More Like this

1. 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. 

   more » « less
2. E(3) equivariant graph neural networks for robust and accurate protein-protein interaction site prediction

   https://doi.org/10.1371/journal.pcbi.1011435  

   Roche, Rahmatullah; Moussad, Bernard; Shuvo, Md Hossain; Bhattacharya, Debswapna (August 2023, PLOS Computational Biology)

   Li, Jinyan (Ed.)

   Artificial intelligence-powered protein structure prediction methods have led to a paradigm-shift in computational structural biology, yet contemporary approaches for predicting the interfacial residues (i.e., sites) of protein-protein interaction (PPI) still rely on experimental structures. Recent studies have demonstrated benefits of employing graph convolution for PPI site prediction, but ignore symmetries naturally occurring in 3-dimensional space and act only on experimental coordinates. Here we present EquiPPIS, an E(3) equivariant graph neural network approach for PPI site prediction. EquiPPIS employs symmetry-aware graph convolutions that transform equivariantly with translation, rotation, and reflection in 3D space, providing richer representations for molecular data compared to invariant convolutions. EquiPPIS substantially outperforms state-of-the-art approaches based on the same experimental input, and exhibits remarkable robustness by attaining better accuracy with predicted structural models from AlphaFold2 than what existing methods can achieve even with experimental structures. Freely available athttps://github.com/Bhattacharya-Lab/EquiPPIS, EquiPPIS enables accurate PPI site prediction at scale. 

   more » « less
3. ClipperQTL: ultrafast and powerful eGene identification method

   https://doi.org/10.1186/s13059-025-03662-y  

   Zhou, Heather J; Ge, Xinzhou; Li, Jingyi Jessica (December 2025, Genome Biology)

   Abstract A central task in expression quantitative trait locus analysis is to identify cis-eGenes, i.e., genes whose expression levels are regulated by at least one local genetic variant. Existing cis-eGene identification methods are either computationally expensive, requiring thousands of permutations per gene (FastQTL), or statistically underpowered (eigenMT and TreeQTL). We propose ClipperQTL, which requires only one permutation for data sets with large sample sizes (>450; ClipperQTL works on smaller data sets too). We show that ClipperQTL performs as well as FastQTL and runs up to 500 times faster. The R package ClipperQTL is available athttps://github.com/heatherjzhou/ClipperQTL. 

   more » « less
4. TIPP-SD: A new method for species detection in microbiomes

   https://doi.org/10.1371/journal.pcbi.1014347  

   Shen, Chengze; Wedell, Eleanor; Pop, Mihai; Warnow, Tandy (May 2026, PLOS Computational Biology)

   Zhu, Shanfeng (Ed.)

   In this study, we present TIPP-SD (i.e., TIPP for Species Detection), a new technique for species detection in a microbiome sample. TIPP-SD uses a substantially modified version of TIPP3, which is a recently developed abundance profiling tool based on maximum likelihood phylogenetic placement into marker gene taxonomies. TIPP-SD depends on a parameter (i.e., “threshold”) for the required support for species detection, thus allowing us to compute a precision-recall curve as we vary this parameter. In comparing the precision-recall curves for TIPP-SD, TIPP3, Kraken2, Bracken, Metabuli, and Metapresence, we find that TIPP-SD improves on the other methods with respect to accuracy under conditions where there is a highly variable distribution of species abundance or where there is sequencing error. Under other conditions, TIPP-SD is close to the best of these methods. Finally, although TIPP-SD is slower than the other methods, it is still fast enough to be used on large datasets. TIPP-SD is available in github as part of the TIPP3 software package. 

   more » « less
5. Heterogeneous pseudobulk simulation enables realistic benchmarking of cell-type deconvolution methods

   https://doi.org/10.1186/s13059-024-03292-w  

   Hu, Mengying; Chikina, Maria (December 2024, Genome Biology)

   Abstract BackgroundComputational cell type deconvolution enables the estimation of cell type abundance from bulk tissues and is important for understanding tissue microenviroment, especially in tumor tissues. With rapid development of deconvolution methods, many benchmarking studies have been published aiming for a comprehensive evaluation for these methods. Benchmarking studies rely on cell-type resolved single-cell RNA-seq data to create simulated pseudobulk datasets by adding individual cells-types in controlled proportions. ResultsIn our work, we show that the standard application of this approach, which uses randomly selected single cells, regardless of the intrinsic difference between them, generates synthetic bulk expression values that lack appropriate biological variance. We demonstrate why and how the current bulk simulation pipeline with random cells is unrealistic and propose a heterogeneous simulation strategy as a solution. The heterogeneously simulated bulk samples match up with the variance observed in real bulk datasets and therefore provide concrete benefits for benchmarking in several ways. We demonstrate that conceptual classes of deconvolution methods differ dramatically in their robustness to heterogeneity with reference-free methods performing particularly poorly. For regression-based methods, the heterogeneous simulation provides an explicit framework to disentangle the contributions of reference construction and regression methods to performance. Finally, we perform an extensive benchmark of diverse methods across eight different datasets and find BayesPrism and a hybrid MuSiC/CIBERSORTx approach to be the top performers. ConclusionsOur heterogeneous bulk simulation method and the entire benchmarking framework is implemented in a user friendly packagehttps://github.com/humengying0907/deconvBenchmarkingandhttps://doi.org/10.5281/zenodo.8206516, enabling further developments in deconvolution methods. 

   more » « less