Overview
- Provides statistical tools for working with the latest research data in NGS
- Contains chapters written by leading statisticians in the field of NGS
- Useful for students and researchers that work in biomedical research and genomic medicine
- Includes supplementary material: sn.pub/extras
Part of the book series: Frontiers in Probability and the Statistical Sciences (FROPROSTAS)
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Table of contents (20 chapters)
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Front Matter
Keywords
About this book
Next Generation Sequencing (NGS) is the latest high throughput technology to revolutionize genomic research. NGS generates massive genomic datasets that play a key role in the big data phenomenon that surrounds us today. To extract signals from high-dimensional NGS data and make valid statistical inferences and predictions, novel data analytic and statistical techniques are needed. This book contains 20 chapters written by prominent statisticians working with NGS data. The topics range from basic preprocessing and analysis with NGS data to more complex genomic applications such as copy number variation and isoform expression detection. Research statisticians who want to learn about this growing and exciting area will find this book useful. In addition, many chapters from this book could be included in graduate-level classes in statistical bioinformatics for training future biostatisticians who will be expected to deal with genomic data in basic biomedical research, genomic clinical trials and personalized medicine.
About the editors:
Somnath Datta is Professor and Vice Chair of Bioinformatics and Biostatistics at the University of Louisville. He is Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics and Elected Member of the International Statistical Institute. He has contributed to numerous research areas in Statistics, Biostatistics and Bioinformatics.
Dan Nettleton is Professor and Laurence H. Baker Endowed Chair of Biological Statistics in the Department of Statistics at Iowa State University. He is Fellow of the American Statistical Association and has published research on a variety of topics in statistics, biology and bioinformatics.
Reviews
From the book reviews:
“This book is an excellent collection of 20 chapters presenting the state of art (as of 2014) of algorithms developed for the analysis of next generation sequencing (NGS) data. … This book is a valuable and well-timed collection of articles on the statistical methods that can be applied on NGS data. Even if no prior NGS knowledge is required, the book is addressed mainly to researchers at postgraduate and post-doc levels.” (Irina Ioana Mohorianu, zbMATH, Vol. 1297, 2014)
Editors and Affiliations
About the editors
About the editors:
Somnath Datta is Professor and Vice Chair of Bioinformatics and Biostatistics at the University of Louisville. He is Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics, and Elected Member of the International Statistical Institute. He has contributed to numerous research areas in Statistics, Biostatistics and Bioinformatics.
Dan Nettleton is Professor and Laurence H. Baker Endowed Chair of Biological Statistics in the Department of Statistics at Iowa State University. He is Fellow of the American Statistical Association and has published research on a variety of topics in statistics, biology, and bioinformatics.
Bibliographic Information
Book Title: Statistical Analysis of Next Generation Sequencing Data
Editors: Somnath Datta, Dan Nettleton
Series Title: Frontiers in Probability and the Statistical Sciences
DOI: https://doi.org/10.1007/978-3-319-07212-8
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing Switzerland 2014
Hardcover ISBN: 978-3-319-07211-1Published: 21 July 2014
Softcover ISBN: 978-3-319-37905-0Published: 17 September 2016
eBook ISBN: 978-3-319-07212-8Published: 03 July 2014
Series ISSN: 2624-9987
Series E-ISSN: 2624-9995
Edition Number: 1
Number of Pages: XIV, 432
Number of Illustrations: 19 b/w illustrations, 68 illustrations in colour
Topics: Statistics for Life Sciences, Medicine, Health Sciences, Human Genetics, Cancer Research