Computational and Statistical Epigenomics

by Andrew E. Teschendorff

Overview

This book introduces the reader to modern computational and statistical tools for translational epigenomics research. Over the last decade, epigenomics has emerged as a key area of molecular biology, epidemiology and genome medicine. Epigenomics not only offers us a deeper understanding of fundamental cellular biology, but also provides us with the basis for an improved understanding and management of complex diseases. From novel biomarkers for risk prediction, early detection, diagnosis and prognosis of common diseases, to novel therapeutic strategies, epigenomics is set to play a key role in the personalized medicine of the future. In this book we introduce the reader to some of the most important computational and statistical methods for analyzing epigenomic data, with a special focus on DNA methylation. Topics include normalization, correction for cellular heterogeneity, batch effects, clustering, supervised analysis and integrative methods for systems epigenomics. This book will be of interest to students and researchers in bioinformatics, biostatistics, biologists and clinicians alike.

Dr. Andrew E. Teschendorff is Head of the Computational Systems Genomics Lab at the CAS-MPG Partner Institute for Computational Biology, Shanghai, China, as well as an Honorary Research Fellow at the UCL Cancer Institute, University College London, UK.

Product Details

ISBN-13:	9789401799263
Publisher:	Springer Netherlands
Publication date:	05/14/2015
Series:	Translational Bioinformatics , #7
Edition description:	2015
Pages:	217
Product dimensions:	6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

Part 1: Normalization and Analysis Methods for DNA methylation and ChIP-Seq data.- Part 2: Integrative and Medical Epigenomics.

Editorial Reviews

Reviewer: Pooja Sethi, MD (East Tennessee State University Quillen College of Medicine)
Description: Translational bioinformatics is defined as the development of storage-related, analytical, and interpretive methods to optimize the transformation of the increasing volume of biomedical data, and genomic data in particular, into proactive, predictive, preventive, and participatory health. This volume in the Translational Bioinformatics series introduces modern computational and statistical tools for translational epigenomic research with a special focus on DNA methylation.
Purpose: Over the past decade, epigenomics has emerged as a key area of molecular biology, epidemiology, and genome medicine. This book is as a powerful and integrative tool for understanding and translating discoveries and advances of genomic, transcriptomic, proteomic, and bioinformatics technologies into the study of human disease.
Audience: The book is aimed primarily at students, clinicians, and researchers with a basic background in bioinformatics and biostatistics who are interested in learning some key concepts in computational and statistical methods for analyzing epigenomic data.
Features: This book is edited by Dr. Andrew E. Teschendorff, a well-known figure in computational genomics. This series in translational bioinformatics focuses on articles/chapters presenting significant recent work in genomic, transcriptomic, proteomic, and bioinformatic profiles related to human organ or cell dysfunction and clinical findings. It includes bioinformatics-driven molecular and cellular disease mechanisms, the understanding of human diseases, and its use in the improvement of patient prognoses. Additionally, it provides practical and useful study insights into protocols of design and methodology. Topics covered in this book include normalization, correction for cellular heterogeneity, batch effects, clustering, supervised analysis, and integrative methods for stem epigenomics. The book is divided into two parts. The first part focuses on normalization and analysis methods for DNA methylation and ChIP-Seq data. The second past focuses on integrative and medical epigenomics. Chapters are uniformly formatted, and move from basic to more advanced concepts as the book progresses. The book includes color diagrams, bar graphs, and tables for easier understanding. In addition, chapters are well referenced for more extensive reading on the topic if readers so desire.
Assessment: This is a valuable resource for students, clinicians, and researchers who wish to understand the basics of modern computational and statistical tools for translational epigenomics research. The analytical, storage-related, and interpretive methods discussed in this book hold great promise in helping improve prediction, early diagnosis, severity monitoring, therapeutic effect, and prognosis of human diseases.

Doody's Review Service

This volume in the Translational Bioinformatics series introduces modern computational and statistical tools for translational epigenomic research with a special focus on DNA methylation. … This is a valuable resource for students, clinicians, and researchers who wish to understand the basics of modern computational and statistical tools for translational epigenomics research. The analytical, storage-related, and interpretive methods discussed in this book hold great promise in helping improve prediction, early diagnosis, severity monitoring, therapeutic effect, and prognosis of human diseases.” (Pooja Sethi, Doody’s Book Reviews, July, 2015)

From the Publisher