Profiling transcription factor activity dynamics using intronic reads in time-series transcriptome data
Yan Wu1,2,3,4, Lingfeng Xue1,2,3, Wen Huang1,2,3
1Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
Plos Computational Biology
|January 10, 2022
Summary
We developed a new method using intron RNA reads to accurately measure transcription factor (TF) activity over time. This approach improves understanding of dynamic cellular processes like circadian rhythms and immune responses.
Area of Science:
- Molecular Biology
- Bioinformatics
- Genomics
Background:
- Transcription factor (TF) activity is crucial for dynamic cellular processes, but current methods using exon data lack temporal accuracy.
- Bioinformatic tools analyzing transcriptome data are advancing TF activity analysis.
Purpose of the Study:
- To develop a novel TF activity measure using intron-level information from time-series RNA-seq data.
- To enhance the temporal accuracy of TF activity estimation for dynamic biological processes.
Main Methods:
- Proposed a TF activity measure utilizing intron-level expression data from time-series RNA-seq.
- Implemented and validated the intron-based measure against exon-based methods.
- Applied the method to analyze circadian rhythms and T cell activation time-series data.
Main Results:
- Intron-based TF activity inference more accurately recapitulates instantaneous TF activities than exon-based measures.
- Improved characterization of temporal phasing for cycling TFs in circadian rhythm studies.
- Facilitated the discovery of temporally opposing TF modules during T cell activation.
Conclusions:
- The proposed intron-based method offers superior temporal resolution for TF activity analysis.
- This approach enhances the decoding of transcriptional regulation in dynamic cellular processes.
- Anticipated broad applicability for understanding global transcriptional architecture.
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