Zheng - Computational non-coding RNA biology

First edition

Yun Zheng

Kunming University of Science and Technology, Institute of Primate Translational Medicine, Yunnan Key Laboratory of Primate Biomedical Research, Faculty of Information Engineering and Automation, Kunming, Yunnan, China

1. Tables in Chapter 1
2. Tables in Chapter 2
3. Tables in Chapter 3
4. Tables in Chapter 5
5. Tables in Chapter 6
6. Tables in Chapter 7

1. Figures in Chapter 1
2. Figures in Chapter 2
3. Figures in Chapter 3
4. Figures in Chapter 4
5. Figures in Chapter 5
6. Figures in Chapter 6
7. Figures in Chapter 7
8. Figures in Chapter 8
9. Figures in Chapter 9
10. Figures in Appendix A

Background

Outline

Small ncRNAs

Outline

MiRNA Targets

Outline

Long ncRNAs

Outline

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This book is for my family.

Yun Zheng

Yun Zheng is an Associate Professor of Bioinformatics at the Institute of Primate Translational Medicine, Yunnan Key Laboratory of Primate Biomedical Research, and at the Faculty of Information Engineering and Automation, Kunming University of Science and Technology at Kunming, Yunnan, China. He is an Academic Editor of the peer-reviewed scientific journal PLOS ONE. He was an Associate Professor of bioinformatics at Fudan University, China, from 2009 to 2013. He obtained his PhD in Computer Science from Nanyang Technological University in Singapore. He was a Research Fellow and a Post-doctoral Research Associate at the National University of Singapore and at Washington University in St. Louis, US, respectively. He was a Visiting Scholar at Oklahoma State University, US, in 2011 and 2013. He was a Visiting Professor at Nanyang Technological University, Singapore, in 2011 and 2013. He was a Visiting Professor at the University of Technology Sydney, Australia, in 2015. He is the chair of the 29th International Conference on Genome Informatics (GIW 2018). He has published more than 50 articles on bioinformatics and non-coding RNAs in various peer-reviewed publications, including Nature Genetics, Genome Biology, Nucleic Acids Research, Developmental Cell, PLOS Genetics, New Phytologist, Cancer Letters, Plant Journal, Scientific Reports, BMC Genomics, Plant Science, and PLOS ONE.

Yun Zheng Kunming, China

Non-coding RNAs (ncRNAs) have become critical molecules in almost all biological processes and human diseases. With the development of high throughput sequencing technologies, our knowledge of the scale of transcriptome has fundamentally changed. While only 2% of the human genome are protein coding genes, 60%75% of the human genome is transcribed into different types of RNAs. Consequently, in the last few years many new ncRNAs have come into our view.

In early years, the studies of ncRNAs were performed in a low-throughput way. Today, a single study could easily generate even several terabyte (TB) of data with hundreds of RNA-seq profiles. This large amount of data naturally needs novel ways, especially computational methods, of accurate and efficient analysis. Although some algorithms have been developed for the identification and quantification of some ncRNAs, these algorithms or methods are distributed in many sources and are often specialized for one type of ncRNA. Currently, it still lacks a unified source that clearly and comprehensively shows the detailed computational steps for identifying and characterizing the ever-expanding repository of ncRNAs from the profuse sequencing data.

To fill the gap between the large amount of sequencing data and the accurate annotations of ncRNAs, this book aims to provide practical computational methods or pipelines and skills for performing diverse analyses of different types of ncRNAs, mainly microRNA (miRNA), small interfering RNA (siRNA), long non-coding RNA (lncRNA), and circular RNA (circRNA), using diverse high throughput sequencing profiles. More emphasis is given to the identification and quantification of ncRNAs, including miRNA, trans-acting siRNA, phased siRNA, long non-coding RNA, circular intronic RNA (or lariat RNA), and circular RNA originating from back-spliced exons. The functional mechanisms of most miRNAs and some siRNAs have been clearly characterized. This book includes two chapters that introduce how to identify miRNA targets in both animals and plants, with and without the high throughput sequencing based technologies. The mutation and editing sites in miRNAs may lead to radical changes in either the expression levels of miRNAs or in the targets of miRNAs, which may cause severe human diseases. Thus, the book includes a chapter to show how to identify mutation and editing sites in miRNAs from small RNA high throughput sequencing profiles, and to show how to integrate the genome sequencing profile of the same sample to distinguish the mutation and editing sites identified in the sRNA-seq profile. The book also shows how to identify deregulated ncRNAs, to perform clustering, and to perform Principle Component Analysis using the expression levels of different types of ncRNAs. For more downstream analyses, after ncRNAs have been identified and quantified, readers can choose from many other works about machine learning and data mining.