Description
Book SynopsisAdvances in sequencing technology have allowed scientists to study the human genome in greater depth and on a larger scale than ever before as many as hundreds of millions of short reads in the course of a few days. But what are the best ways to deal with this flood of data?
Algorithms for Next-Generation Sequencing is an invaluable tool for students and researchers in bioinformatics and computational biology, biologists seeking to process and manage the data generated by next-generation sequencing, and as a textbook or a self-study resource. In addition to offering an in-depth description of the algorithms for processing sequencing data, it also presents useful case studies describing the applications of this technology.
Trade Review"With every advance in sequencing technology, existing string algorithms are yet again at their limits and need to be developed further. As a result, a book like this one is sorely needed. It lays out the concepts and approaches both for practical data analysis and for developing algorithms powerful enough to deal with the deluge of sequence data."
—Martin Vingron, Max Planck Institute for Molecular Genetics
"With every advance in sequencing technology, existing string algorithms are yet again at their limits and need to be developed further. As a result, a book like this one is sorely needed. It lays out the concepts and approaches both for practical data analysis and for developing algorithms powerful enough to deal with the deluge of sequence data."
—Martin Vingron, Max Planck Institute for Molecular Genetics
Table of ContentsIntroduction. Reference Alignment. Genome Assembly. Variation Discovery by Mapping to Reference. RNA-seq. ChIP-seq. Meta-Genomic. Other Technologies.
