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이수인 (Su-In Lee) 저자 이메일 보기
University of Washington
조회 250  인쇄하기 주소복사 트위터 공유 페이스북 공유 
AIControl: replacing matched control experiments with machine learning improves ChIP-seq peak identification
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ChIP-seq is a technique to determine binding locations of transcription factors, which remains a central challenge in molecular biology. Current practice is to use a ‘control’ dataset to remove background signals from a immunoprecipitation (IP) ‘target’ dataset. We introduce the AIControl framework, which eliminates the need to obtain a control dataset and instead identifies binding peaks by estimating the distributions of background signals from many publicly available control ChIP-seq datasets. We thereby avoid the cost of running control experiments while simultaneously increasing the accuracy of binding location identification. Specifically, AIControl can (i) estimate background signals at fine resolution, (ii) systematically weigh the most appropriate control datasets in a data-driven way, (iii) capture sources of potential biases that may be missed by one control dataset and (iv) remove the need for costly and time-consuming control experiments. We applied AIControl to 410 IP datasets in the ENCODE ChIP-seq database, using 440 control datasets from 107 cell types to impute background signal. Without using matched control datasets, AIControl identified peaks that were more enriched for putative binding sites than those identified by other popular peak callers that used a matched control dataset. We also demonstrated that our framework identifies binding sites that recover documented protein interactions more accurately.

- 형식: Research article
- 게재일: 2019년 03월 (BRIC 등록일 2019-03-15)
- 연구진: 국외연구진
- 분야: Genetics, Genomics
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Google (by Su-In Lee)
Pubmed (by Su-In Lee)
프리미엄 Bio일정 Bio일정 프리미엄 안내
제2회 오가노이드 심포지엄 및 핸즈온워크샵 [2019 CHA Organoid Center Symposium]
제2회 오가노이드 심포지엄 및 핸즈온워크샵 [2019 CHA Organoid Center Symposium]
날짜: 2019.05.09
장소: 판교 차바이오컴플렉스 국제회의실
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