{"title":"RECOGNICER: A coarse-graining approach for identifying broad domains from ChIP-seq data.","authors":"Chongzhi Zang, Yiren Wang, Weiqun Peng","doi":"10.1007/s40484-020-0225-2","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Histone modifications are major factors that define chromatin states and have functions in regulating gene expression in eukaryotic cells. Chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) technique has been widely used for profiling the genome-wide distribution of chromatin-associating protein factors. Some histone modifications, such as H3K27me3 and H3K9me3, usually mark broad domains in the genome ranging from kilobases (kb) to megabases (Mb) long, resulting in diffuse patterns in the ChIP-seq data that are challenging for signal separation. While most existing ChIP-seq peak-calling algorithms are based on local statistical models without account of multi-scale features, a principled method to identify scale-free board domains has been lacking.</p><p><strong>Methods: </strong>Here we present RECOGNICER (Recursive coarse-graining identification for ChIP-seq enriched regions), a computational method for identifying ChIP-seq enriched domains on a large range of scales. The algorithm is based on a coarse-graining approach, which uses recursive block transformations to determine spatial clustering of local enriched elements across multiple length scales.</p><p><strong>Results: </strong>We apply RECOGNICER to call H3K27me3 domains from ChIP-seq data, and validate the results based on H3K27me3's association with repressive gene expression. We show that RECOGNICER outperforms existing ChIP-seq broad domain calling tools in identifying more whole domains than separated pieces.</p><p><strong>Conclusion: </strong>RECOGNICER can be a useful bioinformatics tool for next-generation sequencing data analysis in epigenomics research.</p>","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":0.6000,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8318318/pdf/nihms-1669580.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quantitative Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1007/s40484-020-0225-2","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2020/11/19 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Histone modifications are major factors that define chromatin states and have functions in regulating gene expression in eukaryotic cells. Chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) technique has been widely used for profiling the genome-wide distribution of chromatin-associating protein factors. Some histone modifications, such as H3K27me3 and H3K9me3, usually mark broad domains in the genome ranging from kilobases (kb) to megabases (Mb) long, resulting in diffuse patterns in the ChIP-seq data that are challenging for signal separation. While most existing ChIP-seq peak-calling algorithms are based on local statistical models without account of multi-scale features, a principled method to identify scale-free board domains has been lacking.
Methods: Here we present RECOGNICER (Recursive coarse-graining identification for ChIP-seq enriched regions), a computational method for identifying ChIP-seq enriched domains on a large range of scales. The algorithm is based on a coarse-graining approach, which uses recursive block transformations to determine spatial clustering of local enriched elements across multiple length scales.
Results: We apply RECOGNICER to call H3K27me3 domains from ChIP-seq data, and validate the results based on H3K27me3's association with repressive gene expression. We show that RECOGNICER outperforms existing ChIP-seq broad domain calling tools in identifying more whole domains than separated pieces.
Conclusion: RECOGNICER can be a useful bioinformatics tool for next-generation sequencing data analysis in epigenomics research.
期刊介绍:
Quantitative Biology is an interdisciplinary journal that focuses on original research that uses quantitative approaches and technologies to analyze and integrate biological systems, construct and model engineered life systems, and gain a deeper understanding of the life sciences. It aims to provide a platform for not only the analysis but also the integration and construction of biological systems. It is a quarterly journal seeking to provide an inter- and multi-disciplinary forum for a broad blend of peer-reviewed academic papers in order to promote rapid communication and exchange between scientists in the East and the West. The content of Quantitative Biology will mainly focus on the two broad and related areas: ·bioinformatics and computational biology, which focuses on dealing with information technologies and computational methodologies that can efficiently and accurately manipulate –omics data and transform molecular information into biological knowledge. ·systems and synthetic biology, which focuses on complex interactions in biological systems and the emergent functional properties, and on the design and construction of new biological functions and systems. Its goal is to reflect the significant advances made in quantitatively investigating and modeling both natural and engineered life systems at the molecular and higher levels. The journal particularly encourages original papers that link novel theory with cutting-edge experiments, especially in the newly emerging and multi-disciplinary areas of research. The journal also welcomes high-quality reviews and perspective articles.