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.
