NAR Genomics and Bioinformatics最新文献

{"title":"DoBSeqWF: a framework for sensitive detection of individual genetic variation in pooled sequencing data.","authors":"Mads Cort Nielsen, Christian Munch Hagen, Ulrik Kristoffer Stoltze, Thomas van Overeem Hansen, Mette Nyegaard, Henrik Hjalgrim, Marie Bækvad-Hansen, Anna Byrjalsen, Kjeld Schmiegelow, Karin Wadt, Jonas Bybjerg-Grauholm, Simon Rasmussen","doi":"10.1093/nargab/lqag021","DOIUrl":"10.1093/nargab/lqag021","url":null,"abstract":"<p><p>Population screening for rare genetic diseases has the potential to increase early diagnosis and treatment, but the high cost of next-generation sequencing limits widespread implementation. Double-batched sequencing (DoBSeq) is a cost-effective method that uses two-dimensional overlapping pool sequencing to enable individual-level rare variant detection. However, the resulting high-depth, complex data require a specialized workflow for efficient, sensitive, and reproducible analysis. We developed DoBSeqWF (DoBSeq Workflow), a Nextflow-based pipeline that processes pooled sequencing data from alignment through variant calling, filtering, and final variant assignment. Using a childhood cancer cohort of 200 individuals with whole genome sequencing as a reference, we created training and validation datasets, benchmarked multiple variant callers, and implemented machine learning filters to improve rare variant detection while maintaining high sensitivity. DoBSeqWF demonstrates accurate and scalable rare variant detection within the evaluated experimental setting and provides a promising avenue for future cost-effective genetic screening programmes.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqag021"},"PeriodicalIF":3.0,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12907731/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146214408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FragmentFinder-a user-friendly, Windows-based tool for identifying and characterizing short RNAs excised from any noncoding RNA.","authors":"Jeffrey D DeMeis, Courtlyn M Breaux, Allison H Powell, Nick J Campbell, Dominika Houserova, Jingshan Huang, Glen M Borchert","doi":"10.1093/nargab/lqag017","DOIUrl":"10.1093/nargab/lqag017","url":null,"abstract":"<p><p>Noncoding RNAs <200 nucleotides (nt) in length are referred to as short noncoding RNAs (sncRNAs) and include microRNAs (miRNAs), piwi-interacting RNAs, small nucleolar RNAs, transfer RNAs, etc. One striking example of the regulatory capabilities of sncRNAs comes from a group of small yet potent RNAs called miRNAs. MiRNAs are ∼20-nt RNAs excised from longer pre-miRNA hairpins, and to date, thousands of miRNAs have been identified across an array of species with specific roles for miRNAs defined in virtually every cellular activity (e.g. growth, differentiation, apoptosis, and disease). Importantly, studies aimed at evaluating the transcriptomic changes of miRNAs have now revealed the existence of miRNA-like fragments derived from other types of sncRNAs and suggest similar regulatory capacities may be associated with these novel sncRNA fragments. Unfortunately, many biologically relevant sncRNA-excised fragments remain uncharacterized due to their routine exclusion during initial miRNA characterizations as \"sncRNA degradation products\" as well as nearly all sncRNA informatic analyses continuing to solely assess annotated miRNA expressions. To address this, several platforms aimed at identifying novel sncRNA fragments have recently been developed. That said, the principal analytical tools currently employed to characterize novel sncRNA fragments often require significant computational expertise hindering their widespread utilization. As such, the development of a user-friendly platform, requiring minimal programming experience yet capable of identifying and characterizing RNA fragments excised from any sncRNA from any species is highly desirable and potentially impactful. In light of this, we have developed FragmentFinder-an intuitive, Windows-executable resource designed to require absolutely no computational background and capable of accurately characterizing all (annotated and unknown) sncRNA-derived RNAs within a raw small RNA sequencing file in real time.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqag017"},"PeriodicalIF":3.0,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12907730/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146214376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerating rare disease diagnostics by linking DNA and RNA through an explainable and interactive RNA-guided workflow.","authors":"Willem T K Maassen, Charlotte C E T Pape, Carlos G Urzua-Traslavina, Tim Niemeijer, Tijs van Lieshout, Margriet van der Molen, Lennart F Johansson, Kasper J van der Velde, Lude Franke, Mariëlle E van Gijn, Morris A Swertz","doi":"10.1093/nargab/lqag016","DOIUrl":"10.1093/nargab/lqag016","url":null,"abstract":"<p><p>Challenges preventing mainstream use of RNA-sequencing (RNA-seq) in genome diagnostics are sources of biological and technical variation, typically caused by intrinsic differences in gene expression between tissue types, cellular conditions, and environmental factors. While machine learning methods may partially correct unwanted variation, interpreting RNA-seq data that are typically generated by different sources over time, which is a realistic scenario in healthcare, remains challenging and complex. We developed a complete RNA-guided workflow that handles such variation and is therefore able to identify gene-disease associations in the context of genomic, phenotypic, and segregation analysis of rare disease patients. The result is a streamlined implementation of OUTRIDER and FRASER, complemented with Borzoi and MOLGENIS VIP. This novel workflow paves the way for pinpointing rare variants affecting gene expression and splicing using self-contained interactive reports visualizing outlier genes and prioritized patient-level variants for immediate clinical interpretation. We analysed 144 cases from different centres, a realistic cohort for centres more likely to be dependent on background cohorts. We demonstrate that RNA outlier analysis enhances variant interpretation and, despite its limitations, is already able to aid clinical variant interpretation. Our workflow accelerates the prioritization of coding and non-coding variants, and the reclassification of clinically relevant variants of unknown significance.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqag016"},"PeriodicalIF":3.0,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12891912/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146182767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>DEprot</i>: a comprehensive R-package for the analysis of label-free quantitation mass spectrometry data.","authors":"Nils Eickhoff, Liesbeth Hoekman, Onno Bleijerveld, Andries M Bergman, Wilbert Zwart, Sebastian Gregoricchio","doi":"10.1093/nargab/lqag015","DOIUrl":"10.1093/nargab/lqag015","url":null,"abstract":"<p><p>In recent years, studies investigating protein-protein interactions on a proteome-wide scale have increased exponentially. These developments were achieved through implementation of new techniques, as well as improvements in mass spectrometry hardware. However, issues specific to this type of technique, such as distinct imputation of random and not-at-random missing proteins, have not been completely addressed. Here, we present <i>DEprot</i>, an R-package providing a comprehensive toolset for end-to-end analysis and visualization of proteomics data. Moreover, we implemented a serial imputation strategy for handling non-random missing values, particularly suitable for protein enrichment techniques and knockout conditions.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqag015"},"PeriodicalIF":3.0,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12891908/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146182742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences.","authors":"Styliani-Christina Fragkouli, Somya Iqbal, Lisa Crossman, Barbara Gravel, Nagat Masued, Mark Onders, Devesh Haseja, Alex Stikkelman, Alfonso Valencia, Tom Lenaerts, Fotis Psomopoulos, Pilib Ó Broin, Núria Queralt-Rosinach, Davide Cirillo","doi":"10.1093/nargab/lqag012","DOIUrl":"10.1093/nargab/lqag012","url":null,"abstract":"<p><p>Synthetic data (SD) has become an increasingly important asset in the life sciences, helping address data scarcity, privacy concerns, and barriers to data access. Creating artificial datasets that mirror the characteristics of real data allows researchers to develop and validate computational methods in controlled environments. Despite its promise, the adoption of SD in life sciences hinges on rigorous evaluation metrics designed to assess their fidelity and reliability. To explore the current landscape of SD evaluation metrics in distinct life sciences domains, the ELIXIR Machine Learning Focus Group performed a systematic review of the scientific literature following the PRISMA guidelines. Six critical domains were examined to identify current practices for assessing SD. Findings reveal that, while generation methods are rapidly evolving, systematic evaluation is often overlooked, limiting researchers' ability to compare, validate, and trust synthetic datasets across different domains. This systematic review underscores the urgent need for robust, standardized evaluation approaches that not only bolster confidence in SD but also guide its effective and responsible implementation. By laying the groundwork for establishing domain-specific yet interoperable standards, this scoping review paves the way for future initiatives aimed at enhancing the role of SD in scientific discovery, clinical practice and beyond.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqag012"},"PeriodicalIF":3.0,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12891913/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146182759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inferring context-specific site variation with evotuned protein language models.","authors":"Spyros Lytras, Adam Strange, Jumpei Ito, Kei Sato","doi":"10.1093/nargab/lqag018","DOIUrl":"10.1093/nargab/lqag018","url":null,"abstract":"<p><p>Multiple sequence alignments (MSAs) have been traditionally used for making inferences about site-specific diversity in proteins. Recent advancements in the field of artificial intelligence have highlighted the potential of protein language models (pLMs) to capture similar protein properties. Unlike MSAs, pLMs can make inferences from single sequences, without the need for a set of aligned sequences. In this study, we introduce a variation of the Context-Dependent Entropy metric, based on pLM embeddings instead of MSA input, to assess protein site conservation and variability. We test this metric using versions of two popular pLMs (ESM-2 and protT5) fine-tuned on the diversity of different Influenza A virus subtype hemagglutinin proteins. Our study demonstrates how our pLM entropy metric can capture which sites are more likely to change in a specific sequence context and how fine-tuning pLMs on a set of evolutionarily related proteins (evotuning) can improve the models' understanding of the group's diversity.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqag018"},"PeriodicalIF":3.0,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12884077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146158737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive annotation of conserved protein domains in human endogenous retroviruses.","authors":"Tomàs Montserrat-Ayuso, Aurora Pujol, Anna Esteve-Codina","doi":"10.1093/nargab/lqag013","DOIUrl":"10.1093/nargab/lqag013","url":null,"abstract":"<p><p>Human endogenous retroviruses (HERVs) occupy nearly 8% of the human genome, yet their protein-coding potential remains largely unexplored. Originating from ancestral retroviruses that infected germline cells, HERVs typically follow the canonical proviral structure LTR-gag-pol-env-LTR, where gag, pol, and env encode structural, enzymatic, and envelope proteins. We present a comprehensive resource annotating conserved retroviral domains across 120 000 + ORFs derived from internal HERV regions. Using a reproducible pipeline based on HMMER and InterProScan, we identified over 17 000 domain hits-primarily from pol genes such as reverse transcriptase, RNase H, and protease-and quantified their structural conservation. Hundreds of domains exceed 95% alignment coverage, revealing a surprising abundance of full-length retrovirus-like domains in both young and ancient families. The HERVK (HML-2) subfamily retains the most complete polyprotein architecture, including 13 loci with nearly intact Gag, Pol, and Env, but full-length Pol domains are also found in HERVH, HERVW, and HERVE. Our annotations recover conserved catalytic motifs in Pol and transmembrane features in Env, enabling fine-grained functional interpretation. All results-including BED, FASTA, domain sequences, InterProScan outputs, and transmembrane predictions-are provided as an open resource at Zenodo to support downstream analyses of HERV protein expression, immune modulation, and co-option in health and disease.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqag013"},"PeriodicalIF":3.0,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12884080/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146158818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ANOMALY: a Snakemake pipeline for identifying NuMTs from long-read sequencing data.","authors":"Nirmal S Mahar, Rachit Singh, Ishaan Gupta, Shweta Ramdas","doi":"10.1093/nargab/lqag014","DOIUrl":"10.1093/nargab/lqag014","url":null,"abstract":"<p><p>Nuclear mitochondrial DNA segments (NuMTs) can contribute to cancer development and disease progression by disrupting protein-coding genes. Furthermore, their presence confounds mitochondrial variant detection, underscoring the critical need for robust NuMT detection. Current methods to call NuMTs rely on short-read sequencing data but struggle to resolve complex NuMTs. These limitations can be overcome by employing long-read sequencing data. However, no such workflow exists to capture NuMTs from long-read sequencing data. Here, we introduce ANOMALY, a novel, easy-to-use workflow for detecting NuMTs from long-read sequencing data. The pipeline takes raw sequencing or aligned data and calls and visualises sample NuMTs. On 50 simulated datasets, the pipeline demonstrated high accuracy, with a precision of 1.000, a recall of 0.989, and an F1-score of 0.994. The pipeline underscores the limitations of short-read data in resolving and capturing complex NuMTs while demonstrating that long-read data enables their accurate identification. The Snakemake pipeline employs Python, Bash and R and is published under an open-source GNU GPL v3 license. Detailed information on setting up and running the pipeline, along with the source code, is available at https://github.com/Nirmal2310/ANOMALY.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqag014"},"PeriodicalIF":3.0,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12869244/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146126664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A highly resolved integrated single-cell atlas of human breast cancers.","authors":"Andrew Chen, Lina Kroehling, Christina S Ennis, Gerald V Denis, Stefano Monti","doi":"10.1093/nargab/lqaf217","DOIUrl":"10.1093/nargab/lqaf217","url":null,"abstract":"<p><p>In this study, we developed an integrated single-cell transcriptomic (scRNAseq) atlas of human breast cancer (BC), the largest resource of its kind, totaling >600 000 cells across 138 patients. Rigorous integration and annotation of publicly available scRNAseq data enabled a highly resolved characterization of epithelial, immune, and stromal heterogeneity within the tumor microenvironment (TME). Within the immune compartment, we were able to characterize heterogeneity of CD4, CD8 T cells, and macrophage subpopulations. Within the stromal compartment, subpopulations of endothelial cells (ECs) and cancer-associated fibroblasts (CAFs) were resolved. Within the cancer epithelial compartment, we characterized the functional heterogeneity of cells across the axes of stemness, epithelial-mesenchymal plasticity, and canonical cancer pathways. Across all subpopulations observed in the TME, we performed a multi-resolution survival analysis to identify epithelial cell states and immune and stromal cell types, which conferred a survival advantage in both The Cancer Genome Atlas (TCGA), METABRIC, and SCANB. We also identified robust associations between TME composition and clinical phenotypes such as tumor subtype and grade that were not discernible when the analysis was limited to individual datasets, highlighting the need for atlas-based analyses. This atlas represents a valuable resource for further high-resolution analyses of TME heterogeneity within BC.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqaf217"},"PeriodicalIF":3.0,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12867518/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146120338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federated learning frameworks: quality and interoperability for biomedical research.","authors":"María Chavero-Diez, Carles Hernandez-Ferrer, Laia Codó, Josep Ll Gelpí, Salvador Capella-Gutiérrez","doi":"10.1093/nargab/lqag010","DOIUrl":"10.1093/nargab/lqag010","url":null,"abstract":"<p><p>This review examines the current landscape of federated learning frameworks to evaluate their long-term sustainability, flexibility, and usability in biomedical research, where strict data regulations limit data sharing across institutions. Through a systematic literature analysis, the study assesses these frameworks against findability, accessibility, interoperability, and reusability for research software principles and compares reported use cases to framework functionalities to identify gaps in usability and scalability. The findings reveal that while most frameworks perform well in findability and reusability, they exhibit limited interoperability both among themselves and with specific software libraries. Although often developed for particular use cases, the technical foundations of these frameworks suggest potential for broader applicability. However, the scarce integration of privacy-preserving techniques and a predominant reliance on horizontal architectures may constrain their scalability in more complex federated learning scenarios. Ultimately, this analysis highlights the necessity for federated learning frameworks to evolve toward greater interoperability, flexibility, and privacy-awareness.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqag010"},"PeriodicalIF":3.0,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12862364/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146114482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}