Comparative genomic analysis of the human genome and six bat genomes using unsupervised machine learning: Mb-level CpG and TFBS islands.

ABSTRACT:

Background

Emerging infectious disease-causing RNA viruses, such as the SARS-CoV-2 and Ebola viruses, are thought to rely on bats as natural reservoir hosts. Since these zoonotic viruses pose a great threat to humans, it is important to characterize the bat genome from multiple perspectives. Unsupervised machine learning methods for extracting novel information from big sequence data without prior knowledge or particular models are highly desirable for obtaining unexpected insights. We previously established a batch-learning self-organizing map (BLSOM) of the oligonucleotide composition that reveals novel genome characteristics from big sequence data.

Results

In this study, using the oligonucleotide BLSOM, we conducted a comparative genomic study of humans and six bat specie

SUBMITTER: Iwasaki Y 

PROVIDER: S-EPMC9264310 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature