pyani-plus : a multitool for average nucleotide identity estimation

Tools

Pritchard, Leighton and Kiepas, Angelika and Cock, Peter (2026) pyani-plus : a multitool for average nucleotide identity estimation. In: Microbiology Society Annual Conference, 2026-04-13 - 2026-04-16, Northern Ireland. (https://doi.org/10.6084/m9.figshare.31999287.v2)

[thumbnail of Pritchard-etal-MicroBio-2026-pyani-plus-a-multitool-for-average-nucleotide-identity-estimation]
Preview
 Text. Filename: Pritchard-etal-MicroBio-2026-pyani-plus-a-multitool-for-average-nucleotide-identity-estimation.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (8MB)| Preview

Abstract

Overall genome relatedness index (OGRI) methods such as digital DNA-DNA hybridisation (dDDH) and average nucleotide identity (ANI), and genome identity estimates using k-mer based approaches such as Mash, fastANI, and sourmash, are central to taxonomic assignment and classification of microbes in modern microbiology. The original, widely used pyani software was written to make several ANI methods available in a single bioinformatics tool, parallelising comparisons across multicore machines and compute clusters for rapid analysis of very large microbial genome datasets. We present pyani-plus (https://pyani-plus.github.io/pyani-plus-docs/), completely rewritten from the ground up as part of the genomeRxiv project (https://genomerxiv.cs.vt.edu) to extend and improve the capabilities of pyani. The new pyani-plus software now supports additional ANI estimation methods, including fastANI and sourmash. ANI calculations are stored in a persistent, shareable local database. The database records software and comparison parameters, and allows for inclusion of new genomes to existing analyses and regeneration of result sets with no need for recalculation, enhancing reproducibility and supporting open research. pyani-plus supports a wider range of job schedulers for deployment on compute clusters, and is able to resume interrupted analysis runs. A new graph-based hierarchical classification algorithm is implemented that gathers genomes into self-consistent cliques on the basis of ANI value, as an aid to classification. These new capabilities are wrapped in a friendlier user interface with improved and extended options for graphical output and data exchange.

ORCID iDs

Pritchard, Leighton ORCID logoORCID: https://orcid.org/0000-0002-8392-2822, Kiepas, Angelika ORCID logoORCID: https://orcid.org/0000-0002-5133-492X and Cock, Peter ORCID logoORCID: https://orcid.org/0000-0001-9513-9993;
CORE (COnnecting REpositories)