Seqwin: ultrafast identification of signature sequences in microbial genomes.

Authors

Michael X Wang, Bryce Kille, Michael G Nute, Siyi Zhou, Lauren B Stadler, Todd J Treangen

Published in

Bioinformatics (Oxford, England). Volume 42. Issue Supplement_1. Jul 01, 2026.

Abstract

Polymerase chain reaction (PCR) enables rapid, cost-effective diagnostics but requires prior identification of genomic regions that allow sensitive and specific detection of target microbial groups, herein referred to as microbial signature sequences. We introduce Seqwin, an open-source framework designed to automate microbial genome signature discovery. Tens of thousands of microbial genomes are now available for a single species, limiting the application of existing manual and automated approaches for identifying signatures. Modern approaches that are capable of leveraging all available microbial genomes will ensure sensitive and accurate DNA signature identification and enable robust pathogen detection for clinical, environmental, and public health applications.
Seqwin builds weighted pan-genome minimizer graphs and uses a traversal algorithm to identify signature sequences that occur frequently in target genomes but remain rare in non-targets. Unlike earlier tools that depend on strict presence or absence of sequences, Seqwin accommodates natural sequence variation and scales to very large genome collections. When applied to genomes from C. difficile, M. tuberculosis, and S. enterica, Seqwin recovered more high-quality signatures than alternative methods with lower computational burden. Seqwin's analysis of nearly 15 000 S. enterica genomes yielded over 200 candidate signatures in three minutes. Seqwin provides an open-source solution for the long-standing need for scalable microbial signature discovery and diagnostic assay design.
Seqwin is available on GitHub (https://github.com/treangenlab/Seqwin) and can be installed via Bioconda (https://bioconda.github.io/recipes/seqwin/README.html). Benchmarking datasets, outputs, and scripts are available on Zenodo (https://doi.org/10.5281/zenodo.19874011).

PMID:
42412815
Bibliographic data and abstract were imported from PubMed on 08 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.