Problem Solving Handbook in Computational Biology and Bioinformatics

Lenwood S. Heath Naren Ramakrishnan

Problem Solving Handbookin Computational Biologyand Bioinformatics

Editors•

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All rights reserved. This work may not be translated or copied in whole or in part without the written

10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connectionwith any form of information storage and retrieval, electronic adaptation, computer software, or by similaror dissimilar methodology now known or hereafter developed is forbidden.

not identified as such, is not to be taken as an expression of opinion as to whether or not they are subjectto proprietary rights.

Springer is part of Springer Science+Business Media (www.springer.com)

Springer New York Dordrecht Heidelberg London

© Springer Science+Business Media, LLC 2011

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permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY

The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are

e-ISBN 978-0-387-09760-2DOI 10.1007/978-0-387-09760-2

Library of Congress Control Number: 2010938722

heath@vt.edu

EditorsLenwood S. HeathDepartment of Computer ScienceVirginia Tech

24061-0106 Blacksburg VirginiaUSA

Department of Computer ScienceVirginia Tech

24061-0106 Blacksburg VirginiaUSA

Naren Ramakrishnan

114 McBryde Hall 114 McBryde Hall

naren@cs.vt.edu

LSH acknowledges the loving support ofDeanie, James, and Kaitlin, who are alwayshis inspiration. NR dedicates this book toKayar and Anant for their unwavering love.

Preface

Bioinformatics is today a breadth-wise subject spanning practically every aspectof the life sciences, from studying DNA sequences, to modeling the structure andfunction of proteins, to unraveling the interactions between proteins, and finally tocapturing the relationship with phenotypes of organisms. While there are several ex-cellent textbooks and monographs covering every aspect of the field, there is a needto collect together, in one place, the algorithms and methods that form the modernbioinformatician’s toolkit. The Problem Solving Handbook in Computational Biol-ogy and Bioinformatics was conceived to fill this need.

In organizing this book, we have consciously chosen those topics that have strongalgorithmic or methodological underpinnings and that are now widely used in arange of bioinformatics investigations, such as functional genomics, haplotype stud-ies, and simulation of disease pathways. The intended audience for this book arepractitioners of bioinformatics algorithms. The coverage is not (intended to be) ex-haustive.

The chapters are written by experts in their respective disciplines and are closelyorganized with an introduction to the underlying problem/task/domain, detailed al-gorithmic descriptions, available software implementations, applications, and ad-vanced topics. For the benefit of the reader, exercises and references to the literaturefor further reading are also provided.

The five sections of the handbook focus on algorithms for sequences, phylo-genetics, proteins, networks, and biological data management/mining, respectively.The sequences section begins with an introduction to BLAST—arguably the mostubiquitous bioinformatics algorithm—by Jian Ma and Louxin Zhang, including adiscussion of its recent incarnations. This is followed by a chapter on practical mul-tiple sequence alignment algorithms by Tobias Rausch and Knut Reinert. In additionto the algorithmic details, this chapter presents the historical context surroundingthis domain and how it has influenced the development of methods. The final chap-ter in this section, by John Spouge, focuses more generally on sequence alignmentstatistics, in particular how to assign p-values to alignment scores.

The second section of the handbook addresses the modeling of evolutionary rela-tionships. The first chapter here, by Paul Marjoram and Paul Joyce, focuses on an in-

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troduction to coalescent theory and its applications, as a framework for evolutionaryanalysis. In the second chapter, Laxmi Parida dwelves further into coalescence andpresents a graph-theoretic framework to understand ‘recombinomics.’ Paul Ryvkinand Li-San Wang then present a chapter on the more specific problem of phyloge-netic tree reconstruction from sequence information. They cover the broad classesof algorithms in this domain and their modeling assumptions. These ideas are gen-eralized in the next chapter on phylogenetic networks by Luay Nakhleh, whichfocuses on the modeling of non-treelike evolutionary behaviors. The final chapteron genome-wide association studies by Paola Sebastiani and Nadia Solovieff helpsmake the connection from sequences and phylogeny to phenotypes.

The third section focuses on the multifaceted world of proteins. Proteins are richin multiple aspects of biology: structure, function, biochemistry, to name a fewaspects. Our section focuses on two of these aspects. Bonnie Berger and JeromeWaldispuhl introduce the protein structure prediction problem and the latest devel-opments on that front. Yang Cao then describes how networks of proteins can besimulated, specifically using stochastic methods.

The fourth section describes algorithms for networks, as broadly modeled inbioinformatics. Christopher Lasher, Christopher Poirel, and T. M. Murali introducecellular response networks as a mechanism to both integrate diverse sources of dataand to understand how distinct stresses manifest in the cellular state of a cell. SinanErten and Mehmet Koyuturk focus on protein-protein interaction networks and al-gorithms for identifying modules in them.

The final section is broadly about biological data management and mining. Thefirst chapter here, by Paola Sebastiani, Jacqui Milton, and Ling Wang, is about thedesign and organization of microarray experiments. The second chapter, by KarthikDevarajan, focuses on matrix decompositions as a specific algorithmic techniquefor analyzing many kinds of bioinformatics data sets. The third chapter, by RachaelHuntley, Emily Dimmer, and Rolf Apweiler, describes the Gene Ontology Resourceand the myriad uses it has come to serve in modern bioinformatics research.

We hope this book will serve as a useful companion to the bioinformatics practi-tioner. We would like to thank Springer publishing for encouraging us to undertakethis project and providing constant guidance throughout the process. In particular,Susan Lagerstrom-Fife has been very supportive of this project every step of theway. Jennifer Maurer helped us organize the various chapters and indexes in a co-herent manner. Many thanks to the authors for their enthusiastic participation andresponding to our queries on time.

Blacksburg, VA, Lenwood S. HeathJuly, 2010 Naren Ramakrishnan

Contents

Part I Sequences

Modern BLAST Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Jian Ma and Louxin Zhang

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Available Implementations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Algorithm Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 BLAST Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 Advanced Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Practical Multiple Sequence Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Tobias Rausch and Knut Reinert

1 History of the Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212 Algorithm Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 Available Implementations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364 Advanced Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Sequence Alignment Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45John L. Spouge

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 462 Dynamic Programming with Independent Random Inputs . . . . . . . 473 The Extreme-Value Distributions for Maxima . . . . . . . . . . . . . . . . . . 494 The Poisson Approximation for Counting Rare Events . . . . . . . . . . 505 Pairwise Sequence Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 516 Islands in Local Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

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7 The Finite-Size Correction in Gapped Local Alignment . . . . . . . . . 548 The Independent Diagonals Approximation . . . . . . . . . . . . . . . . . . . 559 The Combinatorial Approximation . . . . . . . . . . . . . . . . . . . . . . . . . . . 56References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

Part II Phylogenetics

Practical Implications of Coalescent Theory . . . . . . . . . . . . . . . . . . . . . . . . . 63Paul Marjoram and Paul Joyce

1 Introduction — What is the coalescent? . . . . . . . . . . . . . . . . . . . . . . . 632 Motivating Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 663 Implications of the Coalescent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 694 Software Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 765 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 796 Exercises for the reader . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

Graph Model of Coalescence with Recombinations . . . . . . . . . . . . . . . . . . . 85Laxmi Parida

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 852 Random Graph Framework: Pedigree Graph . . . . . . . . . . . . . . . . . . . 873 Pedigree Subgraphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 904 Unilinear Transmission: Monochromatic Subgraphs . . . . . . . . . . . . 925 Genetic Exchange Model: Mixed Subgraph . . . . . . . . . . . . . . . . . . . 936 Topological definition of GMRCA: Least Common Ancestor

with Ancestry (LCAA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 967 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

Phylogenetic Trees From Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101Paul Ryvkin and Li-San Wang

1 Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1012 Sequence evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1063 Distance-based phylogeny reconstruction . . . . . . . . . . . . . . . . . . . . . 1104 Maximum parsimony . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1145 Maximum likelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1156 Multiple phylogenies: comparison, consensus, and confidence . . . . 1177 Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1218 Further reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1219 Exercise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

Evolutionary Phylogenetic Networks: Models and Issues . . . . . . . . . . . . . . 125Luay Nakhleh

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1252 Phylogenetic Networks and the Trees Within . . . . . . . . . . . . . . . . . . 127

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3 Optimization Criteria for Inferring and Evaluating PhylogeneticNetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

4 To Network, or Not to Network, That Is the Question . . . . . . . . . . . 1455 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1536 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

Genome Wide Association Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159Paola Sebastiani and Nadia Solovieff

1 An overview of population genetics . . . . . . . . . . . . . . . . . . . . . . . . . . 1592 Genome-Wide Association Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . 1623 Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1724 Advanced Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1725 Exercise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174

Part III Proteins: Structure, Function, and Biochemistry

Novel Perspectives on Protein Structure Prediction . . . . . . . . . . . . . . . . . . . 179Bonnie Berger, Jerome Waldispuhl

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1792 Modeling transmembrane β -barrel structure . . . . . . . . . . . . . . . . . . . 1823 Energy model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1864 Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1885 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1936 Sampling the local neighborhood of 3D structures . . . . . . . . . . . . . . 2007 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2038 Further reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204

Stochastic Simulation for Biochemical Systems . . . . . . . . . . . . . . . . . . . . . . . 209Yang Cao

1 History of the Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2092 Algorithm Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2113 Available Implementations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2164 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2215 Advanced Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2236 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2277 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228

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Cellular Response Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233Christopher D. Lasher, Christopher L. Poirel, and T. M. Murali

1 History of the Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2332 Algorithm Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2343 Available Implementations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2444 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2455 Advanced Topics: Comparing Response Networks . . . . . . . . . . . . . . 2476 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2497 Outlook and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250

Identification of Modules in Protein-Protein Interaction Networks . . . . . . 253Sinan Erten and Mehmet Koyuturk

1 History of the Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2542 Algorithm Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2553 Available Implementations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2614 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2625 Advanced Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2636 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2647 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265

Part V Biological Data Management and Mining

Designing Microarray Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271Paola Sebastiani, Jacqui Milton, and Ling Wang

1 Designed experiments versus observational studies . . . . . . . . . . . . . 2712 Discovery of Differentially Expressed Genes . . . . . . . . . . . . . . . . . . 2743 Building prognostic models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2794 Running the experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2815 Advanced topics and further reading . . . . . . . . . . . . . . . . . . . . . . . . . 2856 Exercise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287

Matrix and Tensor Decompositions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291Karthik Devarajan

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2912 Non-negative Matrix Factorization . . . . . . . . . . . . . . . . . . . . . . . . . . . 2923 Singular Value Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2954 Other Matrix Decompositions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2995 Comparison of the Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3026 Tensor Decompositions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3087 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3108 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312

Part IV Networks

Contents xiii

Practical Applications of the Gene Ontology Resource . . . . . . . . . . . . . . . . . 319Rachael P. Huntley, Emily C. Dimmer, and Rolf Apweiler

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3192 GO Annotations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3203 Viewing the GO and its annotations . . . . . . . . . . . . . . . . . . . . . . . . . . 3244 Use of GO in the literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3265 Popular methods for supplementing and grouping GO annotations 3286 The effective use of GO for large-scale analyses . . . . . . . . . . . . . . . . 3347 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3358 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341

List of Contributors

Rolf ApweilerEuropean Bioinformatics Institute, e-mail: apweiler@ebi.ac.uk

Bonnie BergerDepartment of Mathematics & Computer Science and AI Lab, MIT, Cambridge,MA, USA, e-mail: bab@mit.edu

Yang CaoDepartment of Computer Science, Virginia Tech, Blacksburg, VA, USA, e-mail:ycao@cs.vt.edu

Karthik DevarajanDepartment of Biostatistics & Bioinformatics, Fox Chase Cancer Center, Philadel-phia, PA USA, e-mail: karthik.devarajan@fccc.edu

Emily C. DimmerEuropean Bioinformatics Institute, e-mail: edimmer@ebi.ac.uk

Sinan ErtenDepartment of Electrical Engineering & Computer Science, Case Western ReserveUniversity, Cleveland, OH, USA, e-mail: sinan.erten@case.edu

Rachael P. HuntleyEuropean Bioinformatics Institute, e-mail: huntley@ebi.ac.uk

Paul JoyceUniversity of Idaho, e-mail: joyce@uidaho.edu

Mehmet KoyuturkDepartment of Electrical Engineering & Computer Science, Case Western ReserveUniversity, Cleveland, OH, USA, e-mail: koyuturk@eecs.case.edu

Christopher D. LasherGenetics, Bioinformatics, and Computational Biology Program, Virginia Polytech-nic Institute and State University, Blacksburg, VA, USA, e-mail: lasher@vt.edu

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xvi List of Contributors

Jian MaUniversity of California at Santa Cruz, e-mail: jianma@soe.ucsc.edu

Paul MarjoramUniversity of Southern California, e-mail: pmarjora@usc.edu

Jacqui MiltonBoston University, Boston MA, e-mail: jnmilton@gmail.com

T. M. MuraliDepartment of Computer Science, Virginia Polytechnic Institute and StateUniversity, Blacksburg, VA, USA, e-mail: murali@cs.vt.edu

Luay NakhlehRice University, e-mail: nakhleh@cs.rice.edu

Laxmi ParidaIBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA, e-mail:parida@us.ibm.com

Christopher L. PoirelDepartment of Computer Science, Virginia Polytechnic Institute and StateUniversity, Blacksburg, VA, USA, e-mail: poirel@vt.edu

Tobias RauschFreie Universitat, e-mail: rausch@inf.fu-berlin.de

Knut ReinertFreie Universitat, e-mail: reinert@inf.fu-berlin.de

Paul RyvkinGenomics and Computational Biology Graduate Program, University of Pennsyl-vania, e-mail: pry@mail.med.upenn.edu

Paola SebastianiBoston University, Boston MA, e-mail: sebas@bu.edu

Nadia SolovieffBoston University, Boston MA, e-mail: ntimofee@bu.edu

John L. SpougeNational Center for Biotechnology Information, National Library of Medicine,National Institutes of Health, Department of Health and Human Services, Bethesda,MD 20894, e-mail: spouge@ncbi.nlm.nih.gov

Jerome WaldispuhlSchool of Computer Science, McGill University, Montreal, QC, Canada, e-mail:jeromew@cs.mcgill.ca

Ling WangNovartis Vaccines and Diagnostics, Emeryville CA, e-mail: ling-1.wang@novartis.com

List of Contributors xvii

Li-San WangPenn Center for Bioinformatics, e-mail: lswang@mail.med.upenn.edu

Louxin ZhangNational University of Singapore, e-mail: matzlx@nus.edu.sg

Part ISequences

This first part of the book covers basic algorithms that work with biological se-quences, from sequence search to multiple sequence alignment.

Modern BLAST Programs

Jian Ma and Louxin Zhang

Abstract The Basic Local Alignment Search Tool (BLAST) is arguably the mostwidely used program in bioinformatics. By sacrificing sensitivity for speed, it makessequence comparison practical on huge sequence databases currently available. Theoriginal version of BLAST was developed in 1990. Since then it has spawned avariant of specialized programs. This chapter surveys the development of BLASTand BLAST-like programs for homology search, discusses alignment statistics thatare used in assessment of reported matches in BLAST, and provides the reader withguidance to select appropriate programs and set proper parameters to match researchrequirements.

1 Introduction

The sequence structures of genes and proteins are conserved in nature. It is com-mon to observe strong sequence similarity between a protein and its counterpart inanother species that diverged hundreds of millions of years ago. Accordingly, thebest method to identify the function of a new gene or protein is to find its sequence-

The Basic Local Alignment Search Tool (BLAST) is a computer program for

Jian Ma

Louxin Zhang

3

sequence comparison practical on huge sequence databases currently available, such

that finds short matches between query and database sequences and then attempts todesigned for comparing a query sequence against a target database. It is a heuristic

University of California at Santa Cruz, e-mail: jianma@soe.ucsc.edu

start alignments from these ‘seed hits’. By sacrificing sensitivity for speed, it makes

L.S. Heath and N. Ramakrishnan (eds.), Problem Solving Handbook in Computational

related genes or proteins whose functions are already known.

Biology and Bioinformatics, DOI 10.1007/978-0-387-09760-2_1,

finding regions of local similarity between two DNA or protein sequences. It is

©

as GenBank, which has over 80 million sequence records as of August 2008. In

National University of Singapore, e-mail: matzlx@nus.edu.sg

Springer Science+Business Media, LLC 2011

4 Jian Ma and Louxin Zhang

addition to generating local alignments, BLAST also provides statistical assessmentof reported alignments. Because of these powerful features, BLAST is one of themost widely used bioinformatics tools.

BLAST analysis is often used to identify conserved sequence patterns and toestablish functional or evolutionary relationships among proteins. It finds numerousapplications in molecular biology, evolutionary biology, and drug discovery.

The original version of BLAST [6] was developed by Altschul, Gish, Lipman,Miller and Myers in 1990. The improved version PSI-BLAST [7] was made avail-able in 1997. Over the intervening years, the original version has been customizedinto a set of specialized programs. These new variants of BLAST handle homol-ogy searches on different types of databases. They were designed to find gappedlocal alignments and to detect weak signals in sequence alignment. Table 3 lists thepopular BLAST programs together with their functions.

The rest of this chapter is divided into seven sections. Section 2 describes theavailable BLAST programs and other BLAST-like programs. Sections 3 and 4present the algorithmic and statistical aspects of BLAST, respectively. Section 5describes two practical examples of using BLAST. Through these examples, weexamine the biological and statistical information output from BLAST. Section 6addresses three advanced issues of using BLAST homology search. Section 7 listssome exercises for the reader to master BLAST programs. Finally, we summarizethe most relevant and useful references on BLAST for further reading in Section 8.

2 Available Implementations

From a user point of view, based on different purposes, a BLAST search generallyinvolves three important parts: input, database searched against, and a particularBLAST program.

On NCBI BLAST web site, the available databases can be categorized into pro-tein databases and nucleotide databases. Frequently used databases are summa-rized in Table 1 and Table 2. In addition, NCBI also provides specialized BLASTdatabases, e.g. genome databases for different species, trace databases, as well asvarious databases for model organisms.

Table 1 Main protein sequence databases for BLAST

Database Description

nr Non-redundant collections from GenBank CDS translations,PDB, SwissProt, PIR, and PRF

month The nr updates in the last 30 daysrefseq Protein sequences from the RefSeq projectswissprot SWISS-PROT protein sequence databasepdb Sequences from the 3-dimensional structure records in PDB

Modern BLAST Programs 5

Table 2 Main nucleotide sequence databases for BLAST

Database Description

nr All sequences in GenBank, EMBL, DDBJ, PDB except EST,STS, GSS, etc.

month The nr updates in the last 30 daysrefseq mrna mRNA sequences from the RefSeq Projectrefseq genomic Genomic sequences from the RefSeq Projectest EST sequences in GenBank, EMBL, and DDBJgss Genome Survey Sequence

A family of BLAST programs have been developed since its original versionwas launched in 1990. The difference mainly comes from the input type and thedatabases that the input is searched against. For example, BLASTN is useful toidentify an unknown nucleotide sequence or to search homologous genomic se-quences in different organisms. The major BLAST programs on the NCBI website are summarized in Table 3. These programs can be used via a web interface(http://www.ncbi.nlm.nih.gov/blast) or as stand-alone tools.

Apart from the set of programs on the NCBI BLAST server, there are otherBLAST-like homology search programs and web servers. Table 4 lists a few widely-used tools. On WU-BLAST server, BLAST programs available on NCBI are alsoavailable, but all the programs were implemented differently. WU-BLAST also in-cludes other tools developed in Warren Gish’s lab.

FASTA is a sequence similarity search program first developed by Lipman andPearson in NCBI. Its sequence format, called the FASTA format, has been widelyadopted for sequence comparison. It uses a multiple-step approach to aligning thequery and target sequences. It first finds runs of ktup or more identities, which arecalled word matches. Here ktup is a program parameter used for controlling thesensitivity and speed of the program. From these identified word matches, it deter-mines a band in which good alignments likely locate and then calculates the optimalalignment in the band using the dynamic programming method.

Sequence Search and Alignment by Hashing Algorithm (SSAHA) is developedto search a large DNA database efficiently. The essential idea is to preprocess thesequences in a database by breaking them into consecutive k-tuples of k contiguousbases and then using a hash table to store them. Therefore, searching for a querysequence in the database is done by obtaining from the hash table the ‘hits’ for eachk-tuple in the query sequence and then performing a sort on the results.

Sim4 employs a BLAST-based approach. It first determines the maximal scoringgap-free segments and then extends these segments into the adjacent regions greed-ily. It can be downloaded from Webb Miller’s lab and installed in a standalone work-station. It can also be run through the web server http://pbil.univ-lyon1.fr/sim4.php.

BLAT is an alignment tool like BLAST, and it is extremely efficient, developedby Jim Kent. On DNA sequences, BLAT works by keeping an index of an entiregenome, consisting of all non-overlapping 11-mers, which makes BLAT quicklyfind sequences of 95% and greater similarity of length 40 bases or more. However,

6 Jian Ma and Louxin Zhang

Table 3 Major BLAST programs on the NCBI web site

Program Description

BLASTN Search a nucleotide sequence against a nucleotide sequence databaseBLASTP Search an amino acid sequence against a protein sequence databaseBLASTX Search a nucleotide sequence translated in all reading frames against a pro-

tein sequence databaseTBLASTN Search a protein sequence against a nucleotide sequence database dynami-

cally translated in all reading framesTBLASTX Search the six-frame translations of a nucleotide sequence against the six-

frame translations of a nucleotide sequence databaseMEGABLAST [29] Find long alignments between very similar sequences more efficientlyPSI-BLAST [7] Find members of a protein family or build a custom position-specific score

matrixPHI-BLAST [28] Find proteins similar to the query around a given pattern

it is less sensitive to more divergent or short sequence alignments. On protein se-quences, BLAT uses 4-mers, rapidly finding protein sequences of 80% and greatersimilarity to the query of length longer than 20 amino acids. However, it is far lesssensitive than BLAST and PSI-BLAST at NCBI.

3 Algorithm Description

Alignment is a way of arranging two DNA or protein sequences to identify regionsof similarity that are conserved among species. Each aligned sequence appears as arow within a matrix. Gaps are inserted between the residues of each sequence so thatidentical or similar bases in different sequences are aligned in successive positions.Each gap spans one or more columns within the alignment matrix. The score ofan alignment is calculated by summing the rewarding scores for match columnsthat contain the same bases and the penalty scores for gaps and mismatch columns

Table 4 Other BLAST-like programs

Program Description URL Refs

WU-BLAST Washington University BLAST http://blast.wustl.edu/ [17]FASTA Homology search against Protein or

DNA databaseshttp://fasta.bioch.virginia.edu/ [26]

SSAHA Fast matching and alignment of DNAsequences

http://www.sanger.ac.uk/ [24]

Sim4 Homology search of an expressed DNAsequence (EST, cDNA, mRNA) with agenomic sequence

http://www.bx.psu.edu/miller lab/ [15]

BLAT BLAST-Like Alignment Tool http://genome.ucsc.edu/ [21]

Modern BLAST Programs 7

that contain different bases. A scoring scheme specifies the scores for matchesand mismatches, which form the scoring matrix, and the scores for gaps, calledthe gap cost. There are two types of alignments for sequence comparison. Given ascoring scheme, calculating a global alignment is a kind of global optimization that‘forces’ the alignment to span the entire length of two query sequences, whereaslocal alignments just identify regions of high similarity within two sequences.

The original version of BLAST finds good ungapped local alignments betweenthe query and database sequences [6]. Accordingly, it is also called ungappedBLAST. Database sequences are usually called target sequences. To speed up thehomology search process, BLAST employs a filtration strategy: It first scans thedatabase for length-w word matches of alignment score at least T between the queryand target sequences and then extends each match in both ends to generate localalignment (in the sequences) whose alignment score is larger than a threshold S.The matches are called high-scoring segment pairs (HSPs). BLAST outputs a list ofHSPs together with E-values that measure how frequent such HSPs would occur bychance.

3.1 Phase 1: Scan the Database for Match Hits

Consider a set of parameters w, T and S. A sequence of length w is called a w-mer.For a query sequence, a w-mer is called a neighborhood sequence if it forms a matchof alignment score at least T with some w-mer in the query sequence. We illustratethis concept using a DNA query sequence.

Consider query sequence Q: GCATTGACCC and parameters w = 8,T = 6. Un-der a simple scoring scheme by which matches and mismatches score 1 and -1 re-spectively, the neighborhood sequences that match 8-mer GCATTGAC in the querysequence are all 1-mismatch 8-mers:

.CATTGAC, G.ATTGAC, GC.TTGAC, GCA.TGAC,GCAT.GAC, GCATT.AC, GCATTG.C, GCATTGA.,

where ’.’ stands for any letter of A, G, C, and T. Similarly, the set of neighborhoodsequences also include the following sequences:

.ATTGACC, C.TTGACC, CA.TGACC, CAT.GACC,CATT.ACC, CATTG.CC, CATTGA.C, CATTGAC.,.TTGACCC, A.TGACCC, AT.GACCC, ATT.ACCC,ATTG.CCC, ATTGA.CC, ATTGAC.C, ATTGACC.,

which match 8-mers CATTGACC or ATTGACCC.The set of neighborhood sequences is efficiently constructed from the query se-

quence since there are at most 4w neighborhood sequences. Having the set of neigh-borhood sequences, the next task is to check whether each neighborhood sequenceoccurs in the target sequence or not. Such an occurrence of a neighborhood sequenceis called a seed hit. For example, for target sequence

8 Jian Ma and Louxin Zhang

T : ATAGCATGGACTTGACCCCGGCATTGTCATCG,the 8-mer GCATTGAC hits T at positions 4 and 21, whereas the 8-mer ATTGACCChits T at position 11. Here seed hits are not perfect. In fact, BLAST programs useperfect hits for DNA sequence search and imperfect hits whose score is higher thana threshold. All the hits can be identified using an efficient data structure such ashash table, suffix tree, or suffix array. The reader is referred to the book [12] ofChao and Zhang for implementation details.

The sensitivity and speed of BLAST search are closely related to the match sizew. When w is large, the BLAST search is fast but has low sensitivity in the sensethat it may miss short homologous sequences. In contrast, when w is small, it isslower, but has high sensitivity. The w is set by default to 11 and 3 for BLASTNand BLASTP, respectively. To achieve the optimal balance between sensitivity andspeed, the discontiguous MEGABLAST finds l-mer pairs that match in w discon-tiguous positions specified by a fixed pattern. Such a pattern is called a spaced seed.For example, one default spaced seed used for searching non-coding sequences is111∗1∗11∗∗1∗11∗111. When such a spaced seed is used, two 18-mers match ifthey have identical nucleotides in the positions indicated by the 1s: 1, 2, 3, 5, 7, 8,11, 13, 14, 16, 17, 18. It is first observed by Ma, Tromp, and Li that an optimallyspaced seed significantly improves homology search sensitivity [23].

3.2 Phase 2: Hit extension

In the second phase, ungapped BLAST extends each ‘seed’ hit in both directions togenerate a HSP and outputs this HSP if its alignment score is S or greater. At eachend, the extension includes aligned pairs in successive positions, with correspondingincrements to the alignment score. It continues until the alignment score drops morethan X below the maximum score that has attained up to that position.

It was observed that ungapped BLAST consumes more than 90% of the runningtime in hit extension. It was also observed that an HSP usually contains multiplehits that are close to one another. Accordingly, Gapped BLAST uses double hits totrigger hit extension to generate high-scoring gapped local alignments. It starts theextension process only if there are two non-overlapping hits within Dg positions,where the subscript g indicates that it is a parameter for Gapped BLAST. Theseadjacent non-overlapping hits can be detected if all hit positions are maintained.

In Gapped BLAST, gap extension is done by using the dynamic programmingapproach. Since the approach takes quadratic time, the extension process is muchslower than ungapped one. Here two more ideas are employed in order to handlegap extension more efficiently. One idea is only to extend those HSPs that havealignment score Sg or greater. The threshold Sg is determined in such a way that onlyone gap extension is invoked on average in per 50 database sequences. Another ideafor handling the extension is to restrict gapped extension to those positions in whichthe optimal local alignment score drops no more than Xg below the maximum localalignment score attained up to the position.

Modern BLAST Programs 9

4 BLAST Statistics

An important feature of BLAST is that it rank-orders the reported HSPs by E-values. For a local alignment of score s, an E-value of 10−5 is often used as a cutofffor BLAST homology search. It means that with a collection of random query se-quences, only once in a hundred thousand instances would an alignment with thatscore or greater occur by chance. The smaller the E-value, the greater the belief thatthe aligned sequences are homologous.

The E-values for HSPs in BLAST printout are calculated based on the seminalwork of Karlin and Altschul on the distribution of optimal ungapped local alignmentscores [19]. Both theoretical and empirical studies suggest that the distributions ofoptimal local alignment scores with or without gaps are accurately described by anextreme value distribution.

Assume that we search a query sequence Q against a database. Let lQ be thelength of Q. For each database sequence T , the mean number ET of HSPs withscore s or greater occurring in the comparison of Q and T is

ET = K(lQ − l(s))(lT − l(s))e−λ s, (1)

where K and λ are constants independent of T and l(s) is the length adjustment.K and λ are the two parameters of the extreme value distribution of optimal localalignment scores. Their values are efficiently calculated from the letter compositionof the database sequences and the scoring scheme used for the search. The values ofK and λ are listed in the BLAST search printout.

The length adjustment l(s) is equal to the mean length of HSPs with score sor greater. It is used to eliminate the ‘edge’ effect of the fact that optimal localalignments are unlikely to occur at the end of both query and target sequences. Let Nand M be the numbers of sequences and letters in the database. The current BLASTP(version 2.2.18) calculates the length adjustment l(s) for score s as an integer-valuedapproximation to the unique root of the following functional equation

x = αln(K(lQ − x)(M−Nx))

λ+β . (2)

For ungapped alignment, α = λ/H, and β = 0, where H is the relative entropy ofthe scoring matrix used for the database search. For gapped alignment, the valuesof α and β depend on scoring matrix and affine gap cost. Take BLOSUM62 as anexample. We have that α = 1.90 and β = −29.70 for the affine gap cost in whichthe gap opening and extension costs are 11 and 1 respectively.

We define the effective size of the search space as

eff-searchSP = ∑T∈D

lT −Nl(s). (3)

By the linearity property of means, the expected number of high-scoring alignmentswith score s or greater found in the entire database is

10 Jian Ma and Louxin Zhang

E-value = ∑T∈D

ET = K × (lQ − l(s))× eff-searchSP× e−λ s. (4)

When two sequences are aligned, insertions and deletions can break a long align-ment into several parts. If this is the case, focusing on the single highest-scoringsegment could lose useful information. As an option, one may consider the scoresof the multiple highest-scoring segments.

Assessing multiple highest-scoring segments is more involved than it might firstappear. Suppose, for example, comparison X reports two highest scores 88 and 68,whereas comparison Y reports 79 and 75. One can say that Y is not better than X,because its high score is lower than that of X. But neither is X considered better,because the second high score of X is lower than that of Y. The natural way to rankall the possible results is to consider the sum of the alignment scores of the HSPs assuggested by Karlin and Altschul [20]. This sum is now called the Karlin-Altschulsum statistic.

In earlier versions of BLAST, the Karlin-Altschul sum statistic was only usedfor ungapped alignments as an alternative to performing gapped alignment. Now,it is applied to any HSP. The Karlin-Altschul sum statistics is too involved to bedescribed here.

Finally, we must warn that formulas for P-value and E-value in BLAST are evolv-ing. The above calculations are used in the current version of BLAST (version 2.2).They are different from the calculations used in earlier versions. The length adjust-ment was calculated as the product of λ and the raw score divided by H in earlierversions. Accordingly, they might be modified again in the future.

5 Examples

5.1 A BLASTP Search Example

As an example of using BLASTP, we will consider the capsid protein of the WestNile Virus (WNV)1. This virus mainly infects birds, but occasionally infects humansthrough the bite of an infected mosquito. The WNV is a positive-sense, single strandof RNA, having about 11,000 nucleotides. There are 7 non-structural proteins and 3structural proteins in the RNA. The capsid protein of the WNV has sequence [10]:

MSKKPGGPGK SRAVNMLKRG MPRVLSLIGL KRAMLSLIDG KGPIRFVLAL LAFFRFTAIAPTRAVLDRWR GVNKQTAMKH LLSFKKELGT LTSAINRRSS KQKKR

whose GenBank accession id is YP 001527877. We compare this sequence againstthe non-redundant GenBank database by using BLASTP available at the NCBIserver with default settings. A BLAST printout contains (a) the information on theprogram, (b) a set of local alignments together with the statistical scores, and (c) aset of parameters used for the statistical analysis. A partial printout from our searchfollows:

1 This example first appeared in the article of Casey [11].

Modern BLAST Programs 11

BLASTP 2.2.18+...

Database: All non-redundant GenBank CDStranslations+PDB+SwissProt+PIR+PRF excluding environmental samples from WGS

projects7,036,788 sequences; 2,431,208,758 total letters

...

Query= gi|158516889|ref|YP 001527878.1| capsid protein [West Nile virus]Length=105

...

Alignments

>gb|ABD67759.1| polyprotein precursor [West Nile virus]Length=3433

Score = 203 bits (517), Expect = 3e-51, Method: Composition-based stats.Identities = 100/100 (100%), Positives = 100/100 (100%), Gaps = 0/100 (0%)

Query 1 MSKKPGGPGKSRAVNMLKRGMPRVLSLIGLKRAMLSLIDGKGPIRFVLALLAFFRFTAIA 60MSKKPGGPGKSRAVNMLKRGMPRVLSLIGLKRAMLSLIDGKGPIRFVLALLAFFRFTAIA

Sbjct 1 MSKKPGGPGKSRAVNMLKRGMPRVLSLIGLKRAMLSLIDGKGPIRFVLALLAFFRFTAIA 60

Query 61 PTRAVLDRWRGVNKQTAMKHLLSFKKELGTLTSAINRRSS 100PTRAVLDRWRGVNKQTAMKHLLSFKKELGTLTSAINRRSS

Sbjct 61 PTRAVLDRWRGVNKQTAMKHLLSFKKELGTLTSAINRRSS 100

...

>gb|ACA28703.1| polyprotein [Japanese encephalitis virus]Length=3432

Score = 164 bits (414), Expect = 2e-39, Method: Composition-based stats.Identities = 71/105 (67%), Positives = 90/105 (85%), Gaps = 0/105 (0%)

Query 1 MSKKPGGPGKSRAVNMLKRGMPRVLSLIGLKRAMLSLIDGKGPIRFVLALLAFFRFTAIA 60M+KKPGGPGK+RA+NMLKRG+PRV L+G+KR ++SL+DG+GP+RFVLAL+ FF+FTA+A

Sbjct 1 MTKKPGGPGKNRAINMLKRGLPRVFPLVGVKRVVMSLLDGRGPVRFVLALITFFKFTALA 60

Query 61 PTRAVLDRWRGVNKQTAMKHLLSFKKELGTLTSAINRRSSKQKKR 105PT+A+L RWR V K AMKHL SFK+ELGTL A+N+R KQ KR

Sbjct 61 PTKALLGRWRAVEKSVAMKHLTSFKRELGTLIDAVNKRGKKQNKR 105

...

Database: All non-redundant GenBank CDS translations+PDB+SwissProt+PIR+PRFexcluding environmental samples from WGS projects

Posted date: Sep 9, 2008 5:57 PM

Number of letters in database: -1,863,758,534

Number of sequences in database: 7,036,788

Lambda K H0.324 0.137 0.389

GappedLambda K H

0.267 0.0410 0.140Matrix: BLOSUM62Gap Penalties: Existence: 11, Extension: 1Number of Sequences: 7036788...

12 Jian Ma and Louxin Zhang

Fig. 1 The web page for launching a PSI-BLAST search.

Length of query: 105Length of database: 2431208758Length adjustment: 111

...

The second local alignment in the printout shows that the capsid protein of theWNV has a significant similarity with a domain region of the Japanese encephalitisvirus. Our BLAST search reveals correctly the fact that the Japanese encephalitisvirus and the WNV share similar proteins in their protein coats.

The statistical analysis associated with each alignment in the printout is done asfollows. As shown in the printout, the query sequence has 105 letters; the targetdatabase contains 7,036,788 sequences and 2,431,208,758 letters; and the lengthadjustment displayed in the printout is 73. Since the local alignment involving theJapanese encephalitis virus is gapped, the following values are used in the calcula-tion of the E-value:

λ = 0.267, K = 0.041.

The raw score of the alignment is 414 and hence its bit score is

λ ×Sraw − ln(K)

ln(2)=

0.267×414− ln(0.041)

ln(2)= 164.080851,

which agrees with 164 in the printout [16]. By Equation (4), the E-value is

0.041× (105−73)× (2,431,208,758−7,036,788×73)× e−0.267×414,

which is 2.481035e-39, in agreement with the printout value 2e-39.