NAR Genomics and Bioinformatics最新文献_第6页

The circadian isoform landscape of mouse livers. 小鼠肝脏的昼夜节律异构体景观。

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NAR Genomics and Bioinformatics Pub Date : 2026-04-13 eCollection Date: 2026-06-01 DOI: 10.1093/nargab/lqag041

Alexander Zee, Dori Z Q Deng, Luciano DiTacchio, Christopher Vollmers

引用次数: 0

T-ChroNet: Time-aware chromatin network reconstruction to detect dynamic regulatory programs in longitudinal epigenetic dataset. T-ChroNet：时间感知的染色质网络重建，以检测纵向表观遗传数据集中的动态调控程序。

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NAR Genomics and Bioinformatics Pub Date : 2026-04-08 eCollection Date: 2026-06-01 DOI: 10.1093/nargab/lqag034

Stefano Di Giovenale, Ottavio Lischio, Clelia Cortile, Giacomo Corleone, Maurizio Fanciulli, Francesco Bonchi, Iros Barozzi

{"title":"T-ChroNet: Time-aware chromatin network reconstruction to detect dynamic regulatory programs in longitudinal epigenetic dataset.","authors":"Stefano Di Giovenale, Ottavio Lischio, Clelia Cortile, Giacomo Corleone, Maurizio Fanciulli, Francesco Bonchi, Iros Barozzi","doi":"10.1093/nargab/lqag034","DOIUrl":"10.1093/nargab/lqag034","url":null,"abstract":"Networks are widely applied to investigate relationships among individual components of complex biological systems. Recent application of biological networks, such as gene co-expression networks and gene regulatory networks, has been instrumental to define principles of transcriptional modulation in development and disease. However, computational methods that can embed the activity of cis-regulatory elements (CRE) into a network are still limited. Capturing temporal CRE activity within a network could help reveal regulatory programs involved in cell fate commitment and disease development. To address this, we present T-ChroNet (Time-aware Chromatin Network), a network-based method that models CRE as nodes and their temporal co-accessibility as edges. Through the detection of CRE sharing similar accessibility patterns over time, T-ChroNet allows the inference of putative upstream regulators and downstream biological pathways. We applied T-ChroNet to temporally-resolved CRE datasets, from both human and mouse, including chromatin accessibility (ATAC-seq) and histone post-translational modifications (H3K27ac ChIP-seq). T-ChroNet successfully recovered known regulators and enriched pathways for both modalities and species, while also uncovering novel putative factors and mechanisms regulating cell identity, organ development and disease progression.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag034"},"PeriodicalIF":3.0,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13069678/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147677089","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

SpNeigh: spatial neighborhood and differential expression analysis for high-resolution spatial transcriptomics. 高分辨率空间转录组学的空间邻域和差异表达分析。

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NAR Genomics and Bioinformatics Pub Date : 2026-04-08 eCollection Date: 2026-06-01 DOI: 10.1093/nargab/lqag039

Jinming Cheng, Pierce Kah Hoe Chow, Nan Liu

{"title":"SpNeigh: spatial neighborhood and differential expression analysis for high-resolution spatial transcriptomics.","authors":"Jinming Cheng, Pierce Kah Hoe Chow, Nan Liu","doi":"10.1093/nargab/lqag039","DOIUrl":"10.1093/nargab/lqag039","url":null,"abstract":"Spatial transcriptomics technologies such as Xenium, MERFISH, and Visium HD enable high-resolution profiling of gene expression while preserving tissue architecture. However, most computational methods for spatial analysis do not explicitly model local tissue context, such as boundaries, neighborhoods, or gradients. Here, we present SpNeigh (https://github.com/jinming-cheng/SpNeigh/), an R package for spatial neighborhood analysis and spatially aware differential expression modeling. SpNeigh includes tools for boundary detection, spatial neighborhood extraction, distance-based weighting, and gradient-based statistical testing. It supports both region-based differential expression and smooth spatial modeling using spline-based regression, along with a spatial enrichment index that identifies genes enriched near defined spatial features. We demonstrate the utility of SpNeigh across multiple platforms and tissues, including mouse brain, human breast cancer, and human liver, revealing intermediate populations at tissue interfaces, immune microenvironment differences, and spatially zonated gene expression patterns. SpNeigh offers a flexible and interpretable framework for dissecting spatial gene expression dynamics in complex tissues.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag039"},"PeriodicalIF":3.0,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13069690/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147677144","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

A birth certificate for data to improve findability, accountability, and traceability. 数据的出生证明，以提高可查找性、问责性和可追溯性。

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NAR Genomics and Bioinformatics Pub Date : 2026-04-08 eCollection Date: 2026-06-01 DOI: 10.1093/nargab/lqag037

Rongbin Li, Avisha Das, Yuntao Yang, Zhao Li, Na Hong, Hua Xu, Maryann Martone, W Jim Zheng

{"title":"A birth certificate for data to improve findability, accountability, and traceability.","authors":"Rongbin Li, Avisha Das, Yuntao Yang, Zhao Li, Na Hong, Hua Xu, Maryann Martone, W Jim Zheng","doi":"10.1093/nargab/lqag037","DOIUrl":"10.1093/nargab/lqag037","url":null,"abstract":"Data quality is the foundational to scientific research and the rapid advancement of artificial intelligence. Ensuring data quality, provenance, and reproducibility requires robust mechanisms for traceability and accountability from the moment data are created. We propose the concept of a Data Birth Certificate, a universal framework for identifying research data at creation with built-in provenance information, including time, location, and data creator. Unlike existing identifiers assigned at deposition, a Data Birth Certificate establishes immutable, origin-centered traceability that complements established principles of Findability, Accessibility, Interoperability, and Reusability (FAIR). By capturing essential metadata at data generation, Data Birth Certificates support reliable data tracking, accountability, and downstream information management without constraining how data are stored or reused. This perspective outlines the conceptual framework, distinguishes it from existing identifier systems, and discusses its potential role in strengthening research reproducibility and data stewardship across scientific domains.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag037"},"PeriodicalIF":3.0,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13069677/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147677065","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

Lancet2: Improved and accelerated somatic variant calling with joint multi-sample local assembly graphs. Lancet2：基于联合多样本局部装配图的改进和加速体细胞变体调用。

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NAR Genomics and Bioinformatics Pub Date : 2026-04-07 eCollection Date: 2026-06-01 DOI: 10.1093/nargab/lqag036

Rajeeva Lochan Musunuri, Bryan Zhu, Wayne E Clarke, William Hooper, Timothy Chu, Jennifer Shelton, André Corvelo, Dickson Chung, Shreya Sundar, Adam M Novak, Benedict Paten, Michael C Zody, Nicolas Robine, Giuseppe Narzisi

{"title":"Lancet2: Improved and accelerated somatic variant calling with joint multi-sample local assembly graphs.","authors":"Rajeeva Lochan Musunuri, Bryan Zhu, Wayne E Clarke, William Hooper, Timothy Chu, Jennifer Shelton, André Corvelo, Dickson Chung, Shreya Sundar, Adam M Novak, Benedict Paten, Michael C Zody, Nicolas Robine, Giuseppe Narzisi","doi":"10.1093/nargab/lqag036","DOIUrl":"10.1093/nargab/lqag036","url":null,"abstract":"Here, we present Lancet2, an open-source somatic variant caller designed to improve detection of small variants in short-read sequencing data. Lancet2 introduces significant enhancements, including: (i) Improved variant discovery and genotyping through partial order multiple sequence alignment of assembled haplotype contigs, and re-alignment of sample reads to the best supporting allele. (ii) Optimized somatic variant scoring with explainable machine learning models, leading to better somatic filtering throughout the sensitivity scale. (iii) Integration with Sequence Tube Map for enhanced visualization of variants with aligned sample reads in graph space. When benchmarked against enhanced two-tech truth sets generated using high-coverage short-read (Illumina) and long-read (Oxford Nanopore) data from four well characterized matched tumor/normal cell lines, Lancet2 outperformed other industry-leading tools in variant calling performance, especially for InDels. In addition, significant runtime performance improvements were observed compared to Lancet1 (∼10× speedup and 50% less peak memory usage), and most other state-of-the-art somatic variant callers (at least 2× speedup with eight cores or more), making Lancet2 an ideal tool for accurate and efficient somatic variant calling.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag036"},"PeriodicalIF":3.0,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13064521/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147677105","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

systemPipeR: a multipurpose workflow management system for reproducible data analysis. systemPipeR：一个多用途的工作流管理系统，用于重复数据分析。

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NAR Genomics and Bioinformatics Pub Date : 2026-04-02 eCollection Date: 2026-06-01 DOI: 10.1093/nargab/lqag032

Le Zhang, Daniela Cassol, Brendan Gongol, Thomas Girke

{"title":"systemPipeR: a multipurpose workflow management system for reproducible data analysis.","authors":"Le Zhang, Daniela Cassol, Brendan Gongol, Thomas Girke","doi":"10.1093/nargab/lqag032","DOIUrl":"10.1093/nargab/lqag032","url":null,"abstract":"Workflow management systems (WMS) are essential for creating and automating multi-step data analyses and ensuring the reproducibility of biological insights. Although numerous WMS solutions exist, few provide deep integration of command-line software with the R and Bioconductor ecosystems, where a substantial portion of statistical modeling and downstream scientific analysis is performed by a large user base. systemPipeR addresses this gap by offering a unified environment that links R-based analytical steps with command-line tools through a standardized workflow specification. It enables the design and execution of reproducible workflows on both local and high-performance computing systems, while allowing users to select the most appropriate R or command-line tool for each analysis step. The latest version introduces a fully redesigned architecture that streamlines workflow construction, execution, monitoring, and reporting. Key enhancements include a flexible workflow management class object, integration of the Common Workflow Language (CWL), formal declaration and standardized execution of both R and command-line steps, utilities for metadata management, and automated generation of scientific and technical reports. Together, these advances establish systemPipeR as a general-purpose R-based WMS for building and executing end-to-end workflows for reproducible analysis of complex data in genomics and other data-intensive fields. The software is distributed as a free open-source Bioconductor package (https://bioconductor.org/packages/systemPipeR).","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag032"},"PeriodicalIF":3.0,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13044936/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147623768","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

Correction to 'Eduomics: a Nextflow pipeline to simulate -omics data for education'. 更正“教育经济学：Nextflow管道模拟教育经济学数据”。

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NAR Genomics and Bioinformatics Pub Date : 2026-04-02 eCollection Date: 2026-06-01 DOI: 10.1093/nargab/lqag038

引用次数: 0

A sorghum-anchored pan-grass syntenic gene set in grasses. 禾草中以高粱为锚定的泛草合成基因集。

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NAR Genomics and Bioinformatics Pub Date : 2026-03-31 eCollection Date: 2026-06-01 DOI: 10.1093/nargab/lqag030

Henrique M Dias, Guilherme Y A Sagawa, Vladimir J Torres-Rodriguez, Ravi V Mural, James C Schnable, Marie-Anne Van Sluys

{"title":"A sorghum-anchored pan-grass syntenic gene set in grasses.","authors":"Henrique M Dias, Guilherme Y A Sagawa, Vladimir J Torres-Rodriguez, Ravi V Mural, James C Schnable, Marie-Anne Van Sluys","doi":"10.1093/nargab/lqag030","DOIUrl":"10.1093/nargab/lqag030","url":null,"abstract":"Understanding orthologous and homeologous relationships among genes informs how evolution and artificial selection have shaped plant genomes of related species to adapt to different niches and produce diverse phenotypes. Grasses are ecologically and economically important plants, and many genes are conserved within syntenic blocks across species. Here, we describe the Pan-Grass Syntenic Gene Set (PGSGS), a curated dataset of orthologous and homeologous relationships among 746 743 protein-coding genes from 17 grass genomes anchored to sorghum, including both major and orphan crops, as well as wild grasses. From this analysis, 344 230 genes (46%) were identified as syntelogs, with 27 567 sorghum genes linked to at least one syntelog in other species. Of these, 11 624 syntelogs form a conserved core present in at least 15 species. PGSGS-core syntelogs are enriched for regulatory and cellular functions, form densely interconnected GO networks, and exhibit reduced nucleotide diversity and elevated Tajima's D relative to nonsyntenic genes in ~400 sorghum accessions. Conversely, PACMAD-specific syntelogs (n = 2266) is a diverse set of clade-specific retained genes. This dataset provides a foundation for understanding genome evolution after polyploidy and encourages the integration of functional genetic and genomic information across grasses, fulfilling the original promise of the \"grasses as a single genetic system.\"","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag030"},"PeriodicalIF":3.0,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13036489/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147595280","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

G/C-ending and synonymous codon bias define functional translational programs that shape human tissue and cancer proteomes. G/ c末端和同义密码子偏差定义了塑造人体组织和癌症蛋白质组的功能翻译程序。

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NAR Genomics and Bioinformatics Pub Date : 2026-03-27 eCollection Date: 2026-06-01 DOI: 10.1093/nargab/lqag033

Sherif Rashad, Kuniyasu Niizuma

{"title":"G/C-ending and synonymous codon bias define functional translational programs that shape human tissue and cancer proteomes.","authors":"Sherif Rashad, Kuniyasu Niizuma","doi":"10.1093/nargab/lqag033","DOIUrl":"10.1093/nargab/lqag033","url":null,"abstract":"Codon usage bias is a fundamental feature of the genetic code, yet its impact on messenger RNA translation is incompletely defined. Here, we integrate comparative genomics, human tissue proteomes, large cancer cell line, and patient cancer datasets to reveal a conserved codon-bias axis. Across mammals, we show that GC-biased gene conversion drives human-specific GC3 (third codon nucleotide bias score) drifts, yet the functional dichotomy is maintained: A/T-ending codons associate with proliferation and RNA processing, while G/C-ending (Third nucleotide Guanine or Cytosine) codons associate with differentiation and neuronal functions. At the isoacceptors level, synonymous codons segregate into distinct functional categories. To mechanistically connect codon usage to cancer, we introduce the ANN- and m7G-indices, capturing codons decoded by transfer RNA (tRNA) modifications t6A and m7G. Both indices negatively correlate with GC3 and enrich for pro-oncogenic proliferative pathways. Human tissue proteomes reveal strong codon bias discordance between RNA and protein levels, with nervous system tissues enriched for G/C-ending codons while proliferative organs are A/T-biased. Analysis of 2600 cancer cell lines and 21 cancer types revealed heterogeneous codon preferences in cancer cell lines but a global A/T-ending shift in human cancer-upregulated proteins. These findings establish synonymous codon divergence and tRNA modification indices as key determinants of translational reprogramming in health and cancer.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag033"},"PeriodicalIF":3.0,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13023039/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147575455","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

Differential gene regulatory network analysis reveals transcriptional disruption in opioid. 差异基因调控网络分析揭示阿片样物质的转录中断。

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NAR Genomics and Bioinformatics Pub Date : 2026-03-27 eCollection Date: 2026-06-01 DOI: 10.1093/nargab/lqag031

Jianlan Ren, Xu Wang, Feiyang Luan, Yeqing Chen, Le Gao, Wenzhe Ho, Zhi Wei

{"title":"Differential gene regulatory network analysis reveals transcriptional disruption in opioid.","authors":"Jianlan Ren, Xu Wang, Feiyang Luan, Yeqing Chen, Le Gao, Wenzhe Ho, Zhi Wei","doi":"10.1093/nargab/lqag031","DOIUrl":"10.1093/nargab/lqag031","url":null,"abstract":"Gene regulatory networks (GRNs) inferred from single-cell data offer a powerful lens for dissecting transcriptional regulation across biological conditions. Yet, statistical methods for comparing TF-level binary regulatory matrices-where edges represent the presence or absence of regulation-remain underdeveloped. Here, we introduce and benchmark five complementary statistical tests for group-level comparison of TF-level binary regulatory matrices. These include three global methods-a U-statistic-based dissimilarity test (Global_U), a distance-based pseudo-[Formula: see text] test (Global_F), and a PCA-based test-as well as two feature-level approaches: a per-feature U-test (Local_U) and Fisher's exact test. Through extensive simulations spanning sparse, coordinated, balanced, and noisy signal structures, we show that global methods consistently outperform in detecting distributed regulatory shifts, particularly under correlation or noise. Applying this framework to single-nucleus RNA-seq data from human brain donors, we uncover astrocyte-specific regulatory alterations linked to opioid exposure. While each method captures distinct signal types, our results underscore the value of combining global and local tests to enhance sensitivity and interpretability. This unified framework provides a robust statistical foundation for GRN-based comparisons in single-cell studies.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag031"},"PeriodicalIF":3.0,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13126120/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147821739","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