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Quantitative Biology

, Volume 6, Issue 3, pp 195–209 | Cite as

Modeling and analysis of RNA-seq data: a review from a statistical perspective

  • Wei Vivian Li
  • Jingyi Jessica LiEmail author
Review
First Online:

Abstract

Background

Since the invention of next-generation RNA sequencing (RNA-seq) technologies, they have become a powerful tool to study the presence and quantity of RNA molecules in biological samples and have revolutionized transcriptomic studies. The analysis of RNA-seq data at four different levels (samples, genes, transcripts, and exons) involve multiple statistical and computational questions, some of which remain challenging up to date.

Results

We review RNA-seq analysis tools at the sample, gene, transcript, and exon levels from a statistical perspective. We also highlight the biological and statistical questions of most practical considerations.

Conclusions

The development of statistical and computational methods for analyzing RNA-seq data has made significant advances in the past decade. However, methods developed to answer the same biological question often rely on diverse statistical models and exhibit different performance under different scenarios. This review discusses and compares multiple commonly used statistical models regarding their assumptions, in the hope of helping users select appropriate methods as needed, as well as assisting developers for future method development.
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Keywords

RNA-seq statistical modeling differentially expressed genes alternatively spliced exons isoform reconstruction and quantification 
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Notes

Acknowledgements

This work was supported by the following grants: National Science Foundation DMS-1613338, NIH/NIGMS R01GM120507, PhRMA Foundation Research Starter Grant in Informatics, Johnson & Johnson WiSTEM2D Award, and Sloan Research Fellowship (to J.J.L) and the UCLA Dissertation Year Fellowship (to W.V.L). The authors would like to thank the insightful feedbacks from Dr. Lior Pachter at California Institute of Technology and Dr. Michael I. Love at University of North Carolina at Chapel Hill.

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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of California, Los AngelesLos AngelesUSA
  2. 2.Department of Human GeneticsUniversity of California, Los AngelesLos AngelesUSA

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