PyCDHIT4 
Cython bindings and Python interface to CD-HIT, an accelerated sequence clustering program..
🗺️ Overview
CD-HIT is a fast method published in 2006 by Weizhong Li[1] for clustering biological sequences. Its clustering approach uses a greedy clustering algorithm and common word counting to approximate sequence identity. Extensive parallelization later implemented by Limin Fu[2] allow for a quasi-linear speed-up for up to 8 CPU cores, a typical figure for consumer hardware.
PyCDHIT4 is a Python module that provides bindings to CD-HIT v4.8
using Cython. It implements a user-friendly, Pythonic
interface to cluster biological sequences using different parameters and access
results directly. It interacts with the CD-HIT library interface, which has
the following advantages:
- single dependency: PyCDHIT4 is distributed as a Python package, so you can add it as a dependency to your project, and stop worrying about the CD-HIT binaries being present on the end-user machine.
- no intermediate files: Everything happens in memory, through Python objects you control, so you don't have to invoke the CD-HIT CLI using a sub-process and temporary files.
- custom alignment parameters: The pairwise alignment can receive custom
parameters rather than the default CD-HIT ones, including custom scoring
matrices from the
scoring-matriceslibrary.
📋 Features
PyCDHIT4 has the following roadmap to support the same features as the CD-HIT command line:
- CD-HIT with default parameters.
- Sequence identity threshold parameter (
-c). - Local sequence identity mode (
-G 0, additional parameters-aL,-AL,-aS,-AS). - Extended parameter support.
- CD-HIT-EST.
- CD-HIT-2D and CD-HIT-2D-EST.
- CD-HIT-DIV
- CD-HIT-454
🔧 Installing
PyCDHIT4 can be installed directly from PyPI, which hosts some pre-built wheels for common architectures & operating systems, as well as the code required to compile from source with Cython:
$ pip install pycdhit4
💡 Example
Let's create some sequences in memory, then cluster them with CD-HIT and default parameter, and finally print result:
import pycdhit4
sequences = [
"MKALIVLGLVLLSVTVQGKVFERCELARTLKRLGMDGYRGISLANWMCLAKWESGYNTRATNYNAGDRSTDYGIFQINSRYWCNDGKTPGAVNACHLSCSALLQDNIADAVACAKRVVRDPQGIRAWVAWRNRCQNRDVRQYVQGCGV",
"MKALIVLGLVLLSVMVQGKVFERCELARTLKRLGMDGYRGISLANWMCLAKWESGYNTRATNYNAGDRSTDYGIFQINSRYWCNDGKTPGAVNACHLSCSALLQDNIADAVACAKRVVRDPQGIRAWVAWRNRCQNRDVRQYVQGCGV",
"MMRFVPLFLACISLPAFQATEFTKCEVSHAIEDMDGYQGISLLEWTCVLFHTSGYDSQAIVKNNGSTEYGLFQISNRNWCKSSEFPESENICDISCDKFLDDELADDIVCAKKIVAIKGIDYWKAHKPMCSEKLEQWRCEKPGAPALVVPALNSETPVP",
"MMSFFPLLLVGILFPAVQAKQLTKCALSHELNDLAGYRDITLPEWLCIIFHISGYDTQAIVKNSDHKEYGLFQINDKDFCESSTTVQSRNICDISCDKLLDDDLTDDIMCVKKILDIKGIDYWLAHKPLCSDKLEQWYCEAQ",
]
clustering = pycdhit4.cluster(sequences)
print("clusters: ", list(clustering.clusters))
print("representatives: ", list(clustering.representatives))
This should output the following:
clusters: [0, 0, 1, 2]
representatives: [0, 2, 3]
i.e. the first two sequences belong to the same cluster (with representative being sequence #0)
while the two remaining ones form a singleton each. The format is similar to
flat clusters returned by e.g. scipy.cluster.hierarchy.fcluster
🔎 See Also
Using CD-HIT to cluster protein families? Then perhaps for the next steps you
may be interested in aligning them, for instance with
pyfamsa, a Python package wrapping
the FAMSA 2 aligner.
Then you may want to build a profile HMM from the alignment, for instance
with pyhmmer, a Python package
which wraps HMMER.
💭 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 GNU General Public License v3.0.
CD-HIT is developed by Weizhong Li's lab
and is distributed under the terms of the GNU General Public License v2.0.
See vendor/cdhit/license.txt for more information.
This project is in no way not affiliated, sponsored, or otherwise endorsed by the CD-HIT authors. It was developed by Martin Larralde during his post-doctoral project at the Idiap Research Institute in the Human-Centered Health AI group
📚 References
- [1] Li, W., & Godzik, A. (2006). Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics (Oxford, England), 22(13), 1658–1659. doi:10.1093/bioinformatics/btl158. Epub 2006 May 26. PMID:16731699.
- [2] Fu, L., Niu, B., Zhu, Z., Wu, S., & Li, W. (2012). CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics (Oxford, England), 28(23), 3150–3152. doi:10.1093/bioinformatics/bts565 PMID:23060610
1 maintainer
Metadata
1 maintainer
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