Algorithms for Next-Generation Sequencing Data: Techniques, Approaches, and Applications

The 14 contributed chapters in this book survey the most recent developments in high-performance algorithms for NGS data, offering fundamental insights and technical information specifically on indexing, compression and storage; error correction; alignment; and assembly.

The book will be of value to researchers, practitioners and students engaged with bioinformatics, computer science, mathematics, statistics and life sciences.

Table of Contents

Part I Indexing, Compression, and Storage of NGS Data
Chapter 1 Algorithms For Indexing Highly Similar Dna Sequences
Chapter 2 Full-Text Indexes For High-Throughput Sequencing
Chapter 3 Searching And Indexing Circular Patterns
Chapter 4 De Novo Ngs Data Compression
Chapter 5 Cloud Storage-Management Techniques For Ngs Data

Part II Error Correction in NGS Data
Chapter 6 Probabilistic Models For Error Correction Of Nonuniform Sequencing Data
Chapter 7 Dna-Seq Error Correction Based On Substring Indices
Chapter 8 Error Correction In Methylation Profiling From Ngs Bisulfite Protocols

Part III Alignment of NGS Data
Chapter 9 Comparative Assessment Of Alignment Algorithms For Ngs Data: Features, Considerations, Implementations, And Future
Chapter 10 Cushaw Suite: Parallel And Efficient Algorithms For Ngs Read Alignment
Chapter 11 String-Matching And Alignment Algorithms For Finding Motifs In Ngs Data

Part IV Assembly of NGS Data
Chapter 12 The Contig Assembly Problem And Its Algorithmic Solutions
Chapter 13 An Efficient Approach To Merging Paired-End Readsand Incorporation Of Uncertainties
Chapter 14 Assembly-Free Techniques For Ngs Data