Ascertainment bias in the genomic test of positive selection on regulatory sequences.

ABSTRACT: Evolution of gene expression mediated by cis-regulatory changes is thought to be an important contributor to organismal adaptation, but identifying adaptive cis-regulatory changes is challenging due to the difficulty in knowing the expectation under no positive selection. A new approach for detecting positive selection on transcription factor binding sites (TFBSs) was recently developed, thanks to the application of machine learning in predicting transcription factor (TF) binding affinities of DNA sequences. Given a TFBS sequence from a focal species and the corresponding inferred ancestral sequence that differs from the former at n sites, one can predict the TF binding affinities of many n-step mutational neighbors of the ancestral sequence and obtain a null di

SUBMITTER: Jiang D 

PROVIDER: S-EPMC10473660 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature