Combined statistical modeling enables accurate mining of circadian transcription.

ABSTRACT: Circadian-regulated genes are essential for tissue homeostasis and organismal function, and are therefore common targets of scrutiny. Detection of rhythmic genes using current analytical tools requires exhaustive sampling, a demand that is costly and raises ethical concerns, making it unfeasible in certain mammalian systems. Several non-parametric methods have been commonly used to analyze short-term (24 h) circadian data, such as JTK_cycle and MetaCycle. However, algorithm performance varies greatly depending on various biological and technical factors. Here, we present CircaN, an ad-hoc implementation of a non-linear mixed model for the identification of circadian genes in all types of omics data. Based on the variable but complementary results obtained through several biological

SUBMITTER: Rubio-Ponce A 

PROVIDER: S-EPMC8074341 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature