Advanced Applications of RNA Sequencing and Challenges

Author:

Han Yixing1,Gao Shouguo2,Muegge Kathrin13,Zhang Wei4,Zhou Bing5

Affiliation:

1. Mouse Cancer Genetics Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, USA.

2. Bioinformatics and Systems Biology Core, National Heart Lung Blood Institute, National Institutes of Health, Rockville Pike, Bethesda, MD, USA.

3. Leidos Biomedical Research, Inc., Basic Science Program, Frederick National Laboratory, Frederick, MD, USA.

4. Department of Medicine, University of California, San Diego, La Jolla, CA, USA.

5. Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA.

Abstract

Next-generation sequencing technologies have revolutionarily advanced sequence-based research with the advantages of high-throughput, high-sensitivity, and high-speed. RNA-seq is now being used widely for uncovering multiple facets of transcriptome to facilitate the biological applications. However, the large-scale data analyses associated with RNA-seq harbors challenges. In this study, we present a detailed overview of the applications of this technology and the challenges that need to be addressed, including data preprocessing, differential gene expression analysis, alternative splicing analysis, variants detection and allele-specific expression, pathway analysis, co-expression network analysis, and applications combining various experimental procedures beyond the achievements that have been made. Specifically, we discuss essential principles of computational methods that are required to meet the key challenges of the RNA-seq data analyses, development of various bioinformatics tools, challenges associated with the RNA-seq applications, and examples that represent the advances made so far in the characterization of the transcriptome.

Publisher

SAGE Publications

Subject

Applied Mathematics,Computational Mathematics,Computer Science Applications,Molecular Biology,Biochemistry

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