Open AccessPhyloPGM: boosting regulatory function prediction accuracy using evolutionary information
Author(s) -
Faizy Ahsan,
Zichao Yan,
Doina Precup,
Mathieu Blanchette
Publication year - 2022
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btac259
Subject(s) - computational biology , computer science , boosting (machine learning) , context (archaeology) , probabilistic logic , sequence (biology) , function (biology) , data mining , extant taxon , genomics , dna binding site , gene regulatory network , machine learning , biology , genome , gene , artificial intelligence , genetics , gene expression , evolutionary biology , promoter , paleontology
The computational prediction of regulatory function associated with a genomic sequence is of utter importance in -omics study, which facilitates our understanding of the underlying mechanisms underpinning the vast gene regulatory network. Prominent examples in this area include the binding prediction of transcription factors in DNA regulatory regions, and predicting RNA-protein interaction in the context of post-transcriptional gene expression. However, existing computational methods have suffered from high false-positive rates and have seldom used any evolutionary information, despite the vast amount of available orthologous data across multitudes of extant and ancestral genomes, which readily present an opportunity to improve the accuracy of existing computational methods.