A supervised Bayesian method for time (re)annotation of transcriptomics data.

ABSTRACT: Transcriptomics experiments are often conducted to capture changes in gene expression over time. However, time annotations may be missing, imprecise, or not reflect the same physiological state of the bacterial culture between different experiments. Assigning accurate time points to these experiments using a reference time course is therefore crucial for identifying differentially expressed genes, and understanding gene regulatory networks for elucidating the studied organism's physiology and life cycle. This important task, which could enhance the biological interpretation of the transcriptomics experiments, has not been previously addressed. In this work, we propose a novel method to solve the challenge of realigning transcriptomics experiments based on a reference time course. Our metho

SUBMITTER: Nushi E 

PROVIDER: S-EPMC12754789 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature