Elloumi M. (Ed.) Algorithms for Next-Generation Sequencing Data: Techniques, Approaches, and Applications

Springer International Publishing AG, 2017. — 356 p. — ISBN: 978-3-319-59824-6.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.Indexing, Compression, and Storage of NGS Data
Algorithms for Indexing Highly Similar DNA Sequences.
Full-Text Indexes for High-Throughput Sequencing
Searching and Indexing Circular Patterns
De Novo NGS Data Compression
Cloud Storage-Management Techniques for NGS Data
Error Correction in NGS Data
Probabilistic Models for Error Correction of Nonuniform Sequencing Data
DNA-Seq Error Correction Based on Substring Indices
Error Correction in Methylation Profiling From NGS Bisulfite Protocols
Alignment of NGS Data
Comparative Assessment of Alignment Algorithms for NGS Data: Features, Considerations, Implementations, and Future
CUSHAW Suite: Parallel and Efficient Algorithms for NGS Read Alignment
String-Matching and Alignment Algorithms for Finding Motifs in NGS Data
Assembly of NGS Data
The Contig Assembly Problem and Its Algorithmic Solutions
An Efficient Approach to Merging Paired-End Reads and Incorporation of Uncertainties
Assembly-Free Techniques for NGS Data