pyopal 0.6.1

Description:

pyopal 0.6.1

🐍🌈🪨 PyOpal
Cython bindings and Python interface to Opal, a SIMD-accelerated database search aligner.















🗺️ Overview
Opal is a sequence aligner enabling fast
sequence similarity search using either of the Smith-Waterman, semi-global or
Needleman-Wunsch algorithms. It is used part of the SW#db method[1]
to align a query sequence to multiple database sequences on CPU, using
the multi-sequence vectorization method described in SWIPE[2]
PyOpal is a Python module that provides bindings to Opal
using Cython. It implements a user-friendly, Pythonic
interface to query a database of sequences and access the search results. It
interacts with the Opal interface rather than with the CLI, which has the
following advantages:

no binary dependency: PyOpal is distributed as a Python package, so
you can add it as a dependency to your project, and stop worrying about the
Opal binary being present on the end-user machine.
no intermediate files: Everything happens in memory, in a Python object
you control, so you don't have to invoke the Opal CLI using a sub-process
and temporary files.
better portability: Opal uses SIMD to accelerate alignment scoring, but
doesn't support dynamic dispatch, so it has to be compiled on the local
machine to be able to use the full capabilities of the local CPU. PyOpal
ships several versions of Opal instead, each compiled with different target
features, and selects the best one for the local platform at runtime.
wider platform support: The Opal code has been backported to work on SSE2
rather than SSE4.1, allowing PyOpal to run on older x86 CPUs (all x86 CPUs
support it since 2003). In addition, Armv7 and Aarch64 CPUs are also
supported if they implement NEON extensions. Finally, the C++ code of Opal
has been modified to compile on Windows.

🔧 Installing
PyOpal is available for all modern versions (3.6+), optionally depending on
the lightweight Python package archspec
for runtime CPU feature detection.
It can be installed directly from PyPI,
which hosts some pre-built x86-64 wheels for Linux, MacOS, and Windows,
Aarch64 wheels for Linux and MacOS, as well as the code required to
compile from source with Cython:
$ pip install pyopal

Otherwise, PyOpal is also available as a Bioconda
package:
$ conda install -c bioconda pyopal

Check the install page
of the documentation for other ways to install PyOpal on your machine.
💡 Example
All classes are imported in the main namespace pyopal:
import pyopal

pyopal can work with sequences passed as Python strings,
as well as with ASCII strings in bytes objects:
query = "MAGFLKVVQLLAKYGSKAVQWAWANKGKILDWLNAGQAIDWVVSKIKQILGIK"
database = [
"MESILDLQELETSEEESALMAASTVSNNC",
"MKKAVIVENKGCATCSIGAACLVDGPIPDFEIAGATGLFGLWG",
"MAGFLKVVQILAKYGSKAVQWAWANKGKILDWINAGQAIDWVVEKIKQILGIK",
"MTQIKVPTALIASVHGEGQHLFEPMAARCTCTTIISSSSTF",
]

If you plan to reuse the database across several queries, you can store it in
a Database,
which will keep sequences encoded according to
an Alphabet:
database = pyopal.Database(database)

The top-level function pyopal.align can be used to align a query
sequence against a database, using multithreading to process chunks
of the database in parallel:
for result in pyopal.align(query, database):
print(result.score, result.target_index, database[result.target_index])

See the API documentation
for more examples, including how to use the internal API, and detailed
reference of the parameters and result types.
🧶 Thread-safety
Database objects are thread safe through a
C++17 read/write lock
that prevents modification while the database is searched. In addition, the
Aligner.align method is re-entrant and can be safely used to query the
same database in parallel with different queries across different threads:
import multiprocessing.pool
import pyopal
import Bio.SeqIO

queries = [
"MEQQIELDVLEISDLIAGAGENDDLAQVMAASCTTSSVSTSSSSSSS",
"MTQIKVPTALIASVHGEGQHLFEPMAARCTCTTIISSSSTF",
"MGAIAKLVAKFGWPIVKKYYKQIMQFIGEGWAINKIIDWIKKHI",
"MGPVVVFDCMTADFLNDDPNNAELSALEMEELESWGAWDGEATS",
]

database = pyopal.Database([
str(record.seq)
for record in Bio.SeqIO.parse("vendor/opal/test_data/db/uniprot_sprot12071.fasta", "fasta")
])

aligner = pyopal.Aligner()
with multiprocessing.pool.ThreadPool() as pool:
hits = dict(pool.map(lambda q: (q, aligner.align(q, database)), queries))


💭 Feedback
⚠️ Issue Tracker
Found a bug ? Have an enhancement request ? Head over to the GitHub issue tracker
if you need to report or ask something. If you are filing in on a bug,
please include as much information as you can about the issue, and try to
recreate the same bug in a simple, easily reproducible situation.
🏗️ Contributing
Contributions are more than welcome! See
CONTRIBUTING.md
for more details.
📋 Changelog
This project adheres to Semantic Versioning
and provides a changelog
in the Keep a Changelog format.
⚖️ License
This library is provided under the MIT License.
Opal is developed by Martin Šošić and is distributed under the
terms of the MIT License as well. See vendor/opal/LICENSE for more information.
This project is in no way not affiliated, sponsored, or otherwise endorsed
by the Opal authors. It was developed
by Martin Larralde during his PhD project
at the European Molecular Biology Laboratory in
the Zeller team.
📚 References

[1] Korpar Matija, Martin Šošić, Dino Blažeka, Mile Šikić. SW#db: ‘GPU-Accelerated Exact Sequence Similarity Database Search’. PLoS One. 2015 Dec 31;10(12):e0145857. doi:10.1371/journal.pone.0145857. PMID:26719890. PMC4699916.
[2] Rognes Torbjørn. Faster Smith-Waterman database searches with inter-sequence SIMD parallelisation. BMC Bioinformatics. 2011 Jun 1;12:221. doi:10.1186/1471-2105-12-221. PMID:21631914.PMC3120707.