Solving the transcription start site identification problem with ADAPT-CAGE: a Machine Learning algorithm for the analysis of CAGE data

Abstract Cap Analysis of Gene Expression (CAGE) has emerged as a powerful experimental technique for assisting in the identification of transcription start sites (TSSs). There is strong evidence that CAGE also identifies capping sites along various other locations of transcribed loci such as splicin...

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Autores principales:	Georgios K. Georgakilas, Nikos Perdikopanis, Artemis Hatzigeorgiou
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3ea25c0969444078827e4deb02a46119
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spelling	oai:doaj.org-article:3ea25c0969444078827e4deb02a461192021-12-02T15:23:48ZSolving the transcription start site identification problem with ADAPT-CAGE: a Machine Learning algorithm for the analysis of CAGE data10.1038/s41598-020-57811-32045-2322https://doaj.org/article/3ea25c0969444078827e4deb02a461192020-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-57811-3https://doaj.org/toc/2045-2322Abstract Cap Analysis of Gene Expression (CAGE) has emerged as a powerful experimental technique for assisting in the identification of transcription start sites (TSSs). There is strong evidence that CAGE also identifies capping sites along various other locations of transcribed loci such as splicing byproducts, alternative isoforms and capped molecules overlapping introns and exons. We present ADAPT-CAGE, a Machine Learning framework which is trained to distinguish between CAGE signal derived from TSSs and transcriptional noise. ADAPT-CAGE provides highly accurate experimentally derived TSSs on a genome-wide scale. It has been specifically designed for flexibility and ease-of-use by only requiring aligned CAGE data and the underlying genomic sequence. When compared to existing algorithms, ADAPT-CAGE exhibits improved performance on every benchmark that we designed based on both annotation- and experimentally-driven strategies. This performance boost brings ADAPT-CAGE in the spotlight as a computational framework that is able to assist in the refinement of gene regulatory networks, the incorporation of accurate information of gene expression regulators and alternative promoter usage in both physiological and pathological conditions.Georgios K. GeorgakilasNikos PerdikopanisArtemis HatzigeorgiouNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Georgios K. Georgakilas Nikos Perdikopanis Artemis Hatzigeorgiou Solving the transcription start site identification problem with ADAPT-CAGE: a Machine Learning algorithm for the analysis of CAGE data
description	Abstract Cap Analysis of Gene Expression (CAGE) has emerged as a powerful experimental technique for assisting in the identification of transcription start sites (TSSs). There is strong evidence that CAGE also identifies capping sites along various other locations of transcribed loci such as splicing byproducts, alternative isoforms and capped molecules overlapping introns and exons. We present ADAPT-CAGE, a Machine Learning framework which is trained to distinguish between CAGE signal derived from TSSs and transcriptional noise. ADAPT-CAGE provides highly accurate experimentally derived TSSs on a genome-wide scale. It has been specifically designed for flexibility and ease-of-use by only requiring aligned CAGE data and the underlying genomic sequence. When compared to existing algorithms, ADAPT-CAGE exhibits improved performance on every benchmark that we designed based on both annotation- and experimentally-driven strategies. This performance boost brings ADAPT-CAGE in the spotlight as a computational framework that is able to assist in the refinement of gene regulatory networks, the incorporation of accurate information of gene expression regulators and alternative promoter usage in both physiological and pathological conditions.
format	article
author	Georgios K. Georgakilas Nikos Perdikopanis Artemis Hatzigeorgiou
author_facet	Georgios K. Georgakilas Nikos Perdikopanis Artemis Hatzigeorgiou
author_sort	Georgios K. Georgakilas
title	Solving the transcription start site identification problem with ADAPT-CAGE: a Machine Learning algorithm for the analysis of CAGE data
title_short	Solving the transcription start site identification problem with ADAPT-CAGE: a Machine Learning algorithm for the analysis of CAGE data
title_full	Solving the transcription start site identification problem with ADAPT-CAGE: a Machine Learning algorithm for the analysis of CAGE data
title_fullStr	Solving the transcription start site identification problem with ADAPT-CAGE: a Machine Learning algorithm for the analysis of CAGE data
title_full_unstemmed	Solving the transcription start site identification problem with ADAPT-CAGE: a Machine Learning algorithm for the analysis of CAGE data
title_sort	solving the transcription start site identification problem with adapt-cage: a machine learning algorithm for the analysis of cage data
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/3ea25c0969444078827e4deb02a46119
work_keys_str_mv	AT georgioskgeorgakilas solvingthetranscriptionstartsiteidentificationproblemwithadaptcageamachinelearningalgorithmfortheanalysisofcagedata AT nikosperdikopanis solvingthetranscriptionstartsiteidentificationproblemwithadaptcageamachinelearningalgorithmfortheanalysisofcagedata AT artemishatzigeorgiou solvingthetranscriptionstartsiteidentificationproblemwithadaptcageamachinelearningalgorithmfortheanalysisofcagedata
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Solving the transcription start site identification problem with ADAPT-CAGE: a Machine Learning algorithm for the analysis of CAGE data

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