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A Survey of Bioinformatics-Based Tools in RNA-Sequencing (RNA-Seq) Data Analysis

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Translational Bioinformatics and Its Application

Part of the book series: Translational Medicine Research ((TRAMERE))

Abstract

The capability of next-generation sequencing can be understood by one of its techniques like RNA sequencing (RNA-Seq) that deals with the transcriptome complexity in a powerful and cost-effective way. This technique has emerged as a revolutionary tool with high sensitivity and accuracy over old techniques. Additionally, this technique also gives unprecedented ability to detect novel mRNA transcripts as well as ncRNAs and analyze alternative splicing. Being a high-throughput sequencing technique, it poses a great demand for bioinformatics-based analysis of the generated data. Here, we explain how RNA-Seq data can be analyzed, discuss its challenges, and provide an overview of the data analysis methods/tools. We discuss strategies for quality check, map**, and differential expression in transcriptomic data along with discussions on lately developed strategies for alternative splicing and isoform quantification. We also mention some useful R/Bioconductor packages for aforementioned tasks.

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Acknowledgments

The authors would like to thank University Grant Commission, India for the support. The authors express their gratitude to Nimisha Chaturvedi, Dr. Raghvendra Singh, and Swadha Singh for giving valuable suggestions regarding the improvement of this chapter.

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Authors and Affiliations

  1. Center of Bioinformatics, Institute of Inter Disciplinary Studies, Nehru Science Center, University of Allahabad, Allahabad, 211002, Uttar Pradesh, India

    Pallavi Gaur

  2. Department of Statistics, Nehru Science Center, University of Allahabad, Allahabad, 211002, Uttar Pradesh, India

    Anoop Chaturvedi

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  1. Pallavi Gaur
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Correspondence to Pallavi Gaur .

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Editors and Affiliations

  1. State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China

    Dong-Qing Wei

  2. Center for Neurosciences, The Feinstein Institute for Medical Research, New York, USA

    Yilong Ma

  3. Department of Molecular Medicine, Hofstra Northwell School of Medicine, New York, USA

    William C.S. Cho

  4. Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, Hong Kong

    Qin Xu

  5. College of Computer Science & Technology, Jilin University, Changchun, Jilin, China

    Fengfeng Zhou

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Gaur, P., Chaturvedi, A. (2017). A Survey of Bioinformatics-Based Tools in RNA-Sequencing (RNA-Seq) Data Analysis. In: Wei, DQ., Ma, Y., Cho, W., Xu, Q., Zhou, F. (eds) Translational Bioinformatics and Its Application. Translational Medicine Research. Springer, Dordrecht. https://doi.org/10.1007/978-94-024-1045-7_10

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