Bioinformatics (Oxford, England)[JOURNAL]
Sun
200 papers
CryoPromptSeg: prompt-guided segmentation with integrated denoising for cryo-EM particle picking.
Yang B, You Y, Jin L … +2 more , Yu H, Zhang L
Bioinformatics · 2026 Jun · PMID 42172598 · Full text
MOTIVATION: Cryo-electron microscopy (Cryo-EM) single particle analysis (SPA) is a key technique for revealing the structure of biomacromolecules by three-dimensional reconstruction. Achieving high-resolution reconstruct...
MOTIVATION: Cryo-electron microscopy (Cryo-EM) single particle analysis (SPA) is a key technique for revealing the structure of biomacromolecules by three-dimensional reconstruction. Achieving high-resolution reconstruction relies on the acquisition of a large number of authentic particles; however, manual particle picking is inefficient and inadequate for the demands of reconstruction, making automated particle picking a major research focus. Although the foundational segmentation model Segment Anything Model (SAM) has recently advanced automated particle picking, its segmentation advantages have not been fully realized in cryo-EM applications. Moreover, cryo-EM images often have significant noise. Conventional denoising decreases noise but frequently overlooks high-level semantic information, leading to oversmoothed particle regions and reduced particle distinguishability. RESULTS: To address these challenges, we propose CryoPromptSeg, which employs prompt-guided SAM for particle picking while integrating a semantically enhanced image denoiser. Specifically, by performing domain adaptation fine-tuning of SAM and incorporating prompts generated by the proposed automatic prompt generator, it achieves precise segmentation of cryo-EM particles. In addition, it employs a parallel multi-task framework to jointly train the denoiser and the prompt generator, incorporating particle semantic information from the prompt generator into the denoiser to suppress noise while preserving highly distinguishable particle structures. To lower the barrier to practical application, we developed a user-friendly online prediction platform for particle picking. Experimental results demonstrate that CryoPromptSeg outperforms existing mainstream methods in both particle picking accuracy and image denoising quality, thus providing a novel solution for the automation of particle picking. AVAILABILITY: The code and platform are available at: https://github.com/347251369/CryoPromptSeg.
3D registration-guided deformable residual inpainting for ssEM restoration.
Zhang Z, Feng J, Li H … +3 more , El-Messiry H, Xu Z, Han R
Bioinformatics · 2026 Jun · PMID 42172593 · Full text
MOTIVATION: Serial section electron microscopy (ssEM) is essential for studying biological cell structures at nanometer resolution. However, supporting film folding (SFF) degradation frequently occurs during sample prepa...
MOTIVATION: Serial section electron microscopy (ssEM) is essential for studying biological cell structures at nanometer resolution. However, supporting film folding (SFF) degradation frequently occurs during sample preparation, causing structural distortions and information loss that severely impair downstream analyses such as 3D reconstruction and neuron segmentation. RESULTS: We propose RegInpaint, a novel recovery framework that jointly addresses deformation correction and missing-information restoration caused by SFF degradation. RegInpaint formulates SFF recovery as a joint problem of 3D elastic registration and image inpainting, providing a generalizable solution for ssEM restoration. Experiments on four EM datasets show that RegInpaint consistently outperforms existing methods in image restoration quality and significantly improves neuron segmentation accuracy. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at https://github.com/zhangzhenbang2021/RegInpaint.git.
eFEL: electrophysiology feature extraction library.
Mandge D, Tuncel A, Jaquier A … +5 more , Kilic I, Damart T, Markram H, Van Geit W, Ranjan R
Bioinformatics · 2026 Jun · PMID 42172582 · Full text
MOTIVATION: Electrophysiological recordings are essential in experimental and computational neuroscience, providing insights into neuronal excitability and network behaviour. Extracting features such as action potential...
MOTIVATION: Electrophysiological recordings are essential in experimental and computational neuroscience, providing insights into neuronal excitability and network behaviour. Extracting features such as action potential thresholds, widths, and firing patterns is conceptually straightforward, but in practice it is complicated by heterogeneous datasets and software environments, which hinder reproducibility and interoperability. A standardized, efficient, and portable framework is needed to ensure consistent analysis across platforms and alignment with community data standards. RESULTS: We present the Electrophysiology Feature Extraction Library (eFEL), a cross-platform, open-source library that implements standardized definitions for over 90 electrophysiological features. eFEL combines a high-performance C++ core with a Python interface, supporting customizable feature dependencies, caching, and parallelization. It integrates with community standards such as Neurodata Without Borders and works seamlessly with common electrophysiology formats and simulation environments. Since its initial release in 2015, eFEL has been used in published studies spanning single-cell analysis, model optimization, multimodal fitting, and circuit simulations. eFEL provides a FAIR-compliant, versatile resource for reproducible electrophysiological data analysis. AVAILABILITY AND IMPLEMENTATION: The eFEL library is publicly available at https://github.com/openbraininstitute/eFEL and the associated study data and scripts have been deposited in Zenodo at https://zenodo.org/records/17241835.
Detecting unannotated splicing events in short-read RNA-seq with SAMI, a UMI-aware Nextflow pipeline.
Mareschal S, Wucher V, Huet S … +7 more , Léonce C, Chabane K, Hayette S, Bringuier PP, Pinson S, Barritault M, Bardel C
Bioinformatics · 2026 Jun · PMID 42166739 · Full text
SUMMARY: Although RNA-sequencing has replaced microarrays for gene expression profiling over the past 15 years, its full potential for splicing analysis in clinical settings remains underexploited. Most available tools a...
SUMMARY: Although RNA-sequencing has replaced microarrays for gene expression profiling over the past 15 years, its full potential for splicing analysis in clinical settings remains underexploited. Most available tools are tailored for large cohorts or known isoforms, limiting their applicability in routine diagnostics where non-recurring events must be identified in low-dimension datasets. We present SAMI (Splicing Analysis with Molecular Indexes), a fully-integrated UMI-aware pipeline designed to detect splicing events diverging from transcript annotations. Building upon the well-proven STAR aligner, SAMI introduces original post-processing of gaps and potential intron retention to maximize accuracy, along with clear graphical representations and tunable filtering stringency. The ability of SAMI and concurrent software to detect intragenic splicing aberrations and gene fusions was assessed, both on real data from a commercial control sample and simulated data generated with ASimulatoR. AVAILABILITY AND IMPLEMENTATION: Nextflow pipeline and Singularity container recipe freely available under GPL 3 license at https://github.com/HCL-HUBL/SAMI.
INDIGENA: inductive prediction of disease-gene associations using phenotype ontologies.
Zhapa-Camacho F, Hoehndorf R
Bioinformatics · 2026 Jun · PMID 42166723 · Full text
MOTIVATION: Predicting gene-disease associations (GDAs) can be framed as a ranking problem where genes are ranked for a query disease based on features such as phenotypic similarity. By describing phenotypes using phenot...
MOTIVATION: Predicting gene-disease associations (GDAs) can be framed as a ranking problem where genes are ranked for a query disease based on features such as phenotypic similarity. By describing phenotypes using phenotype ontologies, ontology-based semantic similarity measures can be used. However, traditional semantic similarity measures use only the ontology taxonomy. Recent methods based on ontology embeddings compare phenotypes in latent space; these methods can use all ontology axioms as well as a supervised signal, but are inherently transductive, i.e. query entities must already be known at the time of learning embeddings, and therefore these methods do not generalize to novel diseases (sets of phenotypes) at inference time. RESULTS: We developed INDIGENA, an inductive disease-gene association method for ranking genes based on a set of phenotypes. Our method first uses a graph projection to map axioms from phenotype ontologies to a graph structure, and then uses graph embeddings to create latent representations of phenotypes. We use an explicit aggregation strategy to combine phenotype embeddings into representations of genes or diseases, allowing us to generalize to novel sets of phenotypes. We also develop a method to make the phenotype embeddings and the similarity measure task-specific by including a supervised signal from known GDAs. We apply our method to mouse models of human disease and demonstrate that we can significantly improve over the inductive semantic similarity baseline measures, and reach a performance similar to transductive methods for predicting GDAs while being more general. AVAILABILITY AND IMPLEMENTATION: https://github.com/bio-ontology-research-group/indigena.
MetaNet: a scalable and integrated tool for reproducible omics network analysis.
Peng C, Jiang L, Huang Z … +7 more , Wei X, Zhu X, Liu Z, Chen Q, Shen X, Gao P, Jiang C
Bioinformatics · 2026 Jun · PMID 42162964 · Full text
MOTIVATION: Network analysis has become a central strategy for dissecting complex biological and environmental systems, particularly as modern omics technologies generate increasingly large and heterogeneous datasets. Ho...
MOTIVATION: Network analysis has become a central strategy for dissecting complex biological and environmental systems, particularly as modern omics technologies generate increasingly large and heterogeneous datasets. However, current tools often lack the scalability, flexibility, and native multi-omics support required for high-dimensional data analysis. We developed MetaNet, a high-performance R package that unifies network construction, visualization, and analysis across diverse omics layers. RESULTS: MetaNet enables fast and scalable correlation-based network construction for datasets with more than 10 000 features, providing over 40 layout algorithms, rich annotation utilities, and visualization options compatible with both static and interactive platforms. It further offers comprehensive topological and stability metrics for in-depth network characterization. Benchmarking shows that MetaNet delivers up to a 100-fold improvement in computation time and a 50-fold reduction in memory usage compared to existing R packages. We demonstrate its utility through two representative applications: (1) longitudinal microbial co-occurrence networks revealing airborne microbiome dynamics, and (2) an integrative exposome-transcriptome network of over 40 000 features, uncovering distinct regulatory impacts of biological and chemical exposures. By offering a robust, reproducible, and biologically informed framework, MetaNet advances multi-omics network analysis across biological, ecological, and environmental domains. AVAILABILITY: MetaNet package is freely available at https://github.com/Asa12138/MetaNet.
TriosCompass: a snakemake workflow for integrated detection of SNVs, indels, STRs, and structural de novo variants in parent-child trios.
Zhu W, Yeager M, Dawson ET … +5 more , Jain K, Hicks B, Dean M, Chanock SJ, Wong WSW
Bioinformatics · 2026 Jun · PMID 42162957 · Full text
MOTIVATION: The accurate and sensitive identification of de novo variants, which are unique to an individual and not found in the parents' germlines, is critical for understanding the genetic basis of rare diseases, deve...
MOTIVATION: The accurate and sensitive identification of de novo variants, which are unique to an individual and not found in the parents' germlines, is critical for understanding the genetic basis of rare diseases, developmental disorders, and evolutionary processes. Existing de novo variant detection pipelines often lack the flexibility to handle multiple variant types, struggle with speed and reproducibility across computational environments, demand extensive manual configuration, or require bioinformatics expertise for downstream curation and analysis, limiting their scalability and usability for large genomic studies. Accordingly, there is a pressing need to better address these challenges. RESULTS: We introduce TriosCompass, an open-source Snakemake workflow that addresses these challenges by providing a modular, accelerated, and environmentally-configurable end-to-end solution for comprehensive de novo variant discovery. It integrates state-of-the-art tools into a reproducible framework, empowering researchers to discover novel genetic insights with greater efficiency and reliability. AVAILABILITY: TriosCompass is implemented as a Snakemake workflow and is freely available at https://github.com/NCI-CGR/TriosCompass_v2 or on Zenodo (10.5281/zenodo.17981062). SUPPLEMENTARY INFORMATION: Supplementary data is available on GitHub at https://github.com/NCI-CGR/TriosCompass_v2/tree/manuscript/report_dashboards. Supplementary methods on DeepTrio benchmark runs can be viewed at: https://github.com/NCI-CGR/TriosCompass_v2/blob/manuscript/TriosCompass_Supp_Methods_deeptrio_benchmark.md.
CancerOmicsStudio (CoS): a web server for integrative and interpretable analysis of multi-omics cancer data.
Liao C, Dou R, Sun D … +5 more , Hu C, Hu H, Cheng L, Xu Y, Wang X
Bioinformatics · 2026 May · PMID 42162949 · Full text
MOTIVATION: Large-scale omics resources, including The Cancer Genome Atlas, Genomics of Drug Sensitivity in Cancer, and the Cancer Dependency Map, have become essential for cancer research. However, these datasets are di...
MOTIVATION: Large-scale omics resources, including The Cancer Genome Atlas, Genomics of Drug Sensitivity in Cancer, and the Cancer Dependency Map, have become essential for cancer research. However, these datasets are distributed across different platforms, formats and analysis frameworks, which limits their practical use by researchers without extensive computational expertise. RESULTS: We developed CancerOmicsStudio (CoS), a web server for integrative and interpretable analysis of multi-omics cancer data across 33 cancer types. CoS provides five major modules: CosAI, Traditional Analysis, Drug Sensitivity, CRISPR Dependency and Single-Cell Tumor Microenvironment. The Traditional Analysis module supports expression comparison, diagnostic evaluation, survival analysis, enrichment analysis and gene correlation. The Drug Sensitivity and CRISPR Dependency modules enable systematic evaluation of gene-drug response associations and gene essentiality in cancer cell lines. The Single-Cell Tumor Microenvironment module supports tumor microenvironment analysis at single-cell resolution. In total, approximately 1.23 million results have been precomputed to enable rapid retrieval. CosAI further allows users to submit natural-language queries and obtain results through a Real-time Analysis as Retrieval framework, with responses summarized by a lightweight language model. AVAILABILITY AND IMPLEMENTATION: CancerOmicsStudio is freely available at Zenodo (doi: 10.5281/zenodo.18744990) and https://cos.wanglab.bio.
Correction to: Enhancing interpretation of clinical disease-associated copy number variations from multiple sequencing strategies with CNVSeeker.
Bioinformatics · 2026 May · PMID 42152607 · Full text
Abstract loading — click title to view on PubMed.
Mitigating bias in multilabel medical text classification: a cooperative training framework with dynamic debiasing.
Li P, Zhang X, Hu X … +2 more , Lin Z, Zhou D
Bioinformatics · 2026 Jun · PMID 42152185 · Full text
MOTIVATION: Medical text classification plays a critical role in clinical decision support, automated diagnosis, and biomedical research. However, deep learning models are highly susceptible to dataset-induced biases, su...
MOTIVATION: Medical text classification plays a critical role in clinical decision support, automated diagnosis, and biomedical research. However, deep learning models are highly susceptible to dataset-induced biases, such as label bias and keyword bias, which can lead to unreliable predictions and poor generalization in real-world clinical applications. Existing debiasing methods often either overcorrect informative samples or lack interpretability during inference, limiting their effectiveness in multilabel medical text classification tasks. RESULTS: We propose Cooperative Debiasing Network (CoDeNet), a cooperative training framework that mitigates dataset bias through dynamic sample reweighting and interpretable counterfactual inference. The framework consists of a primary classifier and a debias estimator, where the debias estimator quantifies sample-level bias and dynamically regulates the optimization process through an elastic scaling mechanism. In addition, a counterfactual postprocessing strategy explicitly isolates label-level and keyword-level biases to improve interpretability. Experiments conducted on the DepressionEMO and BDI-Sen datasets demonstrate that CoDeNet consistently improves classification performance over strong transformer-based baselines, including BERT and MentalBERT. In particular, CoDeNet achieves improvements of up to +6.57% macro-F1 on BDI-Sen and +2.16% macro-F1 on DepressionEMO, with especially strong gains on low-frequency clinical labels. The results indicate that CoDeNet effectively reduces dataset-induced bias while preserving model robustness and interpretability. AVAILABILITY AND IMPLEMENTATION: The source code and implementation details of CoDeNet will be publicly available on GitHub: https://github.com/66ccff39C5BB/CoDeNet.
MetaCCI: meta cell-cell interaction inference and its application to CCIs characteristics of MDS.
Park H, Imoto S, Miyano S
Bioinformatics · 2026 May · PMID 42149830 · Full text
MOTIVATION: Cell-cell interactions (CCIs) are fundamental to multicellular organisms and play crucial roles in diverse biological processes and disease mechanisms. Understanding CCIs is vital for deciphering disease path...
MOTIVATION: Cell-cell interactions (CCIs) are fundamental to multicellular organisms and play crucial roles in diverse biological processes and disease mechanisms. Understanding CCIs is vital for deciphering disease pathogenesis and developing therapeutic strategies. Although numerous computational methods have been developed to infer CCIs from complex biological data, most existing approaches rely primarily on single-gene expression levels and ligand-receptor databases, often failing to capture the nuanced network-wide changes characteristic of disease states. RESULT: We propose MetaCCI, a novel computational strategy that integrates meta-information into CCI inference by extending the traditional gene expression-based analysis to a gene regulatory network framework. MetaCCI meticulously combines established ligand-receptor pairs with quantitative insights into gene behavior within complex gene networks, enabling the precise extraction of relevant targets for CCI inference. Subsequently, CCI inference was performed using an eigen cell co-expression network, providing a more holistic view of cell-cell communication. Monte Carlo simulations demonstrated that MetaCCI consistently outperforms existing methods in CCI inference. We applied MetaCCI to characterize cell-cell communication in Myelodysplastic Syndromes (MDS). Our results identified distinct interaction patterns in MDS compared with normal cell populations, specifically highlighting the loss of CCIs between "Dendritic cells and Hematopoietic precursor cells" and between "Dendritic cells and Hematopoietic multipotent progenitor cells" as characteristic features of MDS. Furthermore, FABP5, CD63, and HMGB1 were identified as MDS-specific markers. These findings suggest that diminished CCIs involving dendritic cells, hematopoietic precursor cells, and multipotent progenitor cells are pivotal to MDS pathogenesis. AVAILABILITY AND IMPLEMENTATION: The MetaCCI software is freely available at https://github.com/HeewonGitHub/MetaCCI. An archived version of the software and example datasets used in this study is available at Zenodo: https://doi.org/10.5281/zenodo.20101527.
intmap: fast and flexible mapping of mobile DNA integration for basic and translational research.
Bedwell GJ, Cherepanov P, Engelman AN
Bioinformatics · 2026 Jun · PMID 42149824 · Full text
SUMMARY: Integration of exogenous DNA into host cell chromatin is a hallmark of many virus and transposon lifecycles and the foundational basis for many modern cellular and genetic therapies. Defining integration sites (...
SUMMARY: Integration of exogenous DNA into host cell chromatin is a hallmark of many virus and transposon lifecycles and the foundational basis for many modern cellular and genetic therapies. Defining integration sites (ISs) in a population of cells can accordingly inform fundamental aspects of mobile DNA biology and therapeutic treatment outcomes. Here, we describe intmap, a software that generalizes IS analysis to diverse data-types and experimental systems. intmap functions independently of strict library design assumptions and is highly tunable with respect to analysis parameters. Using both simulated and experimental data, we show that IS mapping with intmap is fast, accurate, and highly flexible. AVAILABILITY AND IMPLEMENTATION: intmap, related documentation, and a convenient installation script are available at https://github.com/gbedwell/intmap. Additional information is provided in the online Supplement. Analysis commands, outputs, and other information related to this manuscript are available at https://doi.org/10.5281/zenodo.19475929.
Predicting gene-specific regulation with transcriptomic and epigenetic single-cell data.
Rumpf L, Behjati Ardakani F, Hecker D … +1 more , Schulz MH
Bioinformatics · 2026 Jun · PMID 42143610 · Full text
MOTIVATION: Analysis of single cell ATAC-seq and RNA-seq data has allowed to gain unprecedented insights into gene regulation by allowing to define cell type-specific regulatory regions and their effects on gene expressi...
MOTIVATION: Analysis of single cell ATAC-seq and RNA-seq data has allowed to gain unprecedented insights into gene regulation by allowing to define cell type-specific regulatory regions and their effects on gene expression. While powerful, such analysis is challenging due to the inherent sparsity of single cell data. RESULTS: We present a new approach, MetaFR, to learn gene-specific models that link open-chromatin variation from scATAC-seq data to gene expression from scRNA-seq. Using efficient regression trees, we illustrate that accurate expression prediction models can be learned on the single-cell or meta-cell level. Validation was done using fine-mapped eQTLs. Meta-cell models were found to outperform single-cell models for most genes. Comparison to the SOTA method SCARlink revealed advantages of MetaFR in terms of runtime and prediction performance. MetaFR thus allows time-efficient analysis and obtains reliable models of gene expression prediction, which can be used to study gene regulation in any organism for which scRNA-seq and scATAC-seq data is available. AVAILABILITY AND IMPLEMENTATION: MetaFR is available under https://github.com/SchulzLab/MetaFR.
PEStimate: predicting offspring disease risk after polygenic embryo screening.
Klausner L, Revital A, Lencz T … +1 more , Carmi S
Bioinformatics · 2026 Jun · PMID 42135951 · Full text
MOTIVATION: Polygenic embryo screening (PES) is a new, controversial technology, whereby human in vitro fertilization embryos are screened for their genetic risk of complex, polygenic diseases. PES aims to reduce the dis...
MOTIVATION: Polygenic embryo screening (PES) is a new, controversial technology, whereby human in vitro fertilization embryos are screened for their genetic risk of complex, polygenic diseases. PES aims to reduce the disease burden in offspring by prioritizing the selection of low-risk embryos. However, given that polygenic diseases are usually late-onset, PES outcomes must be estimated by epidemiological modeling. The liability threshold model has been previously used to predict outcomes. However, predictions rely on complex sets of equations, some of which require numerical integration or simulation. Further, previous models failed to account for the possibility that the selected embryo will not be born. RESULTS: Here, we present PEStimate, a freely available online app for predicting PES outcomes when screening for a single disease. PEStimate predicts the offspring risk with and without PES, as well as generates plots of the risk reduction versus key parameters. Users can adjust the number of available embryos, the live birth rate, the disease prevalence, the accuracy of the genetic risk predictor, the embryo selection method, the genetic risk of parents, and the disease status of parents, siblings, uncles/aunts, and grandparents of the embryos. Our model includes, for the first time, the possibility of embryo implantation failure, showing that risk reductions have been previously overestimated. PEStimate provides geneticists, healthcare professionals, patients, and other stakeholders with a necessary tool for examining the impact of PES and weighing its potential benefits against possible personal and societal harms. AVAILABILITY AND IMPLEMENTATION: PEStimate: https://polygenicembryo.shinyapps.io/pestimate. Source code: https://github.com/Lirazk/PEStimate.
BindPred: a framework for predicting protein-protein binding affinity from language model embeddings.
Piao H, Boorla VS, Santra S … +1 more , Maranas CD
Bioinformatics · 2026 Jun · PMID 42135943 · Full text
MOTIVATION: Reliable predictions of protein-protein binding affinities are essential for molecular biology and therapeutic discovery. However, most computational methods rely on three-dimensional structural models, which...
MOTIVATION: Reliable predictions of protein-protein binding affinities are essential for molecular biology and therapeutic discovery. However, most computational methods rely on three-dimensional structural models, which are often unavailable for many complexes. RESULTS: We introduce BindPred, a structure-agnostic input framework that predicts affinities directly from amino acid sequences by combining embeddings from large protein language models with gradient boosting trees. On the protein-protein binding (PPB)-Affinity benchmark, which comprises 11 919 diverse complexes, BindPred achieves a Pearson correlation coefficient of 0.86 in random split five-fold cross-validation. Ablation analysis indicates that evolutionary embeddings alone capture most of the predictive signals, while augmenting with physics-based energy terms from PyRosetta and BindCraft increases the correlation only by 0.01. A more stringent protein-level split that places entire protein families (wild-type and all mutants) exclusively in either training or testing sets, resulting in only a modest decline in performance, demonstrating robust generalization to novel interaction pairs. Because BindPred operates exclusively on sequence input, it enables rapid inference [approximately 3 million complexes per GPU (T4) hour], making proteome-scale screening computationally feasible. AVAILABILITY: The pretrained model and inference pipeline are available in a Google Colab notebook: BindPred Colab notebook. The training dataset, code, and model weights are available on the hugging face: https://huggingface.co/hbp5181/BindPred.
EMERALD-UI: an interactive web application to unveil novel protein biology hidden in the alternative alignment space.
Preoteasa A, Grigorjew A, Tomescu AI … +1 more , Drost HG
Bioinformatics · 2026 May · PMID 42133828 · Full text
SUMMARY: Life over the past four billion years has been shaped by proteins and their capacity to assemble into three-dimensional conformations. Protein sequence alignments have been the enabling technology for exploring...
SUMMARY: Life over the past four billion years has been shaped by proteins and their capacity to assemble into three-dimensional conformations. Protein sequence alignments have been the enabling technology for exploring the evolution and functional adaptation of proteins across the tree of life. Recent advancements in scaling the prediction of three-dimensional protein structures from primary sequence alone, revealed that different modes of conservation and function operate on the sequence and structure level. This difference in protein conservation patterns and their underlying functional change that could emerge in suboptimal alignment configurations is often ignored in optimal protein alignment approaches. We introduce EMERALD-UI, an open-source interactive web application which is designed to reveal unexplored biology by visualising stable structural conformations or protein regions hidden in the alternative alignment space. AVAILABILITY: EMERALD-UI is available at https://algbio.github.io/emerald-ui/. The source code of the version described in this manuscript is available at https://github.com/algbio/emerald-ui and archived at Software Heritage: swh: 1: dir: 8b5a70160396d5e9a2e6d015c3b6f1426176d9a4.
Yomix: an interactive tool for the exploration of low-dimensional embeddings in omics data.
Perrin-Gilbert N, Amjad N, Fumeron P … +2 more , Tulli S, Waterfall JJ
Bioinformatics · 2026 May · PMID 42133825 · Full text
SUMMARY: In the analysis of diverse omics data, a common and important preliminary step involves computing low-dimensional embeddings using techniques such as PCA, UMAP, t-SNE, or variational autoencoders. These embeddin...
SUMMARY: In the analysis of diverse omics data, a common and important preliminary step involves computing low-dimensional embeddings using techniques such as PCA, UMAP, t-SNE, or variational autoencoders. These embeddings provide a global overview of sample distributions and their relationships, often serving as the basis for formulating biological hypotheses. To facilitate rapid and intuitive exploration of such low-dimensional embeddings, we developed Yomix, an interactive omics-agnostic visualization and data exploration tool. Yomix enables users to flexibly define subsets of interest using a lasso selection tool, instantly compute their feature signatures, and compare their distributions. Yomix is a fast and efficient tool for interactive exploration of diverse omics datasets. AVAILABILITY AND IMPLEMENTATION: Yomix and its documentation are publicly available at https://github.com/perrin-isir/yomix.
A novel method for across-chromosome phasing without relative data.
Sapin E, Kelly KM, Keller MC
Bioinformatics · 2026 May · PMID 42128011 · Full text
MOTIVATION: Across-chromosome phasing identifies which haplotypes of different chromosomes come from the same parent. This differs from within-chromosome phasing, which uses linkage disequilibrium patterns to determine w...
MOTIVATION: Across-chromosome phasing identifies which haplotypes of different chromosomes come from the same parent. This differs from within-chromosome phasing, which uses linkage disequilibrium patterns to determine which alleles were co-inherited within each chromosome but does not match haplotypes across different chromosomes. While across-chromosome phasing can be conducted using genotypes from parents or close relatives, current methods perform poorly for samples of unrelated individuals. Here, we introduce a novel approach for across-chromosome phasing that employs a window-based SNP-similarity metric, eliminating the need for data from close relatives or detection of identical-by-descent haplotypes. RESULTS: Using UK Biobank offspring with both parents genotyped as a gold standard, we evaluated the performance of our method by phasing the offspring without using parental data. In genomic data with no within-chromosome phase errors, our algorithm achieved a mean across-chromosome phasing accuracy of 95%, with 53% of individuals phased perfectly. When data was pre-phased computationally using a standard within-chromosome phasing algorithm, mean accuracy for across-chromosome phasing dropped to 83.1%. Thus, our method is limited primarily by the accuracy of within-chromosome phasing accuracy and can approach near-perfect across-chromosome phasing accuracy as within-chromosome phasing accuracy improves. AVAILABILITY AND IMPLEMENTATION: The implementation was executed within a multi-node computational environment of University of Colorado Boulder Research Computing (Blanca Cluster: https://www.colorado.edu/rc/resources/blanca), employing parallelization techniques in the C programming language. The source code has been made publicly accessible online at https://github.com/emmanuelsapin/AcrossChromosomesPhasing, thereby facilitating reproducibility of the results for researchers with authorized access to the UK Biobank dataset.
SCUDDO: an unsupervised clustering algorithm for single-cell Hi-C maps using diagonal diffusion operators.
Maisuradze L, Shattuck MD, O'Hern CS
Bioinformatics · 2026 May · PMID 42114088 · Full text
MOTIVATION: Advances in high-throughput chromatin conformation capture have provided insight into the three-dimensional structure and organization of chromatin. While bulk Hi-C experiments capture spatio-temporally avera...
MOTIVATION: Advances in high-throughput chromatin conformation capture have provided insight into the three-dimensional structure and organization of chromatin. While bulk Hi-C experiments capture spatio-temporally averaged chromatin interactions across millions of cells, single-cell Hi-C experiments report on the chromatin interactions of individual cells. Supervised and unsupervised algorithms have been developed to embed single-cell Hi-C maps and identify different cell types. However, single-cell Hi-C maps are often difficult to cluster due to their high sparsity, with state-of-the-art algorithms achieving a maximum Adjusted Rand Index (ARI) of only ≲0.4 on several datasets. RESULTS: We introduce a novel unsupervised algorithm, Single-cell Clustering Using Diagonal Diffusion Operators (SCUDDO), to embed and cluster single-cell Hi-C maps. We evaluate SCUDDO on four previously difficult-to-cluster single-cell Hi-C datasets, and show that it can outperform other current algorithms in ARI by ≳0.2. Further, SCUDDO outperforms all other tested algorithms even when we restrict the number of intrachromosomal maps for each cell type and when we use only a small fraction of contacts in each Hi-C map. Thus, SCUDDO can capture the underlying latent features of single-cell Hi-C maps and provide accurate labelling of cell types even when cell types are not known a priori. AVAILABILITY AND IMPLEMENTATION: SCUDDO is freely available at https://www.github.com/lmaisuradze/scuddo as well as https://doi.org/10.6084/m9.figshare.31759915. The tested datasets are publicly available and can be downloaded from the Gene Expression Omnibus.
SNaQ.jl: Improved scalability for level-1 phylogenetic network inference.
Kolbow N, Kong S, Chafin T … +3 more , Justison J, Ané C, Solís-Lemus C
Bioinformatics · 2026 Jun · PMID 42114082 · Full text
MOTIVATION: Phylogenetic networks represent complex biological scenarios that are overlooked in trees, such as hybridization and horizontal gene transfer. Although numerous methods have been developed for phylogenetic ne...
MOTIVATION: Phylogenetic networks represent complex biological scenarios that are overlooked in trees, such as hybridization and horizontal gene transfer. Although numerous methods have been developed for phylogenetic network inference, their scalability is severely limited by the computational demands of likelihood optimization and the vastness of network space. Composite (or pseudo-) likelihood approaches like SNaQ have improved computational tractability for network inference, but they remain inadequate for datasets of sizes routinely handled by tree inference methods. RESULTS: Here, we introduce SNaQ.jl, a new standalone Julia package with the composite likelihood inference originally implemented within PhyloNetworks.jl as well as new scalability features that enhance computational efficiency through (i) parallelization of quartet likelihood calculations during composite likelihood computation, (ii) weighted random selection of quartets, and (iii) probabilistic decision-making during network search. Through a simulation study and empirical data analysis, we show that this new version of SNaQ.jl (version 1.1) improves average runtimes by up to 499% on average with no change in function parameters or method accuracy. AVAILABILITY AND IMPLEMENTATION: SNaQ.jl is a new open source Julia package available at https://github.com/JuliaPhylo/SNaQ.jl.
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