NAR Genomics and Bioinformatics最新文献_第10页

nf-core/crisprseq: a versatile pipeline for comprehensive analysis of CRISPR gene editing and screening assays. nf-core/crisprseq：用于CRISPR基因编辑和筛选分析的综合分析的多功能管道。

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NAR Genomics and Bioinformatics Pub Date : 2026-01-15 eCollection Date: 2026-03-01 DOI: 10.1093/nargab/lqaf214

Júlia Mir-Pedrol, Laurence Kuhlburger, Marta Sanvicente-García, Metin Yazar, Colm J Ryan, Sabrina Krakau, Gisela Gabernet, Marc Güell, Matteo Bonfanti, Nf-Core Community, Sven Nahnsen

{"title":"nf-core/crisprseq: a versatile pipeline for comprehensive analysis of CRISPR gene editing and screening assays.","authors":"Júlia Mir-Pedrol, Laurence Kuhlburger, Marta Sanvicente-García, Metin Yazar, Colm J Ryan, Sabrina Krakau, Gisela Gabernet, Marc Güell, Matteo Bonfanti, Nf-Core Community, Sven Nahnsen","doi":"10.1093/nargab/lqaf214","DOIUrl":"10.1093/nargab/lqaf214","url":null,"abstract":"In recent years, CRISPR technology has become widely applied in scientific research, being simpler, cheaper, and more precise than previous gene-editing techniques. This editing technology can be used for various applications, such as gene knockout, gene knock-in, CRISPR activation (CRISPRa), CRISPR interference (CRISPRi), CRISPR screens, base editing, and prime editing. The share of pipelines to analyze the variety of CRISPR editing methods is low, and until now, none of them caters to both gene editing and CRISPR-based functional genomics. Here, we introduce nf-core/crisprseq, a Nextflow DSL2 pipeline for the assessment of CRISPR gene editing and screening assays. The workflow is written in a modularized fashion to allow the easy incorporation of new steps. nf-core/crisprseq is the first generic pipeline enabling the analysis of the broad spectrum of CRISPR designs. We show the performance and usability of the software using publicly available datasets.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqaf214"},"PeriodicalIF":3.0,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12805889/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145999206","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

SyMetrics: an integrated machine learning model for evaluating the pathogenicity of synonymous variants in the human genome. SyMetrics：一个综合的机器学习模型，用于评估人类基因组中同义变异的致病性。

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NAR Genomics and Bioinformatics Pub Date : 2026-01-15 eCollection Date: 2026-03-01 DOI: 10.1093/nargab/lqaf211

Linnaeus Bundalian, Martina Schmidt Strnadová, Felix Garten, Susanne Horn, Udo Stenzel, Denny Popp, Johannes R Lemke, Saskia Biskup, Björn Schulte, Patrick May, Frank Bösebeck, Antje Garten, Doreen Thor, Angela Schulz, Julia Hentschel, Janet Kelso, Torsten Schöneberg, Diana Le Duc

{"title":"SyMetrics: an integrated machine learning model for evaluating the pathogenicity of synonymous variants in the human genome.","authors":"Linnaeus Bundalian, Martina Schmidt Strnadová, Felix Garten, Susanne Horn, Udo Stenzel, Denny Popp, Johannes R Lemke, Saskia Biskup, Björn Schulte, Patrick May, Frank Bösebeck, Antje Garten, Doreen Thor, Angela Schulz, Julia Hentschel, Janet Kelso, Torsten Schöneberg, Diana Le Duc","doi":"10.1093/nargab/lqaf211","DOIUrl":"10.1093/nargab/lqaf211","url":null,"abstract":"Synonymous single nucleotide variants (sSNVs), traditionally seen as neutral, are now recognized for their biological impact. To assess their relevance, we developed SyMetrics, a framework that integrates predictors of splicing, RNA stability, evolutionary conservation, codon usage, synonymous variation effects, sequence properties, and allele frequency. We analyzed all possible sSNVs across the human genome, and our machine-learning model achieved 97% accuracy in distinguishing deleterious from benign variants, with a ROC-AUC of 0.89, outperforming individual predictors. Our estimates indicate that about 1.98 ± 0.17% of sSNVs absent from population databases are damaging (roughly 900 000 sSNVs), with an odds ratio of 3.87 for deleteriousness compared to common sSNVs (P < 0.05). To validate predictions, we performed functional assays on selected sSNVs in the AVPR2 gene and additionally used available large scale mutagenesis screens of RAD51C and BAP1 variants. In a clinical cohort, we identified 15 predicted deleterious sSNVs in genes linked to patient phenotypes; 9 were classified as (likely) pathogenic while 6 were variants of uncertain significance (VUS) per American College of Medical Genetics guidelines. For three VUS, segregation data supported their suspected inheritance patterns (de novo, X-linked). Our findings underscore the functional importance of sSNVs. To support further research and clinical applications, we provide a Python package and web application (https://symetrics.org/) for evaluating these variants comprehensively.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqaf211"},"PeriodicalIF":3.0,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12805901/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145999179","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

FLYNC: a machine-learning-driven framework for discovering long noncoding RNAs in Drosophila melanogaster. FLYNC：发现果蝇长链非编码rna的机器学习驱动框架。

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NAR Genomics and Bioinformatics Pub Date : 2026-01-15 eCollection Date: 2026-03-01 DOI: 10.1093/nargab/lqaf216

Ricardo F Dos Santos, Tiago Baptista, Graça S Marques, Catarina C F Homem

{"title":"FLYNC: a machine-learning-driven framework for discovering long noncoding RNAs in Drosophila melanogaster.","authors":"Ricardo F Dos Santos, Tiago Baptista, Graça S Marques, Catarina C F Homem","doi":"10.1093/nargab/lqaf216","DOIUrl":"10.1093/nargab/lqaf216","url":null,"abstract":"Noncoding RNAs have increasingly recognized roles in critical molecular mechanisms of disease. However, the noncoding genome of Drosophila melanogaster, one of the most powerful disease model organisms, has been understudied. Here, we present FLYNC-FLY noncoding RNA discovery and classification-a novel explainable boosting machine model that accurately predicts the probability of a newly identified RNA transcript being a long noncoding RNA (lncRNA). Integrated into an end-to-end bioinformatics pipeline capable of processing single cell or bulk RNA sequencing data, FLYNC outputs potential new noncoding RNA genes. FLYNC leverages large-scale genomic and transcriptomic datasets to identify patterns and features that distinguish noncoding genes from protein-coding genes, thereby facilitating lncRNA prediction. We demonstrate the application of FLYNC to publicly available Drosophila adult head bulk transcriptome and single-cell transcriptomic data from Drosophila neural stem cell lineages and identify several novel tissue- and cell-specific lncRNAs. We have further experimentally validated the existence of a set of FLYNC predicted lncRNAs by RT-PCR and RNA PolII binding. Overall, our findings demonstrate that FLYNC serves as a robust tool for identifying lncRNAs in D. melanogaster, transcending current limitations in ncRNA identification and harnessing the potential of machine learning.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqaf216"},"PeriodicalIF":3.0,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12805895/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145999237","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

RNA-KG v2.0: an RNA-centered Knowledge Graph with Properties. RNA-KG v2.0：一个以rna为中心的带有属性的知识图谱。

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NAR Genomics and Bioinformatics Pub Date : 2026-01-12 eCollection Date: 2026-03-01 DOI: 10.1093/nargab/lqaf194

Emanuele Cavalleri, Paolo Perlasca, Marco Mesiti

{"title":"RNA-KG v2.0: an RNA-centered Knowledge Graph with Properties.","authors":"Emanuele Cavalleri, Paolo Perlasca, Marco Mesiti","doi":"10.1093/nargab/lqaf194","DOIUrl":"10.1093/nargab/lqaf194","url":null,"abstract":"RNA-KG is a recently developed biomedical knowledge graph that integrates the interactions involving coding and non-coding RNA molecules extracted from public data sources. It can be used to support the classification of new molecules, identify new interactions through the use of link prediction methods, and reveal hidden patterns among the represented entities. In this paper, we propose RNA-KG v2.0, a new release of RNA-KG that integrates around [Formula: see text] manually curated interactions sourced from 91 linked open data repositories and ontologies. Relationships are characterized by standardized properties that capture the specific context (e.g. cell line, tissue, pathological state) in which they have been identified. In addition, the nodes are enriched with detailed attributes, such as descriptions, synonyms, and molecular sequences sourced from platforms such as OBO ontologies, NCBI repositories, RNAcentral, and Ensembl. The enhanced repository enables the expression of advanced queries that take into account the context in which the experiments were conducted. It also supports downstream applications in RNA research, including 'context-aware' link prediction techniques that combine both topological and semantic information. Finally, the recent integration of RNA-KG relationships into the RNAcentral portal provides a powerful resource for linking RNA-centric relationships with non-coding gene expression in human tissues, RNA secondary structures, and their functional roles in biological pathways, which can accelerate the discovery of novel therapeutic targets.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqaf194"},"PeriodicalIF":3.0,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12791124/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145966838","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

OmNI: a modular open-source framework for interactive multi-omics data integration and visualization. OmNI：用于交互式多组学数据集成和可视化的模块化开源框架。

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NAR Genomics and Bioinformatics Pub Date : 2026-01-10 eCollection Date: 2026-03-01 DOI: 10.1093/nargab/lqaf206

Grace Potter, Jacob A Beierle, Camron Bryant, Sadhna Phanse, Carl White, Andrew Emili, Indranil Paul

{"title":"OmNI: a modular open-source framework for interactive multi-omics data integration and visualization.","authors":"Grace Potter, Jacob A Beierle, Camron Bryant, Sadhna Phanse, Carl White, Andrew Emili, Indranil Paul","doi":"10.1093/nargab/lqaf206","DOIUrl":"10.1093/nargab/lqaf206","url":null,"abstract":"Omics Notebook Interactive (OmNI) is an R-based, open-source, and modular framework engineered for streamlined multi-omics data integration and analysis across diverse data types, incorporating interactive visualizations at each processing step. OmNI performs differential expression analysis utilizing customizable linear models, accommodating various covariates and complex experimental designs. For cross-omic layer integration, OmNI employs a modified S-score statistic, ensuring sensitive detection of differential features. The framework also integrates network and metabolomics data, offering detailed insights into regulatory mechanisms through comprehensive enrichment analysis using multiple pathway databases. Outputs include interactive HTML reports, CSV/TSV files, and Cytoscape-compatible objects. OmNI is readily deployable in both local and high-performance computing environments, enabling scalable data processing. Acknowledging the public health concerns of opioids, we performed TMT18-based deep proteome and phosphoproteome analysis of brains from genetically diverse collaborative cross diversity outbred (CC/DO) founder mouse strains exposed to fentanyl to demonstrate OmNI's capabilities. The integrative S-score uniquely identified differential signaling and interaction hubs conserved across all strains and revealed strain-specific molecular neuro-responses to fentanyl. OmNI is freely available for download at https://github.com/gracerhpotter/OmNI and is also accessible via a web interface at https://emili-laboratory.shinyapps.io/omni/.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqaf206"},"PeriodicalIF":3.0,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12789801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145953079","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

Structural analysis of uridine modifications in solved RNA structures. 已解RNA结构中尿苷修饰的结构分析。

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NAR Genomics and Bioinformatics Pub Date : 2026-01-10 eCollection Date: 2026-03-01 DOI: 10.1093/nargab/lqaf197

Sebastian J Arteaga, Brent M Znosko

{"title":"Structural analysis of uridine modifications in solved RNA structures.","authors":"Sebastian J Arteaga, Brent M Znosko","doi":"10.1093/nargab/lqaf197","DOIUrl":"10.1093/nargab/lqaf197","url":null,"abstract":"Naturally occurring uridine modifications in RNA play critical roles in modulating RNA stability, translation, and immune responses. While detection methods have advanced, a comprehensive structural analysis across experimentally determined RNA 3D structures remains limited. In this study, we systematically examined six uridine modifications-pseudouridine (PSU), 5-methyluridine (5MU), 3-methyluridine (UR3), O2'-methyluridine (OMU), 4-thiouridine (4SU), and 5,6-dihydrouridine (H2U)-using data from the Research Collaboratory for Structural Bioinformatics Protein Data Bank. After curation, we identified 2982 PSU, 736 5MU, 232 UR3, 429 OMU, 314 4SU, and 171 H2U residues across RNA-containing structures. These modifications were primarily found in ribosomal and transfer RNAs, often localized within hairpin secondary structures. Sugar pucker analysis revealed modification-specific preferences for C3'-endo and C2'-endo conformations. To assess structural impacts, we generated sequence-representative structures and compared modified versus unmodified forms using all-atom root-mean-square deviation analysis. Most modifications showed high similarity to their unmodified counterparts (RMSD [Formula: see text] 1.0 Å), though deviations were notable for certain PSU-, 5MU-, and 4SU-containing motifs. Despite overall similarity, interaction differences were observed between modified and canonical uridines. This work provides a detailed structural overview of uridine modifications, offering insights into their conformational behavior and implications for RNA function. These findings may inform future efforts in RNA-targeted therapeutics and structural biology.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqaf197"},"PeriodicalIF":3.0,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12789803/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145953068","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

Very high CO₂ concentrations inhibit photosynthesis and trigger transcriptomic stress responses in cyanobacteria. 在蓝藻中，非常高的CO2浓度会抑制光合作用并触发转录组应激反应。

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NAR Genomics and Bioinformatics Pub Date : 2026-01-10 eCollection Date: 2026-03-01 DOI: 10.1093/nargab/lqaf207

Adrian Sven Geissler, Elena Carrasquer-Alvarez, Jan Gorodkin, Niels-Ulrik Frigaard, Stefan Ernst Seemann

{"title":"Very high CO2 concentrations inhibit photosynthesis and trigger transcriptomic stress responses in cyanobacteria.","authors":"Adrian Sven Geissler, Elena Carrasquer-Alvarez, Jan Gorodkin, Niels-Ulrik Frigaard, Stefan Ernst Seemann","doi":"10.1093/nargab/lqaf207","DOIUrl":"10.1093/nargab/lqaf207","url":null,"abstract":"CO[Formula: see text] concentrations above air level (0.04%) are beneficial for the growth of cyanobacteria. However, very high CO[Formula: see text] levels inhibit growth, limiting the usability of cyanobacteria for carbon capture and biotechnological applications. The transcriptomic changes in the cyanobacterium Synechococcus sp. PCC 7002 were investigated at varying growth rates governed by different gas-phase CO[Formula: see text] concentrations, ranging from limiting (0.04%) to optimal (4% and 8%) to inhibitory high (30%). Compared to optimal CO[Formula: see text] concentrations, large differences in the transcriptome were observed in limiting and inhibiting CO[Formula: see text]. At 30% CO[Formula: see text], genes encoding CO[Formula: see text] uptake mechanisms, photosynthetic electron transfer, and light-harvesting antennae proteins were down-regulated compared to lower CO[Formula: see text]. Genes involved in the ribosomal machinery and biosynthetic pathways were down-regulated at 30% and 0.04% CO[Formula: see text], consistent with the observed reduced growth. The genes most strongly up-regulated at 30% CO[Formula: see text] were primarily associated with stress responses, but did not closely resemble the transcriptomic changes at other stress conditions previously described. The small RNA PsrR1 was strongly up-regulated at 30% CO[Formula: see text] and is likely to be involved in the regulation of growth. These transcriptomic insights are essential for the engineering of fast-growing cyanobacteria at high CO[Formula: see text], which can be applied in carbon capture from industrial point sources.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqaf207"},"PeriodicalIF":3.0,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12789804/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145953123","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

DNA methylation profiles aid to identify putative genome activation histories along lymphomagenesis. DNA甲基化谱有助于确定沿淋巴瘤发生推定的基因组激活历史。

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NAR Genomics and Bioinformatics Pub Date : 2026-01-10 eCollection Date: 2026-03-01 DOI: 10.1093/nargab/lqaf187

Leone Albinati, Irene D'Onofrio, Rabia Gül Aydin, Houyem Toukabri, Renée Beekman

{"title":"DNA methylation profiles aid to identify putative genome activation histories along lymphomagenesis.","authors":"Leone Albinati, Irene D'Onofrio, Rabia Gül Aydin, Houyem Toukabri, Renée Beekman","doi":"10.1093/nargab/lqaf187","DOIUrl":"10.1093/nargab/lqaf187","url":null,"abstract":"DNA methylation (DNAm) is widely used to leverage biological information in the context of cancer. Active regulatory elements marked by increased chromatin accessibility and specific transcription factor (TF) binding, such as enhancers, show tumor-specific DNAm patterns reflecting the biological forces shaping tumorigenesis. However, DNAm changes also occur at regions that are inactive at histone-mark level in fully developed tumors. Beyond hypermethylation of promoters, these changes are often overlooked, leaving their functional relevance poorly understood. By analyzing DNAm and chromatin state annotations from conventional mantle cell lymphoma (cMCL), we identified a subset of ~300 CpGs with homogeneous cMCL-specific demethylation patterns located in cMCL-inactive regions. We show that these regions contain cMCL-related TF motifs and are flanked by genes with cMCL-specific expression patterns, suggesting a potential regulatory role in lymphomagenesis. We hypothesize that these regions represent DNA demethylation imprints of genome activation prior to full-blown tumor formation, either present in its cell type of origin (COO) or during early stages of tumor formation. Altogether, we put forward a new, DNAm-centered approach to gain insights into potential early tumorigenic events, allowing us to generate novel, further explorable hypotheses to better understand the features playing a role in lymphomagenesis and beyond.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqaf187"},"PeriodicalIF":3.0,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12789802/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145953089","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

Insights into the architecture of earthworm metallothionein genes, powered by long-read genomics and transcriptomics. 深入了解蚯蚓金属硫蛋白基因结构，由长读基因组学和转录组学提供动力。

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NAR Genomics and Bioinformatics Pub Date : 2026-01-08 eCollection Date: 2026-03-01 DOI: 10.1093/nargab/lqaf195

Maxim A Karpov, Stephen Short, Peter Kille, Carl Hobbs, Amaia Green Etxabe, David J Spurgeon, Stephen R Stürzenbaum

{"title":"Insights into the architecture of earthworm metallothionein genes, powered by long-read genomics and transcriptomics.","authors":"Maxim A Karpov, Stephen Short, Peter Kille, Carl Hobbs, Amaia Green Etxabe, David J Spurgeon, Stephen R Stürzenbaum","doi":"10.1093/nargab/lqaf195","DOIUrl":"10.1093/nargab/lqaf195","url":null,"abstract":"The metal-handling metallothionein gene family is subjected to a strong selection pressure in earthworms inhabiting metalliferous soils. The resultant single nucleotide polymorphisms and structural variants within metallothionein's characteristically short exons challenge standard gene prediction and genome assembly tools and complicate phylogenetic analysis of earthworm metallothioneins (wMTs). Here, the genomic origin of wMTs was defined by sequencing four wMT-containing Bacterial Artificial Chromosomes (BACs). This yielded sequence contigs of lengths from 97 to 123 kb and enabled further genetic analyses, supporting the creation of a BLAST-based software for visualizing long-read RNA-seq alignments-lrRNAseq TAST (Total Alignment Search Tool). Phylogenetic analysis of wMT proteins from long-read RNA-seq data grouped wMTs into three distinct clades. Tissue-specific lrRNAseq and metallomic mapping, conducted in context of lead toxicity, revealed enrichment of wMT-1 and wMT-2 in tissues of heavy metal detoxification, and colocalization of wMT with lead in the chloragog. The generation of rare wMT isoforms was attributed to errors in expression, but the erroneous transcripts may possess a degree of order and purpose. A conserved region in the 3' UTR of wMT transcripts was found completely distinct between wMT homologues, allowing classification without inferring phylogenetic trees.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqaf195"},"PeriodicalIF":3.0,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12783039/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145953110","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

MUSICiAn: genome-wide identification of genes involved in DNA repair via control-free mutational spectra analysis. 音乐家：通过无控制突变谱分析参与DNA修复的基因全基因组鉴定。

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NAR Genomics and Bioinformatics Pub Date : 2026-01-08 eCollection Date: 2026-03-01 DOI: 10.1093/nargab/lqaf202

Colm Seale, Marco Barazas, Robin van Schendel, Marcel Tijsterman, Joana P Gonçalves

{"title":"MUSICiAn: genome-wide identification of genes involved in DNA repair via control-free mutational spectra analysis.","authors":"Colm Seale, Marco Barazas, Robin van Schendel, Marcel Tijsterman, Joana P Gonçalves","doi":"10.1093/nargab/lqaf202","DOIUrl":"10.1093/nargab/lqaf202","url":null,"abstract":"Understanding DNA double-strand break (DSB) repair is crucial for the development of targeted anticancer therapies, yet the roles of many genes remain unclear. Recent studies show that disruption of known DSB repair genes can alter the sequence-specific distribution of mutations arising after DSB repair, suggesting that genome-wide perturbation screens could be leveraged to identify new DSB genes leading to distinct deviations from the expected wild-type distribution. Given the challenges of designing controls for a genome-wide screen, we explore the high gene throughput to forgo the use of traditional controls by reframing the analysis as an outlier detection problem, assuming that most genes have minimal influence on DSB repair outcomes. We propose MUSICiAn (Mutational Signature Catalogue Analysis), a compositional data analysis method that ranks gene perturbation impact on mutational spectra without controls by measuring deviations from the central tendency considering the distribution of all spectra. We show that MUSICiAn effectively estimates pseudo-controls for the Repair-seq screen, yielding 476 genes and 60 nontargeting controls. We further apply MUSICiAn to the first genome-wide screen of 18 406 genes with mutational spectra readout, MUSIC, reporting that MUSICiAn successfully recovers known DSB repair genes, highlights the spliceosome as a lesser-appreciated player, and reveals candidates for further investigation.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqaf202"},"PeriodicalIF":3.0,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12783044/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145953051","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