Title

Elucidation of the sequential transcriptional activity in Escherichia coli using time-series RNA-seq data

Authors

Pui Shan Wong¹, Kosuke Tashiro², Satoru Kuhara², Sachiyo Aburatani^1,3,*

Affiliation

¹Biotechnology Research Institute for Drug Discovery, National Institute of AIST, Tokyo, Japan;

²Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, Fukuoka, Japan;

³Com. Bio Big Data Open Innovation Lab. (CBBD-OIL), National Institute of AIST, Tokyo, Japan

Email

s.aburatani@aist.go.jp;

Article Type

Hypothesis

Date

Received December 26, 2016; Accepted January 25, 2017; Published January 31, 2017

Functional genomics and gene regulation inference has readily expanded our knowledge and understanding of gene interactions with regards to expression regulation. With the advancement of transcriptome sequencing in time-series comes the ability to study the sequential changes of the transcriptome. Here, we present a new method to augment regulation networks accumulated in literature with transcriptome data gathered from time-series experiments to construct a sequential representation of transcription factor activity. We apply our method on a time-series RNA-Seq data set of Escherichia coli as it transitions from growth to stationary phase over five hours and investigate the various activity in gene regulation process by taking advantage of the correlation between regulatory gene pairs to examine their activity on a dynamic network. We analyse the changes in metabolic activity of the pagP gene and associated transcription factors during phase transition, and visualize the sequential transcriptional activity to describe the change in metabolic pathway activity originating from the pagP transcription factor, phoP. We observe a shift from amino acid and nucleic acid metabolism, to energy metabolism during the transition to stationary phase in E. coli.

Keywords

Escherichia coli, gene regulation, network, time-series

Citation

Wong et al. Bioinformation 13(1): 25-30 (2017)

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.