Generating transcriptional regulatory networks from time-ordered stem cell differentiation RNA sequencing data

Yu-Shuen Tsai; Yao-Ming Chang; Yang-Mooi Lim; Soon-Keng Cheong; I-Fang Chung; Chee-Yin Wong

doi:10.1016/j.xpro.2022.101541

Generating transcriptional regulatory networks from time-ordered stem cell differentiation RNA sequencing data

STAR Protoc. 2022 Aug 19;3(3):101541. doi: 10.1016/j.xpro.2022.101541. eCollection 2022 Sep 16.

Authors

Yu-Shuen Tsai¹, Yao-Ming Chang², Yang-Mooi Lim³, Soon-Keng Cheong³, I-Fang Chung⁴, Chee-Yin Wong⁵

Affiliations

¹ Center for Systems and Synthetic Biology, National Yang Ming Chiao Tung University, Taipei, Taiwan.
² Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
³ Faculty of Medicine and Health Sciences, Universiti Tunku Abdul Rahman, Selangor, Malaysia.
⁴ Center for Systems and Synthetic Biology, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan; Preventive Medicine Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan. Electronic address: ifchung@nycu.edu.tw.
⁵ Faculty of Medicine and Health Sciences, Universiti Tunku Abdul Rahman, Selangor, Malaysia. Electronic address: wongcy@1utar.my.

Abstract

We describe steps to 1) identify ascending and descending monotonic key genes from time-ordered stem cell differentiation expression data, 2) construct time-ordered transcriptional regulatory networks, and 3) infer the involvement of transcription factors along the differentiation process. For complete details on the use and execution of this protocol, please refer to Wong et al. (2020).

Keywords: Bioinformatics; Cell Differentiation; Genomics; RNAseq; Sequence analysis; Stem Cells.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Cell Differentiation / genetics
Exome Sequencing
Gene Regulatory Networks* / genetics
RNA*
Sequence Analysis, RNA

Substances

RNA