SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles | Zendy

Nan Papili Gao | Zendy; S. M. Minhaz Ud-Dean | Zendy; Olivier Gandrillon | Zendy; Rudiyanto Gunawan | Zendy

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SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles

Author(s) -

Nan Papili Gao,

S. M. Minhaz Ud-Dean,

Olivier Gandrillon,

Rudiyanto Gunawan

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx575

Subject(s) - expression (computer science) , gene expression , computer science , computational biology , gene , gene regulatory network , regulation of gene expression , regulatory sequence , regulator gene , biology , genetics , programming language

Single cell transcriptional profiling opens up a new avenue in studying the functional role of cell-to-cell variability in physiological processes. The analysis of single cell expression profiles creates new challenges due to the distributive nature of the data and the stochastic dynamics of gene transcription process. The reconstruction of gene regulatory networks (GRNs) using single cell transcriptional profiles is particularly challenging, especially when directed gene-gene relationships are desired.

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