  # '~' also works on k-mers

Seq provides native sequence and k-mer types, e.g. a 8-mer is represented by

as above.

Here is a more complex example that counts occurrences of subsequences from a FASTQ file (

) in sequences obtained from a FASTA file (
) using an FM-index:
from sys import argv
from bio.fmindex import FMIndex
fmi = FMIndex(argv[1])
k, step, n = 20, 20, 0

def add(count: int): global n n += count

@prefetch def search(s: seq, fmi: FMIndex): intv = fmi.interval(s[-1]) s = s[:-1] # trim last base while s and intv: # backwards-extend intv intv = fmi[intv, s[-1]] s = s[:-1] # trim last # return count of occurrences return len(intv)

FASTQ(argv[2]) |> seqs |> split(k, step) |> search(fmi) |> add print 'total:', n


annotation tells the compiler to perform a coroutine-based pipeline transformation to make the FM-index queries faster, by overlapping the cache miss latency from one query with other useful work. In practice, the single
line can provide a 2x performance improvement.


Pre-built binaries

Pre-built binaries for Linux and macOS on x86_64 are available alongside each release. We also have a script for downloading and installing pre-built versions:

/bin/bash -c "$(curl -fsSL"

Build from source

See Building from Source.


Please check for in-depth documentation.

Citing Seq

If you use Seq in your research, please cite:

Ariya Shajii, Ibrahim Numanagić, Riyadh Baghdadi, Bonnie Berger, and Saman Amarasinghe. 2019. Seq: a high-performance language for bioinformatics. Proc. ACM Program. Lang. 3, OOPSLA, Article 125 (October 2019), 29 pages. DOI:


 author = {Shajii, Ariya and Numanagi\'{c}, Ibrahim and Baghdadi, Riyadh and Berger, Bonnie and Amarasinghe, Saman},
 title = {Seq: A High-performance Language for Bioinformatics},
 journal = {Proc. ACM Program. Lang.},
 issue_date = {October 2019},
 volume = {3},
 number = {OOPSLA},
 month = oct,
 year = {2019},
 issn = {2475-1421},
 pages = {125:1--125:29},
 articleno = {125},
 numpages = {29},
 url = {},
 doi = {10.1145/3360551},
 acmid = {3360551},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {Python, bioinformatics, computational biology, domain-specific language, optimization, programming language},

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