Altuna Akalin - Computational Genomics with R

About the Series:

This series aims to capture new developments in computational biology, as well as high-quality work sum- marizing or contributing to more established topics. Publishing a broad range of reference works, text- books, and handbooks, the series is designed to appeal to students, researchers, and professionals in all areas of computational biology, including genomics, proteomics, and cancer computational biology, as well as interdisciplinary researchers involved in associated fields, such as bioinformatics and systems biology.

Introduction to Bioinformatics with R: A Practical Guide for Biologists

Edward Curry

Analyzing High-Dimensional Gene Expression and DNA Methylation Data with R

Hongmei Zhang

Introduction to Computational Proteomics

Golan Yona

Glycome Informatics: Methods and Applications

Kiyoko F. Aoki-Kinoshita

Computational Biology: A Statistical Mechanics Perspective

Ralf Blossey

Computational Hydrodynamics of Capsules and Biological Cells

Constantine Pozrikidis

Computational Systems Biology Approaches in Cancer Research

Inna Kuperstein, Emmanuel Barillot

Clustering in Bioinformatics and Drug Discovery

John David MacCuish, Norah E. MacCuish

Metabolomics: Practical Guide to Design and Analysis

Ron Wehrens, Reza Salek

An Introduction to Systems Biology: Design Principles of Biological Circuits

2nd Edition

Uri Alon

Computational Biology: A Statistical Mechanics Perspective

Second Edition

Ralf Blossey

Stochastic Modelling for Systems Biology

Third Edition

Darren J. Wilkinson

Computational Genomics with R

Altuna Akalin

For more information about this series please visit:

https://www.routledge.com/Chapman--HallCRC-Computational-Biology-Series/book-series/CRCCBS

Dr. Altuna Akalin organized the book structure, wrote most of the book and edited the rest. He is a bioinformatics scientist and the head of Bioinformatics and Omics Data Science Platform at the Berlin Institute for Medical Systems Biology, Max Delbrck Center in Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He is interested in using machine learning and statistics to uncover patterns related to important biological variables such as disease state and type. He lived in the USA, Norway, Turkey, Japan and Switzerland in order to pursue research work and education related to computational genomics. The underlying aim of his current work is utilizing complex molecular signatures to provide decision support systems for disease diagnostics and biomarker discovery. In addition to the research efforts and the managing of a scientific lab, since 2015, he has been organizing and teaching computational genomics courses in Berlin with participants from across the world. This book is mostly a result of material developed for those and previous teaching efforts at Weill Cornell Medical College in New York and Friedrich Miescher Institute in Basel, Switzerland.

Dr. Akalin and the following contributing authors have decades of combined experience in data analysis for genomics. They are developers of Bioconductor packages such as methylKit.

Contributing authors

Dr. Bora Uyar, RNA-seq Analysis. He started his bioinformatics training in Sabanci University (Istanbul/Turkey), from which he got his undergraduate degree. Later, he obtained an MSc from Simon Fraser University (Vancouver/Canada), then a PhD from the European Molecular Biology Laboratory in Heidelberg/Germany. Since 2015, he has been working as a bioinformatics scientist at the Bioinformatics Platform and Omics Data Science Platform at the Berlin Institute for Medical Systems Biology. He has been contributing to the bioinformatics platform through research, collaborations, services and data analysis method development. His current primary research interest is the integration of multiple types of omics datasets to discover prognostic/diagnostic biomarkers of cancers.

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Dr. Vedran Franke, ChIP-seq Analysis. He received his PhD from the University of Zagreb. His work focused on the biogenesis and function of small RNA molecules during early embryogenesis, and establishment of pluripotency. Prior to his PhD, he worked as a scientific researcher under Boris Lenhard at the University of Bergen, Norway, focusing on principles of gene enhancer functions. He continues his research in the Bioinformatics and Omics Data Science Platform at the Berlin Institute for Medical System Biology. He develops tools for multi-omics data integration, focusing on single-cell RNA sequencing, and epigenomics. His integrated knowledge of cellular physiology along with his proficiency in data analysis enable him to find creative solutions to difficult biological problems.

Dr. Jonathan Ronen, a website which tracked Israeli politicians Facebook posts. He obtained a PhD in computational biology in 2020, where he has published tools for imputation for single cell RNA-seq using priors, and integrative analysis of multi-omics data using deep learning.

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First edition published 2021

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ISBN: 9781498781855 (hbk)

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To my family,
Anna, Julia and Gabriel

The aim of this book is to provide the fundamentals for data analysis for genomics. We developed this book based on the computational genomics courses we are giving every year. We have had invariably an interdisciplinary audience with backgrounds from physics, biology, medicine, math, computer science or other quantitative fields. We want this book to be a starting point for computational genomics students and a guide for further data analysis in more specific topics in genomics. This is why we tried to cover a large variety of topics from programming to basic genome biology. As the field is interdisciplinary, it requires different starting points for people with different backgrounds. A biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. In the same manner, a more experienced person might want to refer to this book when needing to do a certain type of analysis, but having no prior experience.