Work on the Seq compiler is being continued in Codon, a general, extensible, high-performance Python compiler. Seq's bioinformatics libraries, features and optimizations are still available and being maintained as a plugin for Codon.
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 many Python programs should work with few if any modifications:
def fib(n):
a, b = 0, 1
while a < n:
print(a, end=' ')
a, b = b, a+b
print()
fib(1000)This prime counting example showcases Seq's OpenMP support, enabled with the addition of one line. The @par annotation tells the compiler to parallelize the following for-loop, in this case using a dynamic schedule, chunk size of 100, and 16 threads.
from sys import argv
def is_prime(n):
factors = 0
for i in range(2, n):
if n % i == 0:
factors += 1
return factors == 0
limit = int(argv[1])
total = 0
@par(schedule='dynamic', chunk_size=100, num_threads=16)
for i in range(2, limit):
if is_prime(i):
total += 1
print(total)Here is an example showcasing some of Seq's bioinformatics features, which include native sequence and k-mer types.
from bio import *
s = s'ACGTACGT' # sequence literal
print(s[2:5]) # subsequence
print(~s) # reverse complement
kmer = Kmer[8](s) # convert to k-mer
# 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(step=1, k=2):
print(~kmer) # '~' also works on k-mersPre-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 https://seq-lang.org/install.sh)"See Building from Source.
Please check docs.seq-lang.org for in-depth documentation.
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: https://doi.org/10.1145/3360551
BibTeX:
@article{Shajii:2019:SHL:3366395.3360551,
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 = {http://doi.acm.org/10.1145/3360551},
doi = {10.1145/3360551},
acmid = {3360551},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {Python, bioinformatics, computational biology, domain-specific language, optimization, programming language},
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent