Anupama Jha, Stephanie C Bohaczuk, Yizi Mao, Jane Ranchalis, Benjamin J Mallory, Alan T Min, Morgan O Hamm, Elliott Swanson, Danilo Dubocanin, Connor Finkbeiner, Tony Li, Dale Whittington, William Stafford Noble, Andrew B Stergachis, Mitchell R Vollger
{"title":"使用 fibertools 进行 DNA-m6A 调用和综合长读数表观遗传学和基因分析。","authors":"Anupama Jha, Stephanie C Bohaczuk, Yizi Mao, Jane Ranchalis, Benjamin J Mallory, Alan T Min, Morgan O Hamm, Elliott Swanson, Danilo Dubocanin, Connor Finkbeiner, Tony Li, Dale Whittington, William Stafford Noble, Andrew B Stergachis, Mitchell R Vollger","doi":"10.1101/gr.279095.124","DOIUrl":null,"url":null,"abstract":"<p><p>Long-read DNA sequencing has recently emerged as a powerful tool for studying both genetic and epigenetic architectures at single-molecule and single-nucleotide resolution. Long-read epigenetic studies encompass both the direct identification of native cytosine methylation and the identification of exogenously placed DNA <i>N</i> <sup><i>6</i></sup> -methyladenine (DNA-m6A). However, detecting DNA-m6A modifications using single-molecule sequencing, as well as coprocessing single-molecule genetic and epigenetic architectures, is limited by computational demands and a lack of supporting tools. Here, we introduce <i>fibertools</i>, a state-of-the-art toolkit that features a semisupervised convolutional neural network for fast and accurate identification of m6A-marked bases using Pacific Biosciences (PacBio) single-molecule long-read sequencing, as well as the coprocessing of long-read genetic and epigenetic data produced using either the PacBio or Oxford Nanopore Technologies (ONT) sequencing platforms. We demonstrate accurate DNA-m6A identification (>90% precision and recall) along >20 kb long DNA molecules with an ∼1000-fold improvement in speed. In addition, we demonstrate that <i>fibertools</i> can readily integrate genetic and epigenetic data at single-molecule resolution, including the seamless conversion between molecular and reference coordinate systems, allowing for accurate genetic and epigenetic analyses of long-read data within structurally and somatically variable genomic regions.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":null,"pages":null},"PeriodicalIF":6.2000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DNA-m6A calling and integrated long-read epigenetic and genetic analysis with <i>fibertools</i>.\",\"authors\":\"Anupama Jha, Stephanie C Bohaczuk, Yizi Mao, Jane Ranchalis, Benjamin J Mallory, Alan T Min, Morgan O Hamm, Elliott Swanson, Danilo Dubocanin, Connor Finkbeiner, Tony Li, Dale Whittington, William Stafford Noble, Andrew B Stergachis, Mitchell R Vollger\",\"doi\":\"10.1101/gr.279095.124\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Long-read DNA sequencing has recently emerged as a powerful tool for studying both genetic and epigenetic architectures at single-molecule and single-nucleotide resolution. Long-read epigenetic studies encompass both the direct identification of native cytosine methylation and the identification of exogenously placed DNA <i>N</i> <sup><i>6</i></sup> -methyladenine (DNA-m6A). However, detecting DNA-m6A modifications using single-molecule sequencing, as well as coprocessing single-molecule genetic and epigenetic architectures, is limited by computational demands and a lack of supporting tools. Here, we introduce <i>fibertools</i>, a state-of-the-art toolkit that features a semisupervised convolutional neural network for fast and accurate identification of m6A-marked bases using Pacific Biosciences (PacBio) single-molecule long-read sequencing, as well as the coprocessing of long-read genetic and epigenetic data produced using either the PacBio or Oxford Nanopore Technologies (ONT) sequencing platforms. We demonstrate accurate DNA-m6A identification (>90% precision and recall) along >20 kb long DNA molecules with an ∼1000-fold improvement in speed. In addition, we demonstrate that <i>fibertools</i> can readily integrate genetic and epigenetic data at single-molecule resolution, including the seamless conversion between molecular and reference coordinate systems, allowing for accurate genetic and epigenetic analyses of long-read data within structurally and somatically variable genomic regions.</p>\",\"PeriodicalId\":12678,\"journal\":{\"name\":\"Genome research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2024-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Genome research\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1101/gr.279095.124\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genome research","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1101/gr.279095.124","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
DNA-m6A calling and integrated long-read epigenetic and genetic analysis with fibertools.
Long-read DNA sequencing has recently emerged as a powerful tool for studying both genetic and epigenetic architectures at single-molecule and single-nucleotide resolution. Long-read epigenetic studies encompass both the direct identification of native cytosine methylation and the identification of exogenously placed DNA N6 -methyladenine (DNA-m6A). However, detecting DNA-m6A modifications using single-molecule sequencing, as well as coprocessing single-molecule genetic and epigenetic architectures, is limited by computational demands and a lack of supporting tools. Here, we introduce fibertools, a state-of-the-art toolkit that features a semisupervised convolutional neural network for fast and accurate identification of m6A-marked bases using Pacific Biosciences (PacBio) single-molecule long-read sequencing, as well as the coprocessing of long-read genetic and epigenetic data produced using either the PacBio or Oxford Nanopore Technologies (ONT) sequencing platforms. We demonstrate accurate DNA-m6A identification (>90% precision and recall) along >20 kb long DNA molecules with an ∼1000-fold improvement in speed. In addition, we demonstrate that fibertools can readily integrate genetic and epigenetic data at single-molecule resolution, including the seamless conversion between molecular and reference coordinate systems, allowing for accurate genetic and epigenetic analyses of long-read data within structurally and somatically variable genomic regions.
期刊介绍:
Launched in 1995, Genome Research is an international, continuously published, peer-reviewed journal that focuses on research that provides novel insights into the genome biology of all organisms, including advances in genomic medicine.
Among the topics considered by the journal are genome structure and function, comparative genomics, molecular evolution, genome-scale quantitative and population genetics, proteomics, epigenomics, and systems biology. The journal also features exciting gene discoveries and reports of cutting-edge computational biology and high-throughput methodologies.
New data in these areas are published as research papers, or methods and resource reports that provide novel information on technologies or tools that will be of interest to a broad readership. Complete data sets are presented electronically on the journal''s web site where appropriate. The journal also provides Reviews, Perspectives, and Insight/Outlook articles, which present commentary on the latest advances published both here and elsewhere, placing such progress in its broader biological context.