nf-chai

POC Nextflow pipeline to run the Chai-1, SOTA model for biomolecular structure prediction

https://github.com/seqeralabs/nf-chai

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
  • DOI references
    Found 4 DOI reference(s) in README
  • Academic publication links
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (11.9%) to scientific vocabulary

Keywords

nextflow pipeline protein-structure structure-prediction

Scientific Fields

Chemistry Physical Sciences - 40% confidence
Biology Life Sciences - 40% confidence
Engineering Computer Science - 40% confidence
Last synced: 4 months ago · JSON representation ·

Repository

POC Nextflow pipeline to run the Chai-1, SOTA model for biomolecular structure prediction

Basic Info
  • Host: GitHub
  • Owner: seqeralabs
  • License: mpl-2.0
  • Language: Nextflow
  • Default Branch: main
  • Homepage:
  • Size: 12.6 MB
Statistics
  • Stars: 11
  • Watchers: 5
  • Forks: 5
  • Open Issues: 2
  • Releases: 0
Topics
nextflow pipeline protein-structure structure-prediction
Created about 1 year ago · Last pushed 12 months ago
Metadata Files
Readme Changelog Contributing License Citation

README.md

nf-chai

GitHub Actions CI Status GitHub Actions Linting Status nf-test

Nextflow run with docker run with singularity Launch on Seqera Platform

POC implementation of Chai-1 in Nextflow

Introduction

nf-chai is a simple, proof-of-concept bioinformatics pipeline for running the Chai-1 protein prediction algorithm on an input set of protein sequences in FASTA format. The pipeline has been written in Nextflow to generate results for downstream analysis in a reproducible, scalable and portable way.

Usage

[!NOTE] If you are new to Nextflow, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data.

First, prepare a FASTA file with entities supported by Chai-1 in the format highlighted below. You can also provide a directory with multiple FASTA files by specifying the --input parameter with a directory path like this: --input "/path/to/fasta_files/*.fasta|*.fa".

multiple_entities.fa:

```txt

protein|name=example-of-long-protein AGSHSMRYFSTSVSRPGRGEPRFIAVGYVDDTQFVRFDSDAASPRGEPRAPWVEQEGPEYWDRETQKYKRQAQTDRVSLRNLRGYYNQSEAGSHTLQWMFGCDLGPDGRLLRGYDQSAYDGKDYIALNEDLRSWTAADTAAQITQRKWEAAREAEQRRAYLEGTCVEWLRRYLENGKETLQRAEHPKTHVTHHPVSDHEATLRCWALGFYPAEITLTWQWDGEDQTQDTELVETRPAGDGTFQKWAAVVVPSGEEQRYTCHVQHEGLPEPLTLRWEP protein|name=example-of-short-protein AIQRTPKIQVYSRHPAENGKSNFLNCYVSGFHPSDIEVDLLKNGERIEKVEHSDLSFSKDWSFYLLYYTEFTPTEKDEYACRVNHVTLSQPKIVKWDRDM protein|name=example-peptide GAAL ligand|name=example-ligand-as-smiles CCCCCCCCCCCCCC(=O)O ```

Run the pipeline using CPUs with the command below:

bash nextflow run seqeralabs/nf-chai \ --input multiple_entities.fa \ --outdir <OUTDIR> \ -profile <docker/singularity>

Run the pipeline using GPUs with the command below:

bash nextflow run seqeralabs/nf-chai \ --input multiple_entities.fa \ --outdir <OUTDIR> \ --use_gpus \ -profile <docker/singularity>

Set the --weights_dir parameter to a location with the pre-downloaded weights required by Chai-1 to avoid having to download them every time you run the pipeline.

To further improve prediction performance using pre-built multiple sequence alignments (MSA) with evolutionary information, set the --msa_dir parameter to a location with *.aligned.pqt format as required by Chai-1.

Credits

nf-chai was originally written by the Seqera Team.

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

Citations

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

This pipeline uses code and infrastructure developed and maintained by the nf-core community, reused here under the MIT license.

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.

Owner

  • Name: Seqera
  • Login: seqeralabs
  • Kind: organization
  • Email: info@seqera.io
  • Location: Spain

Powering the next generation of big data analysis applications

Citation (CITATIONS.md)

# seqeralabs/nf-chai: Citations

## [nf-core](https://pubmed.ncbi.nlm.nih.gov/32055031/)

> Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Mar;38(3):276-278. doi: 10.1038/s41587-020-0439-x. PubMed PMID: 32055031.

## [Nextflow](https://pubmed.ncbi.nlm.nih.gov/28398311/)

> Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017 Apr 11;35(4):316-319. doi: 10.1038/nbt.3820. PubMed PMID: 28398311.

## Pipeline tools

- [Chai-I](https://www.biorxiv.org/content/10.1101/2024.10.10.615955v1)

  > Chai Discovery team, Boitreaud J, Dent J, McPartlon M, Meier J, Reis V, Rogozhonikov A, Wu K. Chai-1: Decoding the molecular interactions of life. bioRxiv. doi: 10.1101/2024.10.10.615955.

## Software packaging/containerisation tools

- [Anaconda](https://anaconda.com)

  > Anaconda Software Distribution. Computer software. Vers. 2-2.4.0. Anaconda, Nov. 2016. Web.

- [Bioconda](https://pubmed.ncbi.nlm.nih.gov/29967506/)

  > Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J; Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods. 2018 Jul;15(7):475-476. doi: 10.1038/s41592-018-0046-7. PubMed PMID: 29967506.

- [BioContainers](https://pubmed.ncbi.nlm.nih.gov/28379341/)

  > da Veiga Leprevost F, Grüning B, Aflitos SA, Röst HL, Uszkoreit J, Barsnes H, Vaudel M, Moreno P, Gatto L, Weber J, Bai M, Jimenez RC, Sachsenberg T, Pfeuffer J, Alvarez RV, Griss J, Nesvizhskii AI, Perez-Riverol Y. BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics. 2017 Aug 15;33(16):2580-2582. doi: 10.1093/bioinformatics/btx192. PubMed PMID: 28379341; PubMed Central PMCID: PMC5870671.

- [Docker](https://dl.acm.org/doi/10.5555/2600239.2600241)

  > Merkel, D. (2014). Docker: lightweight linux containers for consistent development and deployment. Linux Journal, 2014(239), 2. doi: 10.5555/2600239.2600241.

- [Singularity](https://pubmed.ncbi.nlm.nih.gov/28494014/)

  > Kurtzer GM, Sochat V, Bauer MW. Singularity: Scientific containers for mobility of compute. PLoS One. 2017 May 11;12(5):e0177459. doi: 10.1371/journal.pone.0177459. eCollection 2017. PubMed PMID: 28494014; PubMed Central PMCID: PMC5426675.

GitHub Events

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Last Year
  • Create event: 15
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  • Watch event: 13
  • Delete event: 9
  • Issue comment event: 4
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  • Push event: 51
  • Pull request review comment event: 36
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  • Pull request event: 29
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Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 0
  • Total pull requests: 2
  • Average time to close issues: N/A
  • Average time to close pull requests: about 12 hours
  • Total issue authors: 0
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  • Average comments per issue: 0
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Past Year
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  • Pull requests: 2
  • Average time to close issues: N/A
  • Average time to close pull requests: about 12 hours
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 2
  • Bot issues: 0
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Top Authors
Issue Authors
  • mohmhm1 (1)
  • drpatelh (1)
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  • drpatelh (11)
  • FloWuenne (3)
  • adamrtalbot (2)
Top Labels
Issue Labels
enhancement (1) bug (1)
Pull Request Labels

Dependencies

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