Condition-Specific Modeling of Biophysical Parameters Advances Inference of Regulatory Networks.

ABSTRACT: Large-scale inference of eukaryotic transcription-regulatory networks remains challenging. One underlying reason is that existing algorithms typically ignore crucial regulatory mechanisms, such as RNA degradation and post-transcriptional processing. Here, we describe InfereCLaDR, which incorporates such elements and advances prediction in Saccharomyces cerevisiae. First, InfereCLaDR employs a high-quality Gold Standard dataset that we use separately as prior information and for model validation. Second, InfereCLaDR explicitly models transcription factor activity and RNA half-lives. Third, it introduces expression subspaces to derive condition-responsive regulatory networks for every gene. InfereCLaDR's final network is validated by known data and trends and results in multiple insights. Fo

SUBMITTER: Tchourine K 

PROVIDER: S-EPMC5987223 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature