Cited 62 times in
Single-cell ATAC sequencing analysis: From data preprocessing to hypothesis generation
DC Field | Value | Language |
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dc.date.accessioned | 2022-09-02T01:15:18Z | - |
dc.date.available | 2022-09-02T01:15:18Z | - |
dc.date.issued | 2020-06 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/190091 | - |
dc.description.abstract | Most genetic variations associated with human complex traits are located in non-coding genomic regions. Therefore, understanding the genotype-to-phenotype axis requires a comprehensive catalog of functional non-coding genomic elements, most of which are involved in epigenetic regulation of gene expression. Genome-wide maps of open chromatin regions can facilitate functional analysis of cis- and trans-regulatory elements via their connections with trait-associated sequence variants. Currently, Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) is considered the most accessible and cost-effective strategy for genome-wide profiling of chromatin accessibility. Single-cell ATAC-seq (scATAC-seq) technology has also been developed to study cell type-specific chromatin accessibility in tissue samples containing a heterogeneous cellular population. However, due to the intrinsic nature of scATAC-seq data, which are highly noisy and sparse, accurate extraction of biological signals and devising effective biological hypothesis are difficult. To overcome such limitations in scATAC-seq data analysis, new methods and software tools have been developed over the past few years. Nevertheless, there is no consensus for the best practice of scATAC-seq data analysis yet. In this review, we discuss scATAC-seq technology and data analysis methods, ranging from preprocessing to downstream analysis, along with an up-to-date list of published studies that involved the application of this method. We expect this review will provide a guideline for successful data generation and analysis methods using appropriate software tools and databases for the study of chromatin accessibility at single-cell resolution. (C) 2020 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Elsevier B.V. | - |
dc.relation.isPartOf | COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Single-cell ATAC sequencing analysis: From data preprocessing to hypothesis generation | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) | - |
dc.contributor.googleauthor | Seungbyn Baek | - |
dc.contributor.googleauthor | Insuk Lee | - |
dc.identifier.doi | 10.1016/j.csbj.2020.06.012 | - |
dc.relation.journalcode | J03613 | - |
dc.identifier.pmid | 32637041 | - |
dc.subject.keyword | ATAC sequencing | - |
dc.subject.keyword | Chromatin accessibility | - |
dc.subject.keyword | Single-cell biology | - |
dc.subject.keyword | Single-cell ATAC sequencing | - |
dc.subject.keyword | Single-cell RNA sequencing | - |
dc.citation.volume | 18 | - |
dc.citation.startPage | 1429 | - |
dc.citation.endPage | 1439 | - |
dc.identifier.bibliographicCitation | COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, Vol.18 : 1429-1439, 2020-06 | - |
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