ReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiation.

Regulatory elements control gene expression through transcription initiation (promoters) and by enhancing transcription at distant regions (enhancers). Accurate identification of regulatory elements is fundamental for annotating genomes and understanding gene expression patterns. While there are man...

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Autores principales:	Ramzan Umarov, Yu Li, Takahiro Arakawa, Satoshi Takizawa, Xin Gao, Erik Arner
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/d8d98f21607c4485a344943049cf5b7b
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spelling	oai:doaj.org-article:d8d98f21607c4485a344943049cf5b7b2021-12-02T19:57:50ZReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiation.1553-734X1553-735810.1371/journal.pcbi.1009376https://doaj.org/article/d8d98f21607c4485a344943049cf5b7b2021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009376https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Regulatory elements control gene expression through transcription initiation (promoters) and by enhancing transcription at distant regions (enhancers). Accurate identification of regulatory elements is fundamental for annotating genomes and understanding gene expression patterns. While there are many attempts to develop computational promoter and enhancer identification methods, reliable tools to analyze long genomic sequences are still lacking. Prediction methods often perform poorly on the genome-wide scale because the number of negatives is much higher than that in the training sets. To address this issue, we propose a dynamic negative set updating scheme with a two-model approach, using one model for scanning the genome and the other one for testing candidate positions. The developed method achieves good genome-level performance and maintains robust performance when applied to other vertebrate species, without re-training. Moreover, the unannotated predicted regulatory regions made on the human genome are enriched for disease-associated variants, suggesting them to be potentially true regulatory elements rather than false positives. We validated high scoring "false positive" predictions using reporter assay and all tested candidates were successfully validated, demonstrating the ability of our method to discover novel human regulatory regions.Ramzan UmarovYu LiTakahiro ArakawaSatoshi TakizawaXin GaoErik ArnerPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 9, p e1009376 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Ramzan Umarov Yu Li Takahiro Arakawa Satoshi Takizawa Xin Gao Erik Arner ReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiation.
description	Regulatory elements control gene expression through transcription initiation (promoters) and by enhancing transcription at distant regions (enhancers). Accurate identification of regulatory elements is fundamental for annotating genomes and understanding gene expression patterns. While there are many attempts to develop computational promoter and enhancer identification methods, reliable tools to analyze long genomic sequences are still lacking. Prediction methods often perform poorly on the genome-wide scale because the number of negatives is much higher than that in the training sets. To address this issue, we propose a dynamic negative set updating scheme with a two-model approach, using one model for scanning the genome and the other one for testing candidate positions. The developed method achieves good genome-level performance and maintains robust performance when applied to other vertebrate species, without re-training. Moreover, the unannotated predicted regulatory regions made on the human genome are enriched for disease-associated variants, suggesting them to be potentially true regulatory elements rather than false positives. We validated high scoring "false positive" predictions using reporter assay and all tested candidates were successfully validated, demonstrating the ability of our method to discover novel human regulatory regions.
format	article
author	Ramzan Umarov Yu Li Takahiro Arakawa Satoshi Takizawa Xin Gao Erik Arner
author_facet	Ramzan Umarov Yu Li Takahiro Arakawa Satoshi Takizawa Xin Gao Erik Arner
author_sort	Ramzan Umarov
title	ReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiation.
title_short	ReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiation.
title_full	ReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiation.
title_fullStr	ReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiation.
title_full_unstemmed	ReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiation.
title_sort	refeafi: genome-wide prediction of regulatory elements driving transcription initiation.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/d8d98f21607c4485a344943049cf5b7b
work_keys_str_mv	AT ramzanumarov refeafigenomewidepredictionofregulatoryelementsdrivingtranscriptioninitiation AT yuli refeafigenomewidepredictionofregulatoryelementsdrivingtranscriptioninitiation AT takahiroarakawa refeafigenomewidepredictionofregulatoryelementsdrivingtranscriptioninitiation AT satoshitakizawa refeafigenomewidepredictionofregulatoryelementsdrivingtranscriptioninitiation AT xingao refeafigenomewidepredictionofregulatoryelementsdrivingtranscriptioninitiation AT erikarner refeafigenomewidepredictionofregulatoryelementsdrivingtranscriptioninitiation
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ReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiation.

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