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1. Strainify: Strain-Level Microbiome Profiling for Low-Coverage Short-Read Metagenomic Datasets

   https://doi.org/10.1101/2025.10.10.681738  

   Luo, Rossie S; Kille, Bryce; Vaughan, Ellen E; Clark, Justin R; Maresso, Anthony W; Nute, Michael G; Treangen, Todd J (October 2025, bioRxiv)

   ABSTRACT MotivationStrain-level microbiome profiling has revealed key insights into microbial community composition and strain dynamics. However, accurate strain-level analysis remains challenging due to limited linkage information, ambiguous read mapping, and complicating factors such as genome similarity, sequencing depth, and community complexity. These challenges are especially pronounced for short-read metagenomic data when estimating the relative abundances of multiple strains, a task critical for genotype-phenotype association studies. ResultsTo address this gap, we present Strainify, which enables accurate strain-level abundance estimation from short-read metagenomes with as little as 1% genome coverage. Specifically, Strainify combines (1) identification of informative variants via core genome alignment, (2) filtering of confounding variants via a window-based test, and (3) maximum likelihood estimation of strain abundances. A Shannon entropy-weighted version of the model further improves robustness in noisy, low-coverage settings by downweighting sites with low information content. Across simulated communities of varying complexity, Strainify consistently outperformed existing approaches. On mock community sequencing data, Strainify’s estimates aligned more closely with reference abundances. When applied to a longitudinal gut microbiome dataset, Strainify successfully recapitulated the reported temporal dynamics ofBacteroides ovatusstrain groups, demonstrating its ability to recover biologically meaningful patterns from real-world metagenomes. Together, these results establish Strainify as a robust and versatile solution for accurate strain-level abundance estimation in short-read, low-coverage microbiome studies. AvailabilityThe Strainify code and results are available at:https://github.com/treangenlab/Strainify 

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2. Seqwin: Ultrafast identification of signature sequences in microbial genomes

   https://doi.org/10.1101/2025.11.07.687294  

   Wang, Michael X; Kille, Bryce; Nute, Michael G; Zhou, Siyi; Stadler, Lauren B; Treangen, Todd J (November 2025, bioRxiv)

   Abstract MotivationPolymerase 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. ResultsSeqwin 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 5 minutes. Seqwin provides an open-source solution for the long-standing need for scalable microbial signature discovery and diagnostic assay design. Availability and ImplementationSeqwin is freely available for academic use (https://github.com/treangenlab/Seqwin) and can be installed via Bioconda. Benchmarking datasets, outputs, and scripts are available on Zenodohttps://doi.org/10.5281/zenodo.19176444. Contacttreangen@rice.edu,xw66@rice.edu Supplementary MaterialsProvided as separate PDF and data files. 

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3. A rapid, low-cost, and highly sensitive SARS-CoV-2 diagnostic based on whole-genome sequencing

   https://doi.org/10.1371/journal.pone.0294283  

   Adastra, Per A; Durand, Neva C; Mitra, Namita; Pulido, Saul Godinez; Mahajan, Ragini; Blackburn, Alyssa; Colaric, Zane L; Theisen, Joshua_W M; Weisz, David; Dudchenko, Olga; et al (November 2023, PLOS ONE)

   Giri, Basant (Ed.)

   Early detection of SARS-CoV-2 infection is key to managing the current global pandemic, as evidence shows the virus is most contagious on or before symptom onset. Here, we introduce a low-cost, high-throughput method for diagnosing and studying SARS-CoV-2 infection. Dubbed Pathogen-Oriented Low-Cost Assembly & Re-Sequencing (POLAR), this method amplifies the entirety of the SARS-CoV-2 genome. This contrasts with typical RT-PCR-based diagnostic tests, which amplify only a few loci. To achieve this goal, we combine a SARS-CoV-2 enrichment method developed by the ARTIC Network (https://artic.network/) with short-read DNA sequencing andde novogenome assembly. Using this method, we can reliably (>95% accuracy) detect SARS-CoV-2 at a concentration of 84 genome equivalents per milliliter (GE/mL). The vast majority of diagnostic methods meeting our analytical criteria that are currently authorized for use by the United States Food and Drug Administration with the Coronavirus Disease 2019 (COVID-19) Emergency Use Authorization require higher concentrations of the virus to achieve this degree of sensitivity and specificity. In addition, we can reliably assemble the SARS-CoV-2 genome in the sample, often with no gaps and perfect accuracy given sufficient viral load. The genotypic data in these genome assemblies enable the more effective analysis of disease spread than is possible with an ordinary binary diagnostic. These data can also help identify vaccine and drug targets. Finally, we show that the diagnoses obtained using POLAR of positive and negative clinical nasal mid-turbinate swab samples 100% match those obtained in a clinical diagnostic lab using the Center for Disease Control’s 2019-Novel Coronavirus test. Using POLAR, a single person can manually process 192 samples over an 8-hour experiment at the cost of ~$36 per patient (as of December 7^th, 2022), enabling a 24-hour turnaround with sequencing and data analysis time. We anticipate that further testing and refinement will allow greater sensitivity using this approach. 

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4. νDoBe — A Python tool for neutrinoless double beta decay

   https://doi.org/10.1007/JHEP08(2023)043  

   Scholer, Oliver; de_Vries, Jordy; Gráf, Lukáš (August 2023, Journal of High Energy Physics)

   A<sc>bstract</sc> We presentνDoBe, a Python tool for the computation of neutrinoless double beta decay (0νββ) rates in terms of lepton-number-violating operators in the Standard Model Effective Field Theory (SMEFT). The tool can be used for automated calculations of 0νββrates, electron spectra and angular correlations for all isotopes of experimental interest, for lepton-number-violating operators up to and including dimension 9. The tool takes care of renormalization-group running to lower energies and provides the matching to the low-energy effective field theory and, at lower scales, to a chiral effective field theory description of 0νββrates. The user can specify different sets of nuclear matrix elements from various many-body methods and hadronic low-energy constants. The tool can be used to quickly generate analytical and numerical expressions for 0νββrates and to generate a large variety of plots. In this work, we provide examples of possible use along with a detailed code documentation. The code can be accessed through: GitHub:https://github.com/OScholer/nudobe Online User-Interface:https://oscholer-nudobe-streamlit-4foz22.streamlit.app/ 

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5. TIPP3 and TIPP3-fast: Improved abundance profiling in metagenomics

   https://doi.org/10.1371/journal.pcbi.1012593  

   Shen, Chengze; Wedell, Eleanor; Pop, Mihai; Warnow, Tandy (April 2025, PLOS Computational Biology)

   Zhu, Shanfeng (Ed.)

   We present TIPP3 and TIPP3-fast, new tools for abundance profiling in metagenomic datasets. Like its predecessor, TIPP2, the TIPP3 pipeline uses a maximum likelihood approach to place reads into labeled taxonomies using marker genes, but it achieves superior accuracy to TIPP2 by enabling the use of much larger taxonomies through improved algorithmic techniques. We show that TIPP3 is generally more accurate than leading methods for abundance profiling in two important contexts: when reads come from genomes not already in a public database (i.e., novel genomes) and when reads contain sequencing errors. We also show that TIPP3-fast has slightly lower accuracy than TIPP3, but is also generally more accurate than other leading methods and uses a small fraction of TIPP3’s runtime. Additionally, we highlight the potential benefits of restricting abundance profiling methods to those reads that map to marker genes (i.e., using a filtered marker-gene based analysis), which we show typically improves accuracy. TIPP3 is freely available athttps://github.com/c5shen/TIPP3. 

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