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Front Genet. 2016 Sep 21;7:163. eCollection 2016.

Single-Cell Transcriptomics Bioinformatics and Computational Challenges.

Author information

1
Epidemiology Program, University of Hawaii Cancer Center Honolulu, HI, USA.
2
Epidemiology Program, University of Hawaii Cancer CenterHonolulu, HI, USA; Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at ManoaHonolulu, HI, USA.

Abstract

The emerging single-cell RNA-Seq (scRNA-Seq) technology holds the promise to revolutionize our understanding of diseases and associated biological processes at an unprecedented resolution. It opens the door to reveal intercellular heterogeneity and has been employed to a variety of applications, ranging from characterizing cancer cells subpopulations to elucidating tumor resistance mechanisms. Parallel to improving experimental protocols to deal with technological issues, deriving new analytical methods to interpret the complexity in scRNA-Seq data is just as challenging. Here, we review current state-of-the-art bioinformatics tools and methods for scRNA-Seq analysis, as well as addressing some critical analytical challenges that the field faces.

KEYWORDS:

bioinformatics; heterogeneity; microevolution; single-cell analysis; single-cell genomics

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