Parallel generated method of transcriptional regulatory networks. (17th September 2018)

• Record Type: Journal Article
• Authors: Liu, Shuai; Ta, Na; Lu, Mengye; Liu, Gaocheng; Bai, Weiling; Li, Wenhui
• Other Names: Sangaiah Arun Kumar guestEditor.; Pham Hoang guestEditor.; Qiu Tie guestEditor.; Muhammad Khan guestEditor.; Awan Irfan guestEditor.; Younas Muhammad guestEditor.; Hussain Farookh guestEditor.
• Is Part Of: Concurrency and computation. Volume 32:Number 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-09-17
• Subjects: CM -- MCF‐7 -- parallel computing -- transcription factor -- transcriptional regulatory network; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4938
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Generated method of transcriptional regulatory networks remains an important research in biology. Many approaches have been proposed to construct transcriptional regulatory networks. However, with the increase of ChIP‐seq and RNA‐seq data, the speed of constructing transcriptional regulation networks is still a challenge. Moreover, parallel computing lacks application in constructing gene regulatory networks through the analysis of the relationships between transcription factors (TFs) and target genes (TGs). Therefore, in this paper, a parallel generated method of transcriptional regulatory networks was proposed. First, two datasets, Michigan Cancer Foundation – 7 (MCF‐7) and Cardiomyocytes (CM) were applied. Then, a parallel method was used to generate transcriptional regulatory network with their transcription factors (TFs) and target genes (TGs). Finally, experimental results showed that 61% regulatory relations in MCF‐7 were validated in the Gene Expression Omnibus (GEO), while 29% results needed further experimental verification. Besides, 56% regulatory relations in CM were consistent with GEO, while 33% results were not yet verified. Furthermore, speed of parallel algorithm was faster than traditional serial algorithm in generating transcriptional regulatory networks.