Solving the transcription start site identification problem with ADAPT-CAGE: a Machine Learning algorithm for the analysis of CAGE data
| Autor: | Nikos Perdikopanis, Georgios Georgakilas, Artemis G. Hatzigeorgiou |
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| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Gene isoform Computer science Bioinformatics Science Gene regulatory network Locus (genetics) Machine learning computer.software_genre 01 natural sciences Article Annotation 010104 statistics & probability 03 medical and health sciences Exon Transcription (biology) Gene expression medicine Physics::Atomic and Molecular Clusters 0101 mathematics Multidisciplinary business.industry Intron medicine.disease Quantitative Biology::Genomics Cap analysis gene expression Identification (information) 030104 developmental biology RNA splicing Benchmark (computing) Medicine Artificial intelligence business Algorithm computer Transcriptional noise |
| Zdroj: | Scientific Reports Scientific Reports, Vol 10, Iss 1, Pp 1-12 (2020) UnpayWall Microsoft Academic Graph DOAJ-Articles PubMed Central |
| ISSN: | 2045-2322 |
| 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. |
| Databáze: | OpenAIRE |
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