Bookstore

This innovative book provides a completely fresh exploration of bioinformatics, investigating its complex interrelationship with biology and computer science. It approaches bioinformatics from a unique perspective, highlighting interdisciplinary gaps that often trap the unwary.The book considers how the need for biological databases drove the evolution of bioinformatics; it reviews bioinformatics basics (including database formats, data-types and current analysis methods), and examines key topics in computer science (including data-structures, identifiers and algorithms), reflecting on their use and abuse in bioinformatics.Bringing these disciplines together, this book is an essential read for those who wish to better understand the challenges for bioinformatics at the interface of biology and computer science, and how to bridge the gaps. It will be an invaluable resource for advanced undergraduate and postgraduate students, and for lecturers, researchers and professionals with an interest in this fascinating, fast-moving discipline and the knotty problems that surround it.

Bioinformatics Challenges at the Interface of Biology and Computer Science: Mind the Gap

Preface xAcknowledgements xviiAbout the companion website xviiiPART 11 Introduction 31.1 Overview 31.2 Bioinformatics 31.2.1 What is bioinformatics? 31.2.2 The provenance of bioinformatics 41.2.3 The seeds of bioinformatics 51.3 Computer Science 71.3.1 Origins of computer science 71.3.2 Computer science meets bioinformatics 91.4 What did we want to do with bioinformatics? 101.5 Summary 121.6 References 131.7 Quiz 141.8 Problems 162 The biological context 172.1 Overview 172.2 Biological data?]types and concepts 172.2.1 Diversity of biological data?]types 172.2.2 The central dogma 182.2.3 Fundamental building?]blocks and alphabets 192.2.4 The protein structure hierarchy 292.2.5 RNA processing in prokaryotes and eukaryotes 302.2.6 The genetic code 332.2.7 Conceptual translation and gene finding 352.3 Access to whole genomes 422.4 Summary 432.5 References 432.6 Quiz 462.7 Problems 473 Biological databases 493.1 Overview 493.2 What kinds of database are there? 493.3 The Protein Data Bank (PDB) 503.4 The EMBL nucleotide sequence data library 563.5 GenBank 583.6 The PIR?]PSD 613.7 Swiss?]Prot 623.8 PROSITE 643.9 TrEMBL 693.10 InterPro 713.11 UniProt 733.12 The European Nucleotide Archive (ENA) 773.13 Summary 813.14 References 823.15 Quiz 853.16 Problems 874 Biological sequence analysis 894.1 Overview 894.2 Adding meaning to raw sequence data 894.2.1 Annotating raw sequence data 944.2.2 Database and sequence formats 964.2.3 Making tools and databases interoperate 1014.3 Tools for deriving sequence annotations 1034.3.1 Methods for comparing two sequences 1034.3.2 The PAM and BLOSUM matrices 1044.3.3 Tools for global and local alignment 1104.3.4 Tools for comparing multiple sequences 1144.3.5 Alignment?]based analysis methods 1154.4 Summary 1314.5 References 1324.6 Quiz 1344.7 Problems 1365 The gap 1385.1 Overview 1385.2 Bioinformatics in the 21st century 1385.3 Problems with genes 1395.4 Problems with names 1425.5 Problems with sequences 1435.6 Problems with database entries 1465.6.1 Problems with database entry formats 1475.7 Problems with structures 1485.8 Problems with alignments 1505.8.1 Different methods, different results 1505.8.2 What properties do my sequences share? 1545.8.3 How similar are my sequences? 1575.8.4 How good is my alignment? 1605.9 Problems with families 1635.10 Problems with functions 1685.11 Functions of domains, modules and their parent proteins 1735.12 Defining and describing functions 1765.13 Summary 1795.14 References 1805.15 Quiz 1825.16 Problems 183PART 26 Algorithms and complexity 1876.1 Overview 1876.2 Introduction to algorithms 1876.2.1 Mathematical computability 1896.3 Working with computers 1916.3.1 Discretisation of solutions 1916.3.2 When computers go bad 1936.4 Evaluating algorithms 1976.4.1 An example: a sorting algorithm 1976.4.2 Resource scarcity: complexity of algorithms 1996.4.3 Choices, choices 2006.5 Data structures 2016.5.1 Structural consequences 2026.5.2 Marrying form and function 2106.6 Implementing algorithms 2116.6.1 Programming paradigm 2126.6.2 Choice of language 2146.6.3 Mechanical optimisation 2166.6.4 Parallelisation 2246.7 Summary 2276.8 References 2276.9 Quiz 2276.10 Problems 2297 Representation and meaning 2307.1 Overview 2307.2 Introduction 2307.3 Identification 2337.3.1 Namespaces 2337.3.2 Meaningless identifiers are a good thing 2337.3.3 Identifying things on the Web 2367.3.4 Cool URIs don't change 2387.3.5 Versioning and provenance 2387.3.6 Case studies 2397.4 Representing data 2437.4.1 Design for change 2457.4.2 Contemporary data?]representation paradigms 2477.5 Giving meaning to data 2557.5.1 Bio ontologies in practice 2607.5.2 First invent the universe 2637.6 Web services 2647.6.1 The architecture of the Web 2667.6.2 Statelessness 2677.7 Action at a distance 2687.7.1 SOAP and WSDL 2707.7.2 HTTP as an API 2707.7.3 Linked Data 2727.8 Summary 2757.9 References 2757.10 Quiz 2767.11 Problems 2778 Linking data and scientific literature 2798.1 Overview 2798.2 Introduction 2798.3 The lost steps of curators 2818.4 A historical perspective on scientific literature 2868.5 The gulf between human and machine comprehension 2888.6 Research objects 2958.7 Data publishing 2978.8 Separating scientific wheat from chaff - towards semantic searches 2988.9 Semantic publication 3008.9.1 Making articles 'semantic' 3018.10 Linking articles with their cognate data 3058.10.1 What Utopia Documents does 3058.10.2 A case study 3068.11 Summary 3148.12 References 3158.13 Quiz 3188.14 Problems 319Afterword 321Glossary 327Quiz Answers 371Problem Answers 378Index 394