Bioinformatics Algorithms

Bioinformatics programs processing large volume of sequence data implement a number of elegant algorithms. They are covered in the following tutorials. Additionally, we posted video tutorials from two Coursera courses on bioinformatics algorithms.

1. Burrows Wheeler Transform

This tutorial describes Burrows Wheeler transform (BWT), a reversible transform used for data compression (bzip2) and indexing genomic libraries.

2. Bloom Filters

This tutorial dpresents Bloom filters, memory-efficient data structures often used in bioinformatics. In contrast to deterministic data structures like hash tables, Bloom filters are 'probabilistic'.

3. Kmer Magic

This tutorial covers kmer spectrum and its uses. It is a powerful tool to learn about genomes, transcriptomes and high-throughput sequencing libraries.

4. All Algorithms

These days, the biologist and bioinformaticians come across many unfamiliar terms such as de Bruijn graphs, Bloom filters, suffix arrays, Burrows Wheeler transform, Hidden Markov model and so on. They represent various computational concepts helpful in speeding up the analysis of large volumes of genomic data. To help biologists feel comfortable about reading the relevant algorithmic papers and make intelligent decisions, we provide simple and easy introduction to those concepts in this tutorial.

5. Langmead Course

This tutorial covers Ben Langmead's excellent bioinformatics course at Coursera.

6. Pevzner Course

This tutorialcovers Pavel Pevzner's excellent bioinformatics course at Coursera.