NAR Genomics and Bioinformatics最新文献_第4页

GINClus: RNA structural motif clustering using graph isomorphism network. GINClus：基于图同构网络的RNA结构基序聚类。

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NAR Genomics and Bioinformatics Pub Date : 2025-04-26 eCollection Date: 2025-06-01 DOI: 10.1093/nargab/lqaf050

Nabila Shahnaz Khan, Md Mahfuzur Rahaman, Shaojie Zhang

{"title":"GINClus: RNA structural motif clustering using graph isomorphism network.","authors":"Nabila Shahnaz Khan, Md Mahfuzur Rahaman, Shaojie Zhang","doi":"10.1093/nargab/lqaf050","DOIUrl":"10.1093/nargab/lqaf050","url":null,"abstract":"Ribonucleic acid (RNA) structural motif identification is a crucial step for understanding RNA structure and functionality. Due to the complexity and variations of RNA 3D structures, identifying RNA structural motifs is challenging and time-consuming. Particularly, discovering new RNA structural motif families is a hard problem and still largely depends on manual analysis. In this paper, we proposed an RNA structural motif clustering tool, named GINClus, which uses a semi-supervised deep learning model to cluster RNA motif candidates (RNA loop regions) based on both base interaction and 3D structure similarities. GINClus converts base interactions and 3D structures of RNA motif candidates into graph representations and using graph isomorphism network (GIN) model in combination with K-means and hierarchical agglomerative clustering, GINClus clusters the RNA motif candidates based on their structural similarities. GINClus has a clustering accuracy of 87.88% for known internal loop motifs and 97.69% for known hairpin loop motifs. Using GINClus, we successfully clustered the motifs of the same families together and were able to find 927 new instances of Sarcin-ricin, Kink-turn, Tandem-shear, Hook-turn, E-loop, C-loop, T-loop, and GNRA loop motif families. We also identified 12 new RNA structural motif families with unique structure and base-pair interactions.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"7 2","pages":"lqaf050"},"PeriodicalIF":4.0,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12034103/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144051078","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

Ocelli: an open-source tool for the analysis and visualization of developmental multimodal single-cell data. Ocelli：一个开源工具，用于分析和可视化发展中的多模态单细胞数据。

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NAR Genomics and Bioinformatics Pub Date : 2025-04-10 eCollection Date: 2025-06-01 DOI: 10.1093/nargab/lqaf040

Piotr Rutkowski, Marcin Tabaka

{"title":"Ocelli: an open-source tool for the analysis and visualization of developmental multimodal single-cell data.","authors":"Piotr Rutkowski, Marcin Tabaka","doi":"10.1093/nargab/lqaf040","DOIUrl":"10.1093/nargab/lqaf040","url":null,"abstract":"The recent expansion of single-cell technologies has enabled simultaneous genome-wide measurements of multiple modalities in the same single cell. The potential to jointly profile such modalities as gene expression, chromatin accessibility, protein epitopes, or multiple histone modifications at single-cell resolution represents a compelling opportunity to study developmental processes at multiple layers of gene regulation. Here, we present Ocelli, a lightweight Python package implemented in Ray for scalable visualization and analysis of developmental multimodal single-cell data. The core functionality of Ocelli focuses on diffusion-based modeling of biological processes involving cell state transitions. Ocelli addresses common tasks in single-cell data analysis, such as visualization of cells on a low-dimensional embedding that preserves the continuity of the developmental progression of cells, identification of rare and transient cell states, integration with trajectory inference algorithms, and imputation of undetected feature counts. Extensive benchmarking shows that Ocelli outperforms existing methods regarding computational time and quality of the reconstructed low-dimensional representation of developmental data.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"7 2","pages":"lqaf040"},"PeriodicalIF":4.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12086682/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144102446","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

Meta-analysis of genomic characteristics for antiviral influenza defective interfering particle prioritization. 用于确定抗病毒流感缺陷干扰颗粒优先级的基因组特征元分析。

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NAR Genomics and Bioinformatics Pub Date : 2025-04-04 eCollection Date: 2025-06-01 DOI: 10.1093/nargab/lqaf031

Jens J G Lohmann, Mia Le, Fadi G Alnaji, Olga Zolotareva, Jan Baumbach, Tanja Laske

{"title":"Meta-analysis of genomic characteristics for antiviral influenza defective interfering particle prioritization.","authors":"Jens J G Lohmann, Mia Le, Fadi G Alnaji, Olga Zolotareva, Jan Baumbach, Tanja Laske","doi":"10.1093/nargab/lqaf031","DOIUrl":"10.1093/nargab/lqaf031","url":null,"abstract":"Defective interfering particles (DIPs) are viral deletion mutants that hamper virus replication and are, thus, potent novel antiviral agents. To evaluate possible antiviral treatments, we first need to get a deeper understanding of DIP characteristics. Thus, we performed a meta-analysis of 20 already published sequencing datasets of influenza A and B viruses (IAV and IBV) from in vivo and in vitro experiments. We analyzed each dataset for characteristics, such as deletion-containing viral genome (DelVG) length distributions, direct repeats, and nucleotide enrichment at the deletion site. Our analysis suggests differences in the length of the 3'- and 5'-end retained in IAV and IBV viral sequences upon deletion. Moreover, in vitro DelVGs tend to be shorter than those in vivo, which is a novel finding with potential implications for future DIP treatment design. Additionally, our analysis demonstrates the presence of DelVGs with longer than expected sequences, possibly related to an alternative mechanism of DelVG formation. Finally, a joint ranking of DelVGs originating from 7 A/Puerto Rico/8/1934 datasets revealed 11 highly abundant, yet unnoticed, candidates. Together, our study highlights the importance of meta-analyses to uncover yet unknown DelVG characteristics and to pre-select candidates for antiviral treatment design.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"7 2","pages":"lqaf031"},"PeriodicalIF":4.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11970370/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796616","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

Digenic variant interpretation with hypothesis-driven explainable AI. 用假设驱动的可解释人工智能解释遗传变异。

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NAR Genomics and Bioinformatics Pub Date : 2025-03-29 eCollection Date: 2025-06-01 DOI: 10.1093/nargab/lqaf029

Federica De Paoli, Giovanna Nicora, Silvia Berardelli, Andrea Gazzo, Riccardo Bellazzi, Paolo Magni, Ettore Rizzo, Ivan Limongelli, Susanna Zucca

引用次数: 0

IPANEMAP Suite: a pipeline for probing-informed RNA structure modeling. IPANEMAP套件：用于探测信息RNA结构建模的管道。

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NAR Genomics and Bioinformatics Pub Date : 2025-03-25 eCollection Date: 2025-03-01 DOI: 10.1093/nargab/lqaf028

Pierre Hardouin, Nan Pan, Francois-Xavier Lyonnet du Moutier, Nathalie Chamond, Yann Ponty, Sebastian Will, Bruno Sargueil

{"title":"IPANEMAP Suite: a pipeline for probing-informed RNA structure modeling.","authors":"Pierre Hardouin, Nan Pan, Francois-Xavier Lyonnet du Moutier, Nathalie Chamond, Yann Ponty, Sebastian Will, Bruno Sargueil","doi":"10.1093/nargab/lqaf028","DOIUrl":"10.1093/nargab/lqaf028","url":null,"abstract":"In addition to their sequence, multiple functions of RNAs are encoded within their structure, which is often difficult to solve using physico-chemical methods. Incorporating low-resolution experimental data such as chemical probing into computational prediction significantly enhances RNA structure modeling accuracy. While medium- and high-throughput RNA structure probing techniques are widely accessible, the subsequent analysis process can be cumbersome, involving multiple software and manual data manipulation. In addition, the relevant interpretation of the data requires proper parameterization of the software and a strict consistency in the analysis pipeline. To streamline such workflows, we introduce IPANEMAP Suite, a comprehensive platform that guides users from chemically probing raw data to visually informative secondary structure models. IPANEMAP Suite seamlessly integrates various experimental datasets and facilitates comparative analysis of RNA structures under different conditions (footprinting), aiding in the study of protein or small molecule interactions with RNA. Here, we show that the unique ability of IPANEMAP Suite to perform integrative modeling using several chemical probing datasets with phylogenetic data can be instrumental in obtaining accurate secondary structure models. The platform's project-based approach ensures full traceability and generates publication-quality outputs, simplifying the entire RNA structure analysis process. IPANEMAP Suite is freely available at https://github.com/Sargueil-CiTCoM/ipasuite under a GPL-3.0 license.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"7 1","pages":"lqaf028"},"PeriodicalIF":4.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11934922/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143711422","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

The BioGenome Portal: a web-based platform for biodiversity genomics data management. 生物基因组门户：一个基于网络的生物多样性基因组数据管理平台。

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NAR Genomics and Bioinformatics Pub Date : 2025-03-22 eCollection Date: 2025-03-01 DOI: 10.1093/nargab/lqaf020

Emilio Righi, Roderic Guigó

引用次数: 0

Reconstructing 3D chromosome structures from single-cell Hi-C data with SO(3)-equivariant graph neural networks. 利用SO(3)-等变图神经网络从单细胞Hi-C数据重建三维染色体结构。

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NAR Genomics and Bioinformatics Pub Date : 2025-03-22 eCollection Date: 2025-03-01 DOI: 10.1093/nargab/lqaf027

Yanli Wang, Jianlin Cheng

{"title":"Reconstructing 3D chromosome structures from single-cell Hi-C data with SO(3)-equivariant graph neural networks.","authors":"Yanli Wang, Jianlin Cheng","doi":"10.1093/nargab/lqaf027","DOIUrl":"10.1093/nargab/lqaf027","url":null,"abstract":"The spatial conformation of chromosomes and genomes of single cells is relevant to cellular function and useful for elucidating the mechanism underlying gene expression and genome methylation. The chromosomal contacts (i.e. chromosomal regions in spatial proximity) entailing the three-dimensional (3D) structure of the genome of a single cell can be obtained by single-cell chromosome conformation capture techniques, such as single-cell Hi-C (ScHi-C). However, due to the sparsity of chromosomal contacts in ScHi-C data, it is still challenging for traditional 3D conformation optimization methods to reconstruct the 3D chromosome structures from ScHi-C data. Here, we present a machine learning-based method based on a novel SO(3)-equivariant graph neural network (HiCEGNN) to reconstruct 3D structures of chromosomes of single cells from ScHi-C data. HiCEGNN consistently outperforms both the traditional optimization methods and the only other deep learning method across diverse cells, different structural resolutions, and different noise levels of the data. Moreover, HiCEGNN is robust against the noise in the ScHi-C data.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"7 1","pages":"lqaf027"},"PeriodicalIF":4.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11928942/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143693442","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

Enhancing the annotation of small ORF-altering variants using MORFEE: introducing MORFEEdb, a comprehensive catalog of SNVs affecting upstream ORFs in human 5'UTRs. 使用MORFEE增强对改变orf的小变异的注释：引入MORFEEdb，一个影响人类5' utr上游orf的snv的综合目录。

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NAR Genomics and Bioinformatics Pub Date : 2025-03-19 eCollection Date: 2025-03-01 DOI: 10.1093/nargab/lqaf017

Caroline Meguerditchian, David Baux, Thomas E Ludwig, Emmanuelle Genin, David-Alexandre Trégouët, Omar Soukarieh

{"title":"Enhancing the annotation of small ORF-altering variants using MORFEE: introducing MORFEEdb, a comprehensive catalog of SNVs affecting upstream ORFs in human 5'UTRs.","authors":"Caroline Meguerditchian, David Baux, Thomas E Ludwig, Emmanuelle Genin, David-Alexandre Trégouët, Omar Soukarieh","doi":"10.1093/nargab/lqaf017","DOIUrl":"10.1093/nargab/lqaf017","url":null,"abstract":"Non-canonical small open reading frames (sORFs) are among the main regulators of gene expression. The most studied of these are upstream ORFs (upORFs) located in the 5'-untranslated region (UTR) of coding genes. Internal ORFs (intORFs) in the coding sequence and downstream ORFs (dORFs) in the 3'UTR have received less attention. Different bioinformatics tools permit the prediction of single nucleotide variants (SNVs) altering upORFs, mainly those creating AUGs or deleting stop codons, but no tool predicts variants altering non-canonical translation initiation sites and those altering intORFs or dORFs. We propose an upgrade of our MORFEE bioinformatics tool to identify SNVs that may alter all types of sORFs in coding transcripts from a VCF file. Moreover, we generate an exhaustive catalog, named MORFEEdb, reporting all possible SNVs altering existing upORFs or creating new ones in human transcripts, and provide an R script for visualizing the results. MORFEEdb has been implemented in the public platform Mobidetails. Finally, the annotation of ClinVar variants with MORFEE reveals that > 45% of UTR-SNVs can alter upORFs or dORFs. In conclusion, MORFEE and MORFEEdb have the potential to improve the molecular diagnosis of rare human diseases and to facilitate the identification of functional variants from genome-wide association studies of complex traits.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"7 1","pages":"lqaf017"},"PeriodicalIF":4.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11920869/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143664809","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

Crafted experiments to evaluate feature selection methods for single-cell RNA-seq data. 精心设计的实验来评估单细胞RNA-seq数据的特征选择方法。

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NAR Genomics and Bioinformatics Pub Date : 2025-03-19 eCollection Date: 2025-03-01 DOI: 10.1093/nargab/lqaf023

Siyao Liu, David L Corcoran, Susana Garcia-Recio, James S Marron, Charles M Perou

{"title":"Crafted experiments to evaluate feature selection methods for single-cell RNA-seq data.","authors":"Siyao Liu, David L Corcoran, Susana Garcia-Recio, James S Marron, Charles M Perou","doi":"10.1093/nargab/lqaf023","DOIUrl":"10.1093/nargab/lqaf023","url":null,"abstract":"While numerous methods have been developed for analyzing scRNA-seq data, benchmarking various methods remains challenging. There is a lack of ground truth datasets for evaluating novel gene selection and/or clustering methods. We propose the use of crafted experiments, a new approach based upon perturbing signals in a real dataset for comparing analysis methods. We demonstrate the effectiveness of crafted experiments for evaluating new univariate distribution-oriented suite of feature selection methods, called GOF. We show GOF selects features that robustly identify crafted features and perform well on real non-crafted data sets. Using varying ways of crafting, we also show the context in which each GOF method performs the best. GOF is implemented as an open-source R package and freely available under GPL-2 license at https://github.com/siyao-liu/GOF. Source code, including all functions for constructing crafted experiments and benchmarking feature selection methods, are publicly available at https://github.com/siyao-liu/CraftedExperiment.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"7 1","pages":"lqaf023"},"PeriodicalIF":4.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11920870/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143664831","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 four eigen-phase model of multi-omics unveils new insights into yeast metabolic cycle. 多组学的四特征相模型揭示了酵母代谢周期的新见解。

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NAR Genomics and Bioinformatics Pub Date : 2025-03-19 eCollection Date: 2025-03-01 DOI: 10.1093/nargab/lqaf022

Linting Wang, Xiaojie Li, Jianhui Shi, Lei M Li

{"title":"A four eigen-phase model of multi-omics unveils new insights into yeast metabolic cycle.","authors":"Linting Wang, Xiaojie Li, Jianhui Shi, Lei M Li","doi":"10.1093/nargab/lqaf022","DOIUrl":"10.1093/nargab/lqaf022","url":null,"abstract":"The yeast metabolic cycle (YMC), characterized by cyclic oscillations in transcripts and metabolites, is an ideal model for studying biological rhythms. Although multiple omics datasets on the YMC are available, a unified landscape for this process is missing. To address this gap, we integrated multi-omics datasets by singular value decompositions (SVDs), which stratify each dataset into two levels and define four eigen-phases: primary 1A/1B and secondary 2A/2B. The eigen-phases occur cyclically in the order 1B, 2A, 1A, and 2B, demonstrating an interplay of induction and repression: one eigen-phase induces the next one at a different level, while represses the other one at the same level. Distinct molecular characteristics were identified for each eigen-phase. Novel ones include the production and consumption of glycerol in eigen-phases 2A/2B, and the opposite regulation of ribosome biogenesis and aerobic respiration between 2A/2B. Moreover, we estimated the timing of multi-omics: histone modifications H3K9ac/H3K18ac precede mRNA transcription in ∼3 min, followed by metabolomic changes in ∼13 min. The transition to the next eigen-phase occurs roughly 38 min later. From epigenome H3K9ac/H3K18ac to metabolome, the eigen-entropy increases. This work provides a computational framework applicable to multi-omics data integration.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"7 1","pages":"lqaf022"},"PeriodicalIF":4.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11920873/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143664830","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