NAR Genomics and Bioinformatics最新文献_第9页

Robin: an advanced tool for comparative loop caller result analysis leveraging large language models. Robin：利用大型语言模型进行比较循环调用者结果分析的高级工具。

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NAR Genomics and Bioinformatics Pub Date : 2026-01-30 eCollection Date: 2026-03-01 DOI: 10.1093/nargab/lqag009

H M A Mohit Chowdhury, Mattie Fuller, Oluwatosin Oluwadare

{"title":"Robin: an advanced tool for comparative loop caller result analysis leveraging large language models.","authors":"H M A Mohit Chowdhury, Mattie Fuller, Oluwatosin Oluwadare","doi":"10.1093/nargab/lqag009","DOIUrl":"10.1093/nargab/lqag009","url":null,"abstract":"There has been significant interest in genomics research, leading to the development of numerous new methods. One notable area of progress is in chromosome loop detection algorithms (also known as loop callers). However, despite these advancements, there is no available platform to analyze, compare, or benchmark current tools' results on the go. Developing such a platform is crucial to accelerating research and ensuring the reliability and effectiveness of new methods in this field. Hence, in this work, we propose Robin, an advanced, ready-to-go platform for comparative loop caller result analysis that leverages large language models (LLMs). Robin is a web server designed to analyze loop caller results, offering a comprehensive range of analysis metrics, such as recovery and overlap. It is tightly integrated with HiGlass for interactive, multi-resolution visualization of Hi-C matrices and loop annotations, allowing users to visually inspect and validate loop structures in a genomic context. Additionally, Robin incorporates LLM capabilities that enable users to generate customized plots and figures simply by providing natural language instructions. Overall, Robin is a robust and comprehensive loop caller result analysis and visualization tool. It is publicly accessible at http://hicrobin.online, with comprehensive documentation available at http://documentation.hicrobin.online/.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqag009"},"PeriodicalIF":3.0,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12856358/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146107560","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 novel explainable deep-learning approach for network analysis of epistatic interactions. 一种新颖的可解释的深度学习方法，用于上位相互作用的网络分析。

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NAR Genomics and Bioinformatics Pub Date : 2026-01-30 eCollection Date: 2026-03-01 DOI: 10.1093/nargab/lqag004

Andrea Mastropietro, Georgios Markopoulos, Evangelos Evangelou, Aris Anagnostopoulos

{"title":"A novel explainable deep-learning approach for network analysis of epistatic interactions.","authors":"Andrea Mastropietro, Georgios Markopoulos, Evangelos Evangelou, Aris Anagnostopoulos","doi":"10.1093/nargab/lqag004","DOIUrl":"10.1093/nargab/lqag004","url":null,"abstract":"Epistatic interactions of gene loci often determine complex trait phenotypes and may indicate the underlying molecular mechanisms of traits and diseases. Yet, the inference of epistatic interactions and gene-gene networks remains challenging. Neural networks have become successful in classifying complex data, revolutionizing various fields. However, their complexity does not reveal how they combine input features, and this lack of interpretability limits their use with genetic data. We thus introduce EpiDetect, a novel framework for discovering interactions between input features (single-nucleotide polymorphisms-SNPs-in our setting). EpiDetect neural-network-based classifiers detect interactions in systolic, diastolic, and pulse pressure genome-wide association datasets. Central to EpiDetect is EpiCID, a novel explainability algorithm for neural networks. Using EpiCID, we identified a network of highly interactive SNPs, performed centrality analysis to pinpoint central SNPs and genes, and outperformed established epistatic-interaction-detection algorithms. Moreover, pathway analysis uncovered well-known and novel pathways that could play a significant role in blood pressure traits, opening up new research directions.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqag004"},"PeriodicalIF":3.0,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12856359/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146107545","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

Evolutionary and methodological considerations when interpreting gene presence-absence variation in pangenomes. 在解释泛基因组中基因存在-缺失变异时的进化和方法学考虑。

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NAR Genomics and Bioinformatics Pub Date : 2026-01-30 eCollection Date: 2026-03-01 DOI: 10.1093/nargab/lqag011

Tomáš Brůna, Avinash Sreedasyam, Avril M Harder, John T Lovell

{"title":"Evolutionary and methodological considerations when interpreting gene presence-absence variation in pangenomes.","authors":"Tomáš Brůna, Avinash Sreedasyam, Avril M Harder, John T Lovell","doi":"10.1093/nargab/lqag011","DOIUrl":"10.1093/nargab/lqag011","url":null,"abstract":"While graph-based pangenomes have become a standard and interoperable foundation for comparisons across multiple reference genomes, integrating protein-coding gene annotations across pangenomes in a single 'pangene set' remains challenging, both because of methodological inconsistency and biological presence-absence variation (PAV). Here, we review and experimentally evaluate the root of genome annotation and pangene set inconsistency using two polyploid plant pangenomes: cotton and soybean, which were chosen because of their existing diverse high-quality genomic resources and the known importance of gene PAV in their respective breeding programs. We first demonstrate that building pangene sets across different genome resources is highly error prone: PAV calculated directly from the genome annotations hosted on public repositories recapitulates structure in annotation methods and not biological sequence differences. Re-annotation of all genomes with a single identical pipeline largely resolves the broadest stroke issues; however, substantial challenges remain, including a surprisingly common case where exactly identical sequences have different gene model structural annotations. Combined, these results clearly show that pangenome gene model annotations must be carefully integrated before any biological inference can be made regarding sequence evolution, gene copy-number, or PAV.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqag011"},"PeriodicalIF":3.0,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12856356/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146107592","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

RAZOR: a database of PCR primers targeting human respiratory viruses. RAZOR：针对人类呼吸道病毒的PCR引物数据库。

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NAR Genomics and Bioinformatics Pub Date : 2026-01-27 eCollection Date: 2026-03-01 DOI: 10.1093/nargab/lqag005

Hunter M Gill, Quoseena Mir, Rajneesh Srivastava, Sarath Chandra Janga

{"title":"RAZOR: a database of PCR primers targeting human respiratory viruses.","authors":"Hunter M Gill, Quoseena Mir, Rajneesh Srivastava, Sarath Chandra Janga","doi":"10.1093/nargab/lqag005","DOIUrl":"10.1093/nargab/lqag005","url":null,"abstract":"Respiratory viruses like SARS-CoV-2, Influenza A, and others represent a considerable threat to public health, infecting millions of people annually. Previous respiratory virus outbreaks have demonstrated the value of polymerase chain reaction (PCR) testing as a gold standard for definitive virus identification; however, existing database resources for viral PCR primer design are either outdated or restricted to a small number of species. To address the need for updated and comprehensive viral PCR resources, we introduce RAZOR, a database of nearly 20 000 primers covering 20 different respiratory virus species. We created genome-wide template sets for each virus and used them to design quantitative PCR (qPCR) and standard PCR primer pairs with Primer3. Detailed primer information, including sequence coordinates, melting and annealing temperatures, GC content, and hairpin structure probabilities, is provided through a user-friendly Integrative Genomics Viewer (IGV) interactive display. Validated primers, including a group of SARS-CoV-2 primers tested by our group, are also showcased in a dedicated section of the IGV. RAZOR stands out as a valuable tool for investigators designing targeted PCR approaches for respiratory virus detection. Database URL: https://razor-razor.webapps.iu.edu.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqag005"},"PeriodicalIF":3.0,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12838527/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146094454","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

NGSTroubleFinder: a tool for detection and quantification of contamination and kinship across human NGS data. NGSTroubleFinder：用于检测和量化人类NGS数据中的污染和亲缘关系的工具。

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NAR Genomics and Bioinformatics Pub Date : 2026-01-27 eCollection Date: 2026-03-01 DOI: 10.1093/nargab/lqag006

Samuel Valentini, Tecla Venturelli, Xavier Gallego, Lynn Durham, Laura Perez-Cano, Emre Guney

{"title":"NGSTroubleFinder: a tool for detection and quantification of contamination and kinship across human NGS data.","authors":"Samuel Valentini, Tecla Venturelli, Xavier Gallego, Lynn Durham, Laura Perez-Cano, Emre Guney","doi":"10.1093/nargab/lqag006","DOIUrl":"10.1093/nargab/lqag006","url":null,"abstract":"Quality control constitutes a critical component of any next-generation sequencing (NGS) pipeline; however, most existing pipelines emphasize technical quality assessment (e.g. read quality, alignment metrics, duplication rates) while overlooking other equally important dimensions, such as sample identity verification, contamination detection, kinship analysis, and metadata concordance. Detecting issues like cross-sample contamination and sample swaps is essential to control data integrity. Here, we present NGSTroubleFinder, a novel tool to detect cross-sample contamination in human whole-genome and whole-transcriptome sequencing data, sample swaps, and mismatches between the reported and the inferred genetic and transcriptomic sexes. It can be run directly on BAM/CRAM files without requiring additional variant-calling steps and offers an integrated pipeline for ensuring quality control on NGS data, generated particularly within the context of clinical studies or research projects involving family members. It produces a detailed report that combines the results of its multiple analyses, including kinship, sex prediction, and contamination metrics. The tool reports extensive information on the samples, both in textual and HTML formats, including key plots for easy interpretation of the results. NGSTroubleFinder is written in Python and incorporates a custom-built parallelized pileup engine written in C, and it can be easily installed with pip. The tool source code and the models are freely available on GitHub (https://github.com/STALICLA-RnD/NGSTroubleFinder), and a containerized version is available on Docker Hub (https://hub.docker.com/r/staliclarnd/ngstroublefinder).","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqag006"},"PeriodicalIF":3.0,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12838523/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146094416","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

SM3DD with segmented PCA: a comprehensive method for interpreting 3D spatial transcriptomics. SM3DD与分段PCA：一种解释三维空间转录组学的综合方法。

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NAR Genomics and Bioinformatics Pub Date : 2026-01-27 eCollection Date: 2026-03-01 DOI: 10.1093/nargab/lqag007

Tony Blick, Aaron Kilgallon, James Monkman, Caroline Cooper, Chin Wee Tan, Emily E Killingbeck, Liuliu Pan, Youngmi Kim, Yan Liang, Andy Nam, Michael Leon, Paulo S F Guimaraes, Seigo Nagashima, Ana P C Martins, Cleber Machado-Souza, Lucia de Noronha, John F Fraser, Gabrielle T Belz, Fernando Souza-Fonseca-Guimaraes, Arutha Kulasinghe

{"title":"SM3DD with segmented PCA: a comprehensive method for interpreting 3D spatial transcriptomics.","authors":"Tony Blick, Aaron Kilgallon, James Monkman, Caroline Cooper, Chin Wee Tan, Emily E Killingbeck, Liuliu Pan, Youngmi Kim, Yan Liang, Andy Nam, Michael Leon, Paulo S F Guimaraes, Seigo Nagashima, Ana P C Martins, Cleber Machado-Souza, Lucia de Noronha, John F Fraser, Gabrielle T Belz, Fernando Souza-Fonseca-Guimaraes, Arutha Kulasinghe","doi":"10.1093/nargab/lqag007","DOIUrl":"10.1093/nargab/lqag007","url":null,"abstract":"We developed Standardised Minimum 3D Distance (SM3DD), an entirely cell segmentation/annotation-free approach to the analysis of spatial RNA datasets, using it to compare lung tissue from 16 clinically normal individuals to that of 18 SARS-CoV-2 patients who died from acute respiratory distress syndrome. RNA spatial coordinates were determined using the CosMx™ Spatial Molecular Imager (Bruker Spatial Biology, US). For each individual transcript location, we calculated the three-dimensional distances to the nearest transcript of each transcript type, standardising the distances to each transcript type. Mean SM3DDs were compared between normal and SARS-CoV-2 patients. Notably, hierarchical clustering of the directional log10(P) values organized genes by functionality, making it easier to interpret biological contexts, and for FKBP11, where a decrease in distance to MZT2A was the most significant difference, suggesting a role in interferon signalling. Using a segmented principal components analysis of the entire SM3DD dataset, we identified multiple pathways, including 'SARS-CoV-2 infection', even though the assay did not include any SARS-CoV-2 transcripts.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqag007"},"PeriodicalIF":3.0,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12838529/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146094466","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

MoGAAAP: a modular Snakemake workflow for automated genome assembly and annotation with quality assessment. MoGAAAP：一个模块化的Snakemake工作流程，用于自动基因组组装和注释，并进行质量评估。

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NAR Genomics and Bioinformatics Pub Date : 2026-01-22 eCollection Date: 2026-03-01 DOI: 10.1093/nargab/lqag008

Dirk-Jan M van Workum, Kuntal K Dey, Alexander Kozik, Dean O Lavelle, Dick de Ridder, M Eric Schranz, Richard W Michelmore, Sandra Smit

引用次数: 0

Eukan: a fully automated nuclear genome annotation pipeline for less studied and divergent eukaryotes. Eukan：一个完全自动化的核基因组注释管道，用于研究较少和不同的真核生物。

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NAR Genomics and Bioinformatics Pub Date : 2026-01-20 eCollection Date: 2026-03-01 DOI: 10.1093/nargab/lqag003

Matt Sarrasin, Gertraud Burger, B Franz Lang

{"title":"Eukan: a fully automated nuclear genome annotation pipeline for less studied and divergent eukaryotes.","authors":"Matt Sarrasin, Gertraud Burger, B Franz Lang","doi":"10.1093/nargab/lqag003","DOIUrl":"10.1093/nargab/lqag003","url":null,"abstract":"Here, we introduce a new annotation pipeline, called Eukan, designed to deliver reliably high-quality results across a broad range of eukaryotes. First, experimental evidence is automatically leveraged to refine predictions, specifically, RNA-Seq coverage to inform generalized Hidden Markov Model gene prediction and intron lengths to inform protein sequence alignments. Second, a consensus is created from an empirically optimized weighting of gene predictions from multiple sources. Third, Eukan runs a post-annotation routine to recover gene predictions missing from the consensus that otherwise have strong transcript support and appear to be protein-coding. We compare the results of Eukan with those of three popular freely available pipelines (Maker, Braker, and Gemoma) on 17 phylogenetically diverse haploid and diploid nuclear genomes. In addition to the commonly reported annotation accuracy statistics, we define a novel classification system of critical defects commonly observed in automated annotations. Furthermore, we demonstrate that each of the tested pipelines correctly identified the majority of the validated \"gold standard\" genes across the test set, but each pipeline uniquely generates a non-negligible portion of either fragmented, artificially fused, or missing genes. Despite that, Eukan performs consistently well where other pipelines encounter challenges, such as for compact protist genomes.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqag003"},"PeriodicalIF":3.0,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12817076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146019777","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

CAMP: a modular metagenomics analysis system for integrated multistep data exploration. CAMP：一个模块化宏基因组学分析系统，用于集成多步数据探索。

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NAR Genomics and Bioinformatics Pub Date : 2026-01-16 eCollection Date: 2026-03-01 DOI: 10.1093/nargab/lqaf172

Lauren Mak, Braden Tierney, Wei Wei, Cynthia Ronkowski, Rodolfo Brizola Toscan, Berk Turhan, Michael Toomey, Juan Sebastian Andrade-Martínez, Chenlian Fu, Alexander G Lucaci, Arthur Henrique Barrios Solano, João Carlos Setubal, James R Henriksen, Sam Zimmerman, Malika Kopbayeva, Anna Noyvert, Zana Iwan, Shraman Kar, Nikita Nakazawa, Dmitry Meleshko, Dmytro Horyslavets, Valeriia Kantsypa, Alina Frolova, Andre Kahles, David Danko, Eran Elhaik, Pawel Labaj, Serghei Mangul, Christopher E Mason, Iman Hajirasouliha

{"title":"CAMP: a modular metagenomics analysis system for integrated multistep data exploration.","authors":"Lauren Mak, Braden Tierney, Wei Wei, Cynthia Ronkowski, Rodolfo Brizola Toscan, Berk Turhan, Michael Toomey, Juan Sebastian Andrade-Martínez, Chenlian Fu, Alexander G Lucaci, Arthur Henrique Barrios Solano, João Carlos Setubal, James R Henriksen, Sam Zimmerman, Malika Kopbayeva, Anna Noyvert, Zana Iwan, Shraman Kar, Nikita Nakazawa, Dmitry Meleshko, Dmytro Horyslavets, Valeriia Kantsypa, Alina Frolova, Andre Kahles, David Danko, Eran Elhaik, Pawel Labaj, Serghei Mangul, Christopher E Mason, Iman Hajirasouliha","doi":"10.1093/nargab/lqaf172","DOIUrl":"10.1093/nargab/lqaf172","url":null,"abstract":"Computational analysis of large-scale metagenomics sequencing datasets provides valuable isolate-level taxonomic and functional insights from complex microbial communities. However, the ever-expanding ecosystem of metagenomics-specific methods and file formats makes designing scalable workflows and seamlessly exploring output data increasingly challenging. Although one-click bioinformatics pipelines can help organize these tools into workflows, they face compatibility and maintainability challenges that can prevent replication. To address the gap in easily extensible yet robustly distributable metagenomics workflows, we have developed the Core Analysis Modular Pipeline (CAMP), a module-based metagenomics analysis system written in Snakemake, with a standardized module and directory architecture. Each module can run independently or in sequence to produce target data formats (e.g. short-read preprocessing alone or followed by de novo assembly), and provides output summary statistics reports and Jupyter notebook-based visualizations. We applied CAMP to a set of 10 metagenomics samples, demonstrating how a modular analysis system with built-in data visualization facilitates rich seamless communication between outputs from different analytical purposes. The CAMP ecosystem (module template and analysis modules) can be found at https://github.com/Meta-CAMP.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqaf172"},"PeriodicalIF":3.0,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12809600/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145999248","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

Comparative evaluation of the prediction accuracy of AlphaFold and ESMFold for monomeric and dimeric proteins. 比较评价AlphaFold和ESMFold对单体和二聚体蛋白的预测准确性。

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NAR Genomics and Bioinformatics Pub Date : 2026-01-16 eCollection Date: 2026-03-01 DOI: 10.1093/nargab/lqag002

Sanjeet Kumar Mahtha, Sureshkumar Venkadesan, Debasisa Mohanty

{"title":"Comparative evaluation of the prediction accuracy of AlphaFold and ESMFold for monomeric and dimeric proteins.","authors":"Sanjeet Kumar Mahtha, Sureshkumar Venkadesan, Debasisa Mohanty","doi":"10.1093/nargab/lqag002","DOIUrl":"10.1093/nargab/lqag002","url":null,"abstract":"We have evaluated the prediction accuracy of three different tools, deep-learning-based AlphaFold2, AlphaFold3, and large language model-based ESMFold, utilizing the experimentally derived structures deposited in the Protein Data Bank between 2022 and 2024, excluding those entries with close homologs in the structures released prior to 2022. Based on the criteria of sequence identity lower than 40% and query coverage <70%, 1666 monomeric and 994 dimeric proteins were selected as challenging targets for benchmarking. Our analysis showed that AlphaFold2 and AlphaFold3 correctly predicted 88% of monomeric structures and 77% of dimeric proteins. On the other hand, ESMFold accurately predicted 76% of the monomeric proteins and 41% of the dimeric proteins. Since most incorrect predictions involved nuclear magnetic resonance structures, benchmarking on X-ray and cryo-electron microscopy structures showed that the prediction accuracy of AlphaFold and ESMFold was 95% and 83%, respectively, for monomeric proteins. Overall, these findings demonstrate significant differences in the prediction accuracy of these machine learning (ML)-based tools for monomeric and dimeric proteins, highlighting the advantages and limitations of these tools. Finally, to facilitate easy access to benchmarking data, we developed ProModEv (Protein Model Evaluation portal), an interactive web portal for systematic analysis of these benchmarking results, and it is available at http://pdbi.nii.ac.in/ProModEv/.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 1","pages":"lqag002"},"PeriodicalIF":3.0,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12809598/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145999186","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