Advances in sequencing technology have allowed scientists to study the human genome in greater depth and on a larger scale than ever before. 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, who seek 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.
slides/Chapter | Topic | slides |
1 | Introduction | |
2 | NGS file formats | |
3 | Related algorithms and data structures | |
4 | NGS read mapping | |
5 | Genome assembly | |
6 | Single nucleotide variation (SNV) calling | |
7 | Structural variation calling | |
8 | RNA-seq | |
9 | Peak calling methods | |
10 | Data compression techniques used in NGS files |
If you have any suggestions for improvement or if you identify any errors in the book, please send an email to me at ksung@comp.nus.edu.sg. I thanks in advance for your help to improve the book.
A total of 2073 different hosts have accessed this document in the last 2470 days; your host, ec2-44-220-182-198.compute-1.amazonaws.com, has accessed it 1 times.
If you're interested, complete statistics for this document are also available, including breakdowns by top-level domain, host name, and date.