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

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Publikace nespadá pod Pedagogickou fakultu, ale pod Středoevropský technologický institut. Oficiální stránka publikace je na webu muni.cz.
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GEORGAKILAS Georgios PERDIKOPANIS N. HATZIGEORGIOU A.

Rok publikování 2020
Druh Článek v odborném periodiku
Časopis / Zdroj Nature Scientific Reports
Fakulta / Pracoviště MU

Středoevropský technologický institut

Citace
www https://www.nature.com/articles/s41598-020-57811-3.pdf
Doi http://dx.doi.org/10.1038/s41598-020-57811-3
Klíčová slova ANALYSIS GENE-EXPRESSION; CORE PROMOTER; ELEMENTS
Popis 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.
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