NAR Genomics and Bioinformatics最新文献

Customizable host and viral transcript enrichment using CRISPR-Cas9 long-read sequencing for characterization of low-to-moderate abundance isoforms. 使用CRISPR-Cas9长读测序进行低至中等丰度亚型的表征，可定制宿主和病毒转录物富集。

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NAR Genomics and Bioinformatics Pub Date : 2026-09-15 eCollection Date: 2026-09-01 DOI: 10.1093/nargab/lqag111

An N T Nguyen, Jianshu Zhang, Shuxin Zhang, Miranda E Pitt, Devika Ganesamoorthy, Svenja Fritzlar, Jessie J-Y Chang, Sarah L Londrigan, Lachlan J M Coin

引用次数: 0

Unlocking cis-regulatory landscapes across 500 million years of evolution and disease mechanisms. 解开5亿年进化和疾病机制的顺式调控景观。

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NAR Genomics and Bioinformatics Pub Date : 2026-09-15 eCollection Date: 2026-09-01 DOI: 10.1093/nargab/lqag107

Tássia Mangetti Gonçalves, Casey L Stewart, Samantha D Baxley, Jason Xu, Kevin Boyer, Bijesh George, Daofeng Li, Chengran Yang, Harrison W Gabel, Xianhua Piao, Carlos Cruchaga, Yang E Li, Ting Wang, Oshri Avraham, Guoyan Zhao

{"title":"Unlocking cis-regulatory landscapes across 500 million years of evolution and disease mechanisms.","authors":"Tássia Mangetti Gonçalves, Casey L Stewart, Samantha D Baxley, Jason Xu, Kevin Boyer, Bijesh George, Daofeng Li, Chengran Yang, Harrison W Gabel, Xianhua Piao, Carlos Cruchaga, Yang E Li, Ting Wang, Oshri Avraham, Guoyan Zhao","doi":"10.1093/nargab/lqag107","DOIUrl":"10.1093/nargab/lqag107","url":null,"abstract":"Genomic DNA encodes regulatory information that determines where, when, and to what extent genes are expressed. Theoretically, we should be able to identify these transcriptional \"instructions\" by examining genomic DNA sequence alone, yet this has remained challenging. Here we present the Vertebrate Regulatory MOdule Detector (VRMOD), a method that accurately predicts gene regulatory sequences using only the query genomic sequences. We applied VRMOD to 309 Ensembl genomes, generating a compendium of high-resolution, genome-position-fixed cis-regulatory modules without parameter tuning. We performed extensive computational evaluation and experimental validation of VRMOD predictions. Notably, VRMOD predicted three sub-enhancers within the human hs52 enhancer at the FTO locus from the VISTA database, including one missed by existing methods. Using a chicken embryo system and 3D tissue imaging, we showed that each sub-enhancer exhibits restricted spatiotemporal activity within specific subsets of tissues where the full enhancer is active. We further demonstrated VRMOD's utility for identifying evolutionarily non-conserved enhancers, annotating regulatory sequences in non-model organisms, and identifying candidate disease-causal variants. Collectively, VRMOD provides a universal coordinate reference system for regulatory sequences across 309 vertebrate genomes and enables genome-wide annotation of non-coding regulatory elements in any vertebrate species using genomic sequence alone.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 3","pages":"lqag107"},"PeriodicalIF":3.0,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13575426/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148978226","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}

引用次数: 0

FANTASIA suite: a reproducible and configurable framework for embedding-based functional annotation of proteins. FANTASIA套件：一个可重复和可配置的框架，用于基于嵌入的蛋白质功能注释。

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NAR Genomics and Bioinformatics Pub Date : 2026-09-11 eCollection Date: 2026-09-01 DOI: 10.1093/nargab/lqag106

Francisco M Pérez-Canales, Àlex Domínguez-Rodríguez, Belén Carbonetto, Rosa Fernández, Ildefonso Cases, Ana M Rojas

{"title":"FANTASIA suite: a reproducible and configurable framework for embedding-based functional annotation of proteins.","authors":"Francisco M Pérez-Canales, Àlex Domínguez-Rodríguez, Belén Carbonetto, Rosa Fernández, Ildefonso Cases, Ana M Rojas","doi":"10.1093/nargab/lqag106","DOIUrl":"10.1093/nargab/lqag106","url":null,"abstract":"Embedding-based annotation transfer is increasingly used for protein function inference due to protein language models capture sequence, structural, and functional signals that may extend beyond conventional pairwise similarity. However, systematic application of these approaches requires control over model choice, reference composition, lookup parameters, evidence traceability, and output formats. We developed the FANTASIA suite, a configurable framework for embedding-based functional annotation of proteins. The suite combines a database-backed implementation for reproducible and extensible analyses with a portable flat-file implementation for rapid local annotation and pipeline integration. Using non-model and model-organism proteomes, we show that larger neighbourhood sizes remain practical for proteome-scale analyses and that taxonomy and sequence-identity filtering support leakage-aware benchmarking. We also compare the supported models with baseline methods through external CAFA5 evaluation and provide practical guidance based on empirical evidence variables. FANTASIA provides a controlled, scalable, and reproducible framework for extending functional annotation across the rapidly expanding diversity of sequenced organisms.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 3","pages":"lqag106"},"PeriodicalIF":3.0,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13563086/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148956644","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}

引用次数: 0

Long-read based detection of large copy number variants with potential functional significance using the ContextSV structural variant caller. 使用ContextSV结构变量调用者对具有潜在功能意义的大拷贝数变量进行基于长读的检测。

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NAR Genomics and Bioinformatics Pub Date : 2026-09-09 eCollection Date: 2026-09-01 DOI: 10.1093/nargab/lqag108

Jonathan Elliot Perdomo, Mian Umair Ahsan, Jasmine Akoto, James Bauer, Naiara Akizu, Kai Wang

{"title":"Long-read based detection of large copy number variants with potential functional significance using the ContextSV structural variant caller.","authors":"Jonathan Elliot Perdomo, Mian Umair Ahsan, Jasmine Akoto, James Bauer, Naiara Akizu, Kai Wang","doi":"10.1093/nargab/lqag108","DOIUrl":"10.1093/nargab/lqag108","url":null,"abstract":"Long-read sequencing enables improved detection of structural variants (SVs) in the human genome due to its substantially increased read lengths. However, currently widely used long-read SV callers primarily rely on alignment-based evidence, limiting their ability to detect large and complex SVs and potentially missing disease-relevant events. To address these limitations, we developed ContextSV, a framework that integrates alignment evidence with copy number predictions derived from sequencing coverage and single-nucleotide variant allele frequencies to improve SV detection, particularly for large copy number variants (CNVs). We additionally developed ContextScore, a machine learning-based classification model to assign SV confidence scores based on genomic context features and integrated it within ContextSV. Through benchmarking analyses on both simulated and real datasets, we demonstrate that ContextSV improves detection of large CNVs and inversions that may be missed by existing long-read SV callers. We further illustrate its utility by identifying and experimentally validating multiple large SVs in the KOLF2.1J reference stem cell line that were not detected by other methods. Collectively, our results demonstrate that ContextSV serves as a valuable complement to existing long-read SV detection approaches by improving sensitivity for large and clinically relevant SVs.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 3","pages":"lqag108"},"PeriodicalIF":3.0,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13554283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921090","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}

引用次数: 0

VicMAG, an open-source tool for visualizing circular metagenome-assembled genomes highlighting bacterial virulence and antimicrobial resistance. VicMAG是一个开源工具，用于可视化圆形宏基因组组装基因组，突出细菌毒力和抗菌素耐药性。

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NAR Genomics and Bioinformatics Pub Date : 2026-09-08 eCollection Date: 2026-09-01 DOI: 10.1093/nargab/lqag109

Yusuke Tsuda, Yasuhiro Tanizawa, Thi My Hanh Vu, Yosuke Nishimura, Masaki Shintani, Haruka Abe, Futoshi Hasebe, Ikuro Kasuga, Miki Nagao, Masato Suzuki

{"title":"VicMAG, an open-source tool for visualizing circular metagenome-assembled genomes highlighting bacterial virulence and antimicrobial resistance.","authors":"Yusuke Tsuda, Yasuhiro Tanizawa, Thi My Hanh Vu, Yosuke Nishimura, Masaki Shintani, Haruka Abe, Futoshi Hasebe, Ikuro Kasuga, Miki Nagao, Masato Suzuki","doi":"10.1093/nargab/lqag109","DOIUrl":"10.1093/nargab/lqag109","url":null,"abstract":"Bacterial pathogens spread in clinical and environmental settings, and mobile genetic elements (MGEs), such as plasmids and phages, mediate the transfer of virulence factor genes (VFGs) and antimicrobial resistance genes (ARGs) among bacterial communities. Metagenomic analysis of environmental and wastewater samples using highly accurate long-read sequencing technologies, such as Pacific Biosciences (PacBio) HiFi sequencing, provides valuable insights into monitoring the regional spread of VFGs and ARGs, including dissemination mediated by MGEs. No visualization tool is currently available for the comprehensive display of numerous resulting circular metagenome-assembled genomes (cMAGs) with functional gene annotations. Here, we developed visualization of circular metagenome-assembled genome (VicMAG), a visualization tool for highly complex cMAGs derived from long-read metagenome assemblies annotated using updated databases of VFGs, ARGs, and MGEs. Using 353 cMAGs from PacBio HiFi sequencing of a wastewater sample, we demonstrated the utility of VicMAG for metagenome visualization. VicMAG provides comprehensive, size-aware visualization of cMAGs representing bacterial chromosomes and plasmids, annotated with VFGs, ARGs, and phages. By simultaneously visualizing all cMAGs in a framework, VicMAG facilitates a holistic understanding of the distribution and genomic context of VFGs and ARGs across complex microbial communities. This tool supports integrated surveillance of bacteria associated with virulence and antimicrobial resistance across clinical, environmental, and One Health contexts.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 3","pages":"lqag109"},"PeriodicalIF":3.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13551042/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148913704","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}

引用次数: 0

Identifying fundamental gaps in functional metagenomics: a step towards unlocking microbiome research potential. 识别功能宏基因组学的基本差距：朝着释放微生物组研究潜力迈出的一步。

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NAR Genomics and Bioinformatics Pub Date : 2026-09-08 eCollection Date: 2026-09-01 DOI: 10.1093/nargab/lqag110

Sumeet K Tiwari, Andrea Telatin, Dipali Singh

{"title":"Identifying fundamental gaps in functional metagenomics: a step towards unlocking microbiome research potential.","authors":"Sumeet K Tiwari, Andrea Telatin, Dipali Singh","doi":"10.1093/nargab/lqag110","DOIUrl":"10.1093/nargab/lqag110","url":null,"abstract":"Incomplete functional annotation limits biological interpretation in microbiome studies and their translational potential. Poor annotation arises from multiple causes, with incomplete gene-protein-reaction mapping being one tractable yet under-examined contributor. We address this gap by developing a comprehensive hierarchical framework that systematically integrates gene families in UniRef, proteins in UniProt, and metabolic reactions in MetaCyc and BioCyc through UniProtKB accession, EC number, and Pfam-domain matching. Applied to a human gut metagenome dataset via HUMAnN3, our MetaCyc-based mapping recovers up to 2.3-fold more unique reaction identifiers than the default pipeline and increases reaction prevalence across samples from ≈32% to 52% core reactions, addressing the data sparsity that limits statistical and machine-learning applications in microbiome research. Biological plausibility for the tested functions was supported by positive and negative controls: gut-microbial hormone-metabolism reactions previously linked to this dataset were recovered, while vertebrate-specific hormone-metabolism reactions remained correctly undetected. These gains derive from systematic database integration alone, without predictive algorithms, indicating that a tractable, mapping-related component of functional dark matter and data sparsity in microbiome studies is directly addressable. Because Pfam- and BioCyc-derived mappings trade specificity for coverage, confidence in any individual reaction assignment depends on the supporting evidence tier and source database.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 3","pages":"lqag110"},"PeriodicalIF":3.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13551040/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148913696","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}

引用次数: 0

LAMBDA: a prophage detection benchmark for genomic language models. LAMBDA：基因组语言模型的噬菌体检测基准。

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NAR Genomics and Bioinformatics Pub Date : 2026-09-08 eCollection Date: 2026-09-01 DOI: 10.1093/nargab/lqag103

LeAnn M Lindsey, Nicole L Pershing, Keith Dufault-Thompson, Ho-Jin Gwak, Anisa Habib, Aaron Schindler, Arjun Rakheja, June L Round, W Zac Stephens, Anne J Blaschke, Hari Sundar, Xiaofang Jiang

{"title":"LAMBDA: a prophage detection benchmark for genomic language models.","authors":"LeAnn M Lindsey, Nicole L Pershing, Keith Dufault-Thompson, Ho-Jin Gwak, Anisa Habib, Aaron Schindler, Arjun Rakheja, June L Round, W Zac Stephens, Anne J Blaschke, Hari Sundar, Xiaofang Jiang","doi":"10.1093/nargab/lqag103","DOIUrl":"10.1093/nargab/lqag103","url":null,"abstract":"Transformer-based genomic sequence models represent an emerging frontier in computational biology. Yet, their embeddings have not yet shown the same level of predictive power as natural and protein language models, highlighting a gap between current implementations and theoretical promise. Existing benchmarks for DNA language models primarily focus on classifying regulatory elements in eukaryotic genomes, leaving open the fundamental question of whether these models learn sequence-level features across whole genomes. We introduce LAMBDA, a benchmark designed to rigorously evaluate genome language model embeddings through phage-bacteria sequence discrimination across four categories of increasing complexity: probing tasks, fine-tuning assessments, diagnostic tests, and genome-wide prophage detection. Our comprehensive analysis of current genomic language models provides insight into the importance of training data selection relative to model size, the need for domain-specific training, and the capabilities and limitations of genomic language models for detecting prophage sequences. This benchmark represents a challenging genomic annotation task in the bacterial domain and addresses a key computational problem with direct relevance to microbiology and medicine.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 3","pages":"lqag103"},"PeriodicalIF":3.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13551034/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148913665","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}

引用次数: 0

SURE-Pipe: a pipeline to compare genomes and extract shared and unique regions. SURE-Pipe：一个比较基因组和提取共享和独特区域的管道。

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NAR Genomics and Bioinformatics Pub Date : 2026-09-07 eCollection Date: 2026-09-01 DOI: 10.1093/nargab/lqag105

Infant Thomas, Abhishek B Kannur, Arya Sudheer, Debyani Samantray, Akshay Pramod Ware, Budheswar Dehury, Sandipan Chakraborty, Bobby Paul

{"title":"SURE-Pipe: a pipeline to compare genomes and extract shared and unique regions.","authors":"Infant Thomas, Abhishek B Kannur, Arya Sudheer, Debyani Samantray, Akshay Pramod Ware, Budheswar Dehury, Sandipan Chakraborty, Bobby Paul","doi":"10.1093/nargab/lqag105","DOIUrl":"10.1093/nargab/lqag105","url":null,"abstract":"Identification of unique and shared genomic regions between organisms has substantial translational potential for the development of marker-based diagnostic assays and sequence homology-driven taxonomic classification. An automated pipeline capable of performing genome comparisons at both the intra- and inter-species levels with minimal computational requirements can significantly advance genome-driven translational research. Species-specific genomic regions are particularly valuable for sequence-based species identification and for developing DNA amplification- or hybridization-based diagnostic assays. Here, we present SURE-Pipe, an automated and flexible pipeline for genome comparison and extraction of unique and shared genomic regions (https://github.com/BPaul-bioinfoLAB/SURE-Pipe). Benchmarking of this pipeline using simulated datasets demonstrated high accuracy for shared and unique region identification. Using the pairwise genome comparison module, six genome pairs from diverse microorganisms were analysed, and identified the unique and shared regions. In addition, the multigenome comparison module was applied to 96 genomes representing 24 Bacillus species and identified species-specific genomic regions. These regions were highly conserved among four strains of a species (>98% sequence identity) and exhibit little to no similarity with other species. Species-specific primers designed for all 24 Bacillus species showed no off-target amplification in in-silico polymerase chain reaction analysis, indicating their specificity. Overall, SURE-Pipe provides a robust and multipurpose framework for comparative genomics, and the outcomes can be used for species identification and the development of genome-based diagnostic approaches.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 3","pages":"lqag105"},"PeriodicalIF":3.0,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13548059/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148907090","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}

引用次数: 0

Proportionality-based association metrics in count compositional data. 在计数组成数据中基于比例的关联度量。

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NAR Genomics and Bioinformatics Pub Date : 2026-09-04 eCollection Date: 2026-09-01 DOI: 10.1093/nargab/lqag102

Kevin McGregor, Nneka Okaeme, Reihane Khorasaniha, Simona Veniamin, Juan Jovel, Richard Miller, Ramsha Mahmood, Morag Graham, Christine Bonner, Charles N Bernstein, Douglas L Arnold, Amit Bar-Or, Ruth Ann Marrie, Julia O'Mahony, Eluen Ann Yeh, Yinshan Zhao, Brenda Banwell, Emmanuelle Waubant, Natalie Knox, Gary Van Domselaar, Feng Zhu, Ali I Mirza, Helen Tremlett, Heather Armstrong

{"title":"Proportionality-based association metrics in count compositional data.","authors":"Kevin McGregor, Nneka Okaeme, Reihane Khorasaniha, Simona Veniamin, Juan Jovel, Richard Miller, Ramsha Mahmood, Morag Graham, Christine Bonner, Charles N Bernstein, Douglas L Arnold, Amit Bar-Or, Ruth Ann Marrie, Julia O'Mahony, Eluen Ann Yeh, Yinshan Zhao, Brenda Banwell, Emmanuelle Waubant, Natalie Knox, Gary Van Domselaar, Feng Zhu, Ali I Mirza, Helen Tremlett, Heather Armstrong","doi":"10.1093/nargab/lqag102","DOIUrl":"10.1093/nargab/lqag102","url":null,"abstract":"Compositional data comprise vectors that describe the constituent parts of a whole. Data arising from various -omics platforms such as 16S and RNA sequencing are compositional in nature. In this kind of data, correlations between features on raw counts have no meaningful interpretation. Metrics of proportionality were formulated to address this problem. However, an inherent bias arises when these metrics are calculated empirically on count-based measures due to variability in read depths. We quantify the bias introduced by empirically calculating proportionality-based association metrics in count data. Additionally, we propose a means of estimating these metrics within a logit-normal multinomial model in pursuit of more accurate estimates. The model-based estimates are shown to outperform empirical estimates in simulated data and are applied to a mouse embryonic stem cell single-cell sequencing dataset, as well as a pediatric-onset multiple sclerosis metagenomic dataset.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 3","pages":"lqag102"},"PeriodicalIF":3.0,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13542730/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148897842","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}

引用次数: 0

Scalable joint non-negative matrix factorization for paired single cell gene expression and chromatin accessibility data. 配对单细胞基因表达和染色质可及性数据的可扩展联合非负矩阵分解。

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NAR Genomics and Bioinformatics Pub Date : 2026-09-03 eCollection Date: 2026-09-01 DOI: 10.1093/nargab/lqag104

William Morgans, Andrew D Sharrocks, Mudassar Iqbal

{"title":"Scalable joint non-negative matrix factorization for paired single cell gene expression and chromatin accessibility data.","authors":"William Morgans, Andrew D Sharrocks, Mudassar Iqbal","doi":"10.1093/nargab/lqag104","DOIUrl":"10.1093/nargab/lqag104","url":null,"abstract":"Single-cell multi-modal technologies provide powerful means to simultaneously profile cellular states. These are now being employed to study gene regulatory mechanisms in a variety of biological systems. Tailored computational methods for integration and analysis of these data are much needed, with desirable properties in terms of efficiency-to cope with high dimensionality of the data, interpretability-for downstream biological discovery and hypothesis generation, and flexibility-to easily incorporate future modalities. Existing methods cover some but not all of the desirable properties for effective integration and analysis of these data. Here, we present a highly efficient method, q-intNMF, for representation and integration of single-cell multi-modal data using joint non-negative matrix factorization, which can facilitate discovery of linked regulatory topics in each modality. We provide thorough benchmarking using large publicly available datasets against five popular existing methods. q-intNMF performs comparably against the current state-of-the-art methods across a range of metrics. Additionally, q-intNMF provides advantages in terms of computational efficiency and interpretability of discovered regulatory topics in the original feature space. We illustrate this enhanced interpretability in providing insights into cell state changes associated with Alzheimer's disease. q-intNMF is available as a Python package with extensive documentation and use cases at https://github.com/wmorgans/quick_intNMF.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 3","pages":"lqag104"},"PeriodicalIF":3.0,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13539111/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148888720","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}

引用次数: 0