phu 0.5.0

Phage bioinformatics utilities (seqclust runner and friends).

Combating Phage Genomes

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phu - Phage Utilities

phu (phage utilities) or phutilities, is a modular toolkit for viral genomics workflows. It provides command-line tools to handle common steps in phage bioinformatics pipelines—wrapping complex utilities behind a consistent and intuitive interface.

Installation

You can install phu using mamba or conda from the bioconda channel:

mamba create -n phu bioconda::phu

Usage

As a command-line tool, phu follows a modular structure. You can access different functionalities through subcommands. The general syntax is:

phu <command> [options]

Commands

• screen: Screen contigs for specific protein families using HMMER on predicted coding sequences.
• cluster: Cluster viral sequences into species or other operational taxonomic units (OTUs).
• simplify-taxa: Simplify vContact taxonomy prediction columns into compact lineage codes.

Contributing

We welcome contributions to phu! Please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bugfix.
3. Make your changes and commit them.
4. Submit a pull request describing your changes.

Developers

You can also install the development version of phu directly from GitHub:

git clone https://github.com/camilogarciabotero/phu.git
cd phu
pip install -e .

phu is also available on PyPI:

pip install phu

References

This program uses several key tools and libraries, make sure to acknowledge them when using phu:

• vclust: A high-performance clustering tool for viral sequences:

Zielezinski A, Gudyś A, Barylski J, Siminski K, Rozwalak P, Dutilh BE, Deorowicz S. Ultrafast and accurate sequence alignment and clustering of viral genomes. Nat Methods. https://doi.org/10.1038/s41592-025-02701-7

• seqkit: A toolkit for FASTA/Q file manipulation.

Wei Shen*, Botond Sipos, and Liuyang Zhao. 2024. SeqKit2: A Swiss Army Knife for Sequence and Alignment Processing. iMeta e191. doi:10.1002/imt2.191.

• Prodigal: A gene prediction tool for prokaryotic genomes.

Hyatt, D., Chen, G. L., LoCascio, P. F., Land, M. L., Larimer, F. W., & Hauser, L. J. (2010). Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC bioinformatics, 11(1), 119. https://doi.org/10.1186/1471-2105-11-119

• pyrodigal: A tool for gene prediction in prokaryotic genomes.

Larralde, M., (2022). Pyrodigal: Python bindings and interface to Prodigal, an efficient method for gene prediction in prokaryotes. Journal of Open Source Software, 7(72), 4296, https://doi.org/10.21105/joss.04296

• HMMER: A suite of tools for sequence analysis using profile hidden Markov models.

Eddy, S. R. (2011). Accelerated Profile HMM Searches. PLoS Computational Biology, 7(10), e1002195. https://doi.org/10.1371/journal.pcbi.1002195

• pyHMMER: Python bindings for HMMER.

Larralde, M., & Zeller, G. (2023). PyHMMER: a Python library binding to HMMER for efficient sequence analysis. Bioinformatics, 39(5). https://doi.org/10.1093/bioinformatics/btad214