ArticleNAR genomics and bioinformatics2025
A supervised Bayesian method for time (re)annotation of transcriptomics data.
Article in NAR genomics and bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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 method is based on a Bayesian approach that uses Gaussian process regression modeling. We show a use case of applying our method for assigning time annotations in legacy microarray samples of the bacterium
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