NAR Genomics and Bioinformatics最新文献

IPANEMAP Suite: a pipeline for probing-informed RNA structure modeling.

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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.

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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.

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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.

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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.

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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.

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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

Expansion of the tmRNA sequence database and new tools for search and visualization. 扩充 tmRNA 序列数据库以及用于搜索和可视化的新工具。

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NAR Genomics and Bioinformatics Pub Date : 2025-03-18 eCollection Date: 2025-03-01 DOI: 10.1093/nargab/lqaf019

Eric P Nawrocki, Anton I Petrov, Kelly P Williams

{"title":"Expansion of the tmRNA sequence database and new tools for search and visualization.","authors":"Eric P Nawrocki, Anton I Petrov, Kelly P Williams","doi":"10.1093/nargab/lqaf019","DOIUrl":"10.1093/nargab/lqaf019","url":null,"abstract":"Transfer-messenger RNA (tmRNA) contributes essential tRNA-like and mRNA-like functions during the process of trans-translation, a mechanism of quality control for the translating bacterial ribosome. Proper tmRNA identification benefits the study of trans-translation and also the study of genomic islands, which frequently use the tmRNA gene as an integration site. Automated tmRNA gene identification tools are available, but manual inspection is still important for eliminating false positives. We have increased our database of precisely mapped tmRNA sequences over 50-fold to 97 179 unique sequences. Group I introns had previously been found integrated within a single subsite within the TψC-loop; they have now been identified at four distinct subsites, suggesting multiple founding events of invasion of tmRNA genes by group I introns, all in the same vicinity. tmRNA genes were found in metagenomic archaeal genomes, perhaps a result of misbinning of bacterial sequences during genome assembly. With the expanded database, we have produced new covariance models for improved tmRNA sequence search and new secondary structure visualization tools.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"7 1","pages":"lqaf019"},"PeriodicalIF":4.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11915505/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658845","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

SeArcH schemes for Approximate stRing mAtching. 近似环形蚀刻的序列方案。

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NAR Genomics and Bioinformatics Pub Date : 2025-03-18 eCollection Date: 2025-03-01 DOI: 10.1093/nargab/lqaf025

Simon Gene Gottlieb, Knut Reinert

{"title":"SeArcH schemes for Approximate stRing mAtching.","authors":"Simon Gene Gottlieb, Knut Reinert","doi":"10.1093/nargab/lqaf025","DOIUrl":"10.1093/nargab/lqaf025","url":null,"abstract":"Finding approximate occurrences of a query in a text using a full-text index is a central problem in stringology with many applications, especially in bioinformatics. The recent work has shown significant speed-ups by combining bidirectional indices and employing variations of search schemes. Search schemes partition a query and describe how to search the resulting parts with a given error bound. The performance of search schemes can be approximated by the node count, which represents an upper bound of the number of search steps. Finding optimum search schemes is a difficult combinatorial optimization problem that becomes hard to solve for four and more errors. This paper improves on a few topics important to search scheme based searches: First, we show how search schemes can be used to model previously published approximate search strategies such as suffix filters, 01*0-seeds, or the pigeonhole principle. This unifies these strategies in the search scheme framework, makes them easily comparable and results in novel search schemes that allow for any number of errors. Second, we present a search scheme construction heuristic, which is on par with optimum search schemes and has a better node count than any known search scheme for equal or above four errors. Finally, using the different search schemes, we show that the node count measure is not an ideal performance metric and therefore propose an improved performance metric called the weighted node count, which approximates a search algorithm's run time much more accurately.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"7 1","pages":"lqaf025"},"PeriodicalIF":4.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11915513/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658930","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

NucleoSeeker-precision filtering of RNA databases to curate high-quality datasets.

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NAR Genomics and Bioinformatics Pub Date : 2025-03-18 eCollection Date: 2025-03-01 DOI: 10.1093/nargab/lqaf021

Utkarsh Upadhyay, Fabrizio Pucci, Julian Herold, Alexander Schug

{"title":"NucleoSeeker-precision filtering of RNA databases to curate high-quality datasets.","authors":"Utkarsh Upadhyay, Fabrizio Pucci, Julian Herold, Alexander Schug","doi":"10.1093/nargab/lqaf021","DOIUrl":"10.1093/nargab/lqaf021","url":null,"abstract":"The structural prediction of biomolecules via computational methods complements the often involved wet-lab experiments. Unlike protein structure prediction, RNA structure prediction remains a significant challenge in bioinformatics, primarily due to the scarcity of annotated RNA structure data and its varying quality. Many methods have used this limited data to train deep learning models but redundancy, data leakage and bad data quality hampers their performance. In this work, we present NucleoSeeker, a tool designed to curate high-quality, tailored datasets from the Protein Data Bank (PDB) database. It is a unified framework that combines multiple tools and streamlines an otherwise complicated process of data curation. It offers multiple filters at structure, sequence, and annotation levels, giving researchers full control over data curation. Further, we present several use cases. In particular, we demonstrate how NucleoSeeker allows the creation of a nonredundant RNA structure dataset to assess AlphaFold3's performance for RNA structure prediction. This demonstrates NucleoSeeker's effectiveness in curating valuable nonredundant tailored datasets to both train novel and judge existing methods. NucleoSeeker is very easy to use, highly flexible, and can significantly increase the quality of RNA structure datasets.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"7 1","pages":"lqaf021"},"PeriodicalIF":4.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11915511/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658929","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

Determinant-based grouping of SNPs and its application for detecting disease-associated genomic loci.

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NAR Genomics and Bioinformatics Pub Date : 2025-03-18 eCollection Date: 2025-03-01 DOI: 10.1093/nargab/lqaf024

Gennady Khvorykh, Andrey Khrunin

{"title":"Determinant-based grouping of SNPs and its application for detecting disease-associated genomic loci.","authors":"Gennady Khvorykh, Andrey Khrunin","doi":"10.1093/nargab/lqaf024","DOIUrl":"10.1093/nargab/lqaf024","url":null,"abstract":"Groups of single nucleotide polymorphisms (SNPs) are more effective than individual SNPs in identifying genetic loci associated with diseases. However, an optimal method for grouping SNPs remains an open question. Here, we introduce a novel approach for SNP grouping, leveraging the determinant of linkage disequilibrium (LD) matrices as a comprehensive metric of multicollinearity. This method builds on the established use of determinants in regression analysis as an aggregate measure of variable interdependence. We proposed that SNPs be grouped by evaluating the determinant of their LD matrices, with the approach validated using both synthetic genotype-phenotype data and real-world data from genome-wide association studies (GWAS) of ischemic stroke. Application of this method identified two previously known and five novel candidate genes associated with the onset of disease. Additionally, we developed a straightforward procedure to estimate a critical parameter for the model: the minimal determinant value for an LD matrix to be considered singular. In summary, the determinant of the LD matrix serves as a robust integrative measure for assessing SNP group quality. This metric underpins a bioinformatics workflow capable of identifying genomic loci associated with disease onset, offering a valuable tool for advancing genetic association studies.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"7 1","pages":"lqaf024"},"PeriodicalIF":4.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11915498/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658839","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