인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Feature overlap is a critical concept in bioinformatics and occurs when two genomic intervals, usually represented as BED files, are located in the same genomic regions. Instead, feature proximity refers to the spatial proximity of genomic elements. For example, promoters typically overlap or are close to the genes they regulate. Overlap and proximity are also important in epigenetic studies. Here, the overlap of regions enriched for specific epigenetic modifications or accessible chromatin can elucidate complex molecular phenotypes. Consequently, the ability to analyze and interpret feature overlap and proximity is essential for understanding the biological processes that contribute to a given phenotype. To address this need, we present a computational method capable of analyzing data represented in the BED format. This method aims to quantitatively assess the degree of proximity or overlap between genomic features and to determine the statistical significance of these events in the context of a nonparametric randomization test. The aim is to understand whether the observed state differs from what would be expected by chance. The method is designed to be easy to use, requiring only a single command line to run, allowing straightforward overlap and proximity analysis. It also provides clear visualizations and publication-quality figures. In conclusion, this study highlights the importance of feature overlap and proximity in epigenetic studies and presents a method to improve the systematic assessment and interpretation of these features. We propose a new resource for identifying biologically significant interactions between genomic features in both healthy and disease states.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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