Multivariate Analyses of Codon Usage Biases

By Jean R. Lobry

A complete case study with all coding sequences from the bacteria Borrellia burgdorferi illustrates how multivariate analyses reveals evolutionary mechanisms acting at the molecular level. They are either mutationnal (symmetric and asymmetric directionnal mutation pressure) or selective (selection against head-on collisions or linked to gene expressivity or subcellular location).

• The main objective is to provide a complete and reproducible example of the power of multivariate analyses in this application field

• Language: English
• Publisher: Elsevier Science
• Release date: Nov 20, 2018
• ISBN: 9780128172513

Jean R. Lobry

Dr. Jean R. Lobry is a professor-researcher at Claude University Bernard Lyon as university lecturer since 1992 and then as a university professor in 2006

Book preview

Multivariate Analyses of Codon Usage Biases

Jean R. Lobry

Statistics for Bioinformatics Set

coordinated by

Guy Perrière

Table of Contents

Cover image

Title page

Dedication

Copyright

Acknowledgments

Introduction

I.1 Prerequisites and notations

I.2 The case under study is a genomic monster

1: Introduction to Correspondence Analysis

Abstract

1.1 Chapter objectives

1.2 Metric choice

1.3 Properties

2: Global Correspondence Analysis

Abstract

2.1 Data set

2.2 Running global correspondence analysis

2.3 The missing factor F0

2.4 First factor

2.5 Second and third factors

2.6 Fourth and fifth factors

3: Within and Between Correspondence Analysis

Abstract

3.1 Running the analyses

3.2 Synonymous codon usage (WCA)

3.3 Amino acid usage (BCA)

4: Internal Correspondence Analysis

Abstract

4.1 Running the analyses

4.2 Synonymous codon usage

4.3 Non-synonymous codon usage

Conclusion

Appendix 1

A1.1 Introduction

A1.2 Chapter 1

A1.3 Chapter 2

A1.4 Chapter 3

A1.5 Chapter 4

Appendix 2

A2.1 Session information

References

Index

Dedication

This book is dedicated to Tami and Noboru Sueoka

Copyright

First published 2018 in Great Britain and the United States by ISTE Press Ltd and Elsevier Ltd

Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:

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Notices

Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary.

Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility.

To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein.

For information on all our publications visit our website at http://store.elsevier.com/

© ISTE Press Ltd 2018

The rights of Jean R. Lobry to be identified as the author of this work have been asserted by him in accordance with the Copyright, Designs and Patents Act 1988.

British Library Cataloguing-in-Publication Data

A CIP record for this book is available from the British Library

Library of Congress Cataloging in Publication Data

A catalog record for this book is available from the Library of Congress

ISBN 978-1-78548-296-0

Printed and bound in the UK and US

Acknowledgments

Thanks are due to Anne-Béatrice DUFOUR, Laurent GUÉGUEN, Paweł MACKIEWICZ, Simon PENEL, Guy PERRIÈRE and Haruo SUZUKI.

This work was performed using the computing facilities of the CC LBBE/PRABI.

Introduction

I.1 Prerequisites and notations

statistical software [RCO 13] is assumed ¹ . We should be able to install and load the seqinr [CHA 07] and ade4 [CHE 04] packages. In the following, outputs are presented in black to distinguish them from inputs, and comments start with the # character:

library(seqinr) library(ade4) pi <- 3 # God’s approximation in 1 Kings 7:23 and 2 Chronicles 4:2 pi [1] 3 library(fortunes) fortune(pi <- 3) John Fox: I’ve never understood why it’s legal to change the built-in global constants in R, including T and F. That just seems to me to set a trap for users. Why not treat these as reserved symbols, like TRUE, Inf, etc.? Rolf Turner: I rather enjoy being able to set pi <- 3. -- John Fox and Rolf Turner   R-help (June 2013)

To avoid too much verbosity, the code used to produce figures is detailed in the Appendix. This book is composed with LATEX using the command Sweave() [outputs. For instance, the approximation of π that was used by the Babylonians defined in the above code could be referred to in the source text as \Sexpr{pi} and then automatically transformed into three in the final document.

Some code fragments that need an Internet connection or that are time consuming are encapsulated using a logical flag that must be set to TRUE to allow for their execution. The list of these flags is as follows:

# NIC: Need Internet Connection # CTI: Computer Time Intensive TODO <- FALSE # Global switch final.query0 <- TODO # NIC: get Bb chromosome for chirochore   structure final.stability <- TODO # NIC + CTI: codon usage in 12,317 bacteria final.query1 <- TODO  # NIC: get sequences and infos from remote   database final.query2 <- TODO # NIC: get ribosomal sequences informations final.query3 <- TODO  # NIC: get PheL phenylalanin operon leader   peptide final.query4 <- TODO # NIC: get GC content for Ixodes scapulatis CDS final.screeplot2 <- TODO  # CTI: many CA on simulated tables under H0 final.delrow <- TODO # CTI: many CA while deleting progressively     small CDS final.delcol <- TODO  # CTI: many CA while deleting prgressively   minor codons final.ttuco.eig <- TODO  # CTI: many WCA and BCA on simulated tables   under H0 final.ica.eig1 <- TODO  # CTI: many WVA and BCA for within-between   group analysis

final.ica.eig2 <- TODO     # CTI: many ICA on simulated tables under H0

Notes on Figure I.1

The colors and the shape of points used in this book to represent different classes of coding sequences are given in the top panel. Simulations with package dichromat [code available in Appendix 1, section A1.1.1.

Figure I.1 shows the colors used in this book; they were choosen to be discriminated by color blind people. Most figures should be understandable in black and white because the illustrative variables are also encoded by the shape of points. For figures that are in color, a link is provided that leads to a color version. Here is a brief description of these variables:

−Leading: used for a coding sequence, which is transcribed divergently from the origin of replication and then encoded in the leading strand for replication as illustrated in Figure I.4.

−Lagging: used for a coding sequence, which is transcribed convergently toward the origin of replication and then encoded in the lagging strand for replication. In bacteria, there is no documented example of a coding sequence being simultaneously leading and lagging².

−Ribosomal: used for a sequence coding of a ribosomal protein, which is used as a proxy for sequences with a high expressivity, that is with a high expression level in at least some environmental conditions.

−Integral membrane protein: used for a sequence coding of an integral membrane protein; its location in the hydrophobic phospholipid bilayer requires an enrichment in hydrophobic amino acids.

Figure I.1 Color code. For a color version of this figure, see www.iste.co.uk/lobry/multivariate.zip

An elementary knowledge, or at least practice, of data dimension reduction methods such as principal component analysis (PCA) is assumed. Figure I.2 provides a basic introduction to PCA.

Figure I.2 Principal component analysis in a nutshell. For a color version of this figure, see www.iste.co.uk/lobry/multivariate.zip

Let C be the set of the 64 possible codons:

   [I.1]

Let A be the set of the pseudo-amino acid Stp plus the 20 possible amino acids in proteins:

   [I.2]

A genetic code is a surjective function from C onto A: every element of C maps to one element in A, and every element of A is mapped to by some elements of C, as in Figure I.3, corresponding to the so-called universal genetic code. Codons for the same amino acid are termed synonymous’s prompt with the tablecode() function.

Figure I.3 The surjective nature of genetic code. For a color version of this figure, see www.iste.co.uk/lobry/multivariate.zip

Table I.1