Edward Curry - Introduction to Bioinformatics with R: A Practical Guide for Biologists

Introduction to
Bioinformatics with R

Chapman & Hall/CRC Mathematical and Computational Biology

About the Series

This series aims to capture new developments and summarize what is known over the entire spectrum of mathematical and computational biology and medicine. It seeks to encourage the integration of mathematical, statistical, and computational methods into biology by publishing a broad range of textbooks, reference works, and handbooks. The titles included in the series are meant to appeal to students, researchers, and professionals in the mathematical, statistical, and computational sciences and fundamental biology and bioengineering, as well as interdisciplinary researchers involved in the field. The inclusion of concrete examples and applications and programming techniques and examples is highly encouraged.

Series Editors

Xihong Lin

Mona Singh

N. F. Britton

Anna Tramontano

Maria Victoria Schneider

Nicola Mulder

Introduction to Proteins

Structure, Function, and Motion, Second Edition

Amit Kessel, Nir Ben-Tal

Big Data in Omics and Imaging

Integrated Analysis and Causal Inference

Momiao Xiong

Computational Blood Cell Mechanics

Road Towards Models and Biomedical Applications

Ivan Cimrak, Iveta Jancigova

An Introduction to Systems Biology

Design Principles of Biological Circuits, Second Edition

Uri Alon

Computational Biology

A Statistical Mechanics Perspective, Second Edition

Ralf Blossey

Computational Systems Biology Approaches in Cancer Research

Inna Kuperstein and Emmanuel Barillot

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

For more information about this series please visit: https://www.crcpress.com/Chapman--HallCRC-Mathematical-and-Computational-Biology/book-series/CHMTHCOMBIO

Introduction to
Bioinformatics with R

A Practical Guide for Biologists

Edward Curry

First edition published 2020

by CRC Press

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Contents

This book would not have been possible without the excellent students I taught over nearly a decade at Imperial College London. I consider myself extremely grateful to have had the opportunity to teach them, and to learn from them. Likewise, to work with and learn from colleagues who have become life-long friends: Adam Beech, Emma Bell, Charlotte Wilhelm-Benartzi, Nair Bonito, Paula Cunnea, Kirsty Flower, Ian Garner, Ian Green, Erick Loomis, Alun Passey, Euan Stronach, Angela Wilson and many others. I feel I particularly need to thank Professor Bob Brown for his professional support and guidance, and James Flanagan for running the MRes Cancer Informatics course with me. I am also grateful to Philippe Sanseau and the Computational Biology team at GSK, for welcoming me into an exciting research environment.

Special thanks to my wife Vaughan and my family for their invaluable personal support, and to David Grubbs at Taylor & Francis for giving me the opportunity to turn my collection of tutorials into this book.

This is really all about data. In particular, its about working with so much data that learning to program computers to perform calculations for us will save a lot of time, and probably make possible analysis that would otherwise be impossible. In biological research, the amount of data available to researchers has increased so much over recent years this has been described as a data explosion[].

Much of this biological data is freely available for any researcher to access and use in their own work. Therefore, any biological scientist who learns skills to enable obtaining, preprocessing and analyzing publically-available datasets, is giving themselves an advantage when it comes to making the most out of their own opportunities.

One consequence of this increase in biological data is that many of the recent paradigms of molecular biology come from computational analysis of large collections of data. In terms of developing an intuition for what is shown when results from computational analysis is presented in a paper, there is no substitute for first-hand experience of using a method for data analysis in your own research (of course, a theoretical understanding of the method in question is also important!). In reality, it is becoming increasingly difficult to understand the current state of the art in biological research without some experience and understanding of computational biology.

In 2014, the UKs MRC and BBSRC (Medical Research Council and Biotechnology & Biological Sciences Research Council) produced a report of skills vulnerabilities, which reflected important research capabilities lacking in the UK. Both in 2014 and in a 2017 update, computational methods for biological research were identified as key weaknesses. In fact, the following specific points were highlighted:

Data analytics, especially bioinformatics, appear to be particularly vulnerable.

Informatics skills are applicable to many areas of both the biosciences and the medical sciences.

Maths, statistics and computational biology skills are lacking particularly at the postgraduate and postdoctoral levels, with many respondents reporting difficulties in recruiting adequately skilled researchers at these levels; shortages are not just restricted to the UK.

So there is a recognized international shortage of bioinformatics skills, and these skills are increasingly fundamental across all areas of biological research. You were probably already aware of this given youre reading this, but it hopefully serves as a motivating reminder that learning the bioinformatics skills taught in this book will be worth the effort you put in!