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High-throughput sequencing has revolutionised the field of biological sequence analysis. Its application has enabled researchers to address important biological questions, often for the first time. This book provides an integrated presentation of the fundamental algorithms and data structures that power modern sequence analysis workflows. The topics covered range from the foundations of biological sequence analysis (alignments and hidden Markov models), to classical index structures (k-mer indexes, suffix arrays and suffix trees), Burrows–Wheeler indexes, graph algorithms and a number of advanced omics applications. The chapters feature numerous examples, algorithm visualisations, exercises and problems, each chosen to reflect the steps of large-scale sequencing projects, including read alignment, variant calling, haplotyping, fragment assembly, alignment-free genome comparison, transcript prediction and analysis of metagenomic samples. Each biological problem is accompanied by precise formulations, providing graduate students and researchers in bioinformatics and computer science with a powerful toolkit for the emerging applications of high-throughput sequencing.

 

Provides an integrated picture of the fundamental algorithms and data structures that power modern sequence analysis, covering a range of topics including foundations, classical index structures and Burrows–Wheeler indexes

Chapters feature numerous examples, algorithm visualisations, problems and end-of-chapter exercises, providing students with a powerful toolkit for the emerging applications of high-throughput sequencing

Presents only the minimum data structures necessary so that students are not burdened with technical results and can also focus on more conceptual algorithm design questions

Table of Contents

 

Notation

Preface

Part I. Preliminaries:

1. Molecular biology and high-throughput sequencing

2. Algorithm design

3. Data structures

4. Graphs

5. Network flows

Part II. Fundamentals of Biological Sequence Analysis:

6. Alignments

7. Hidden Markov models (HMMs)

Part III. Genome-Scale Index Structures:

8. Classical indexes

9. Burrows–Wheeler indexes

Part IV. Genome-Scale Algorithms:

10. Read alignment

11. Genome analysis and comparison

12. Genome compression

13. Fragment assembly

Part V. Applications:

14. Genomics

15. Transcriptomics

16. Metagenomics

References

Index.