Fundamental Algorithms in Deep Sequencing

Lecture 1: Fundamental Algorithms in Deep Sequencing I: Assembly
Lecture 2: Fundamental Algorithms in Deep Sequencing II: Mapping/Alignment
Lecture 3: Fundamental Algorithms in Deep Sequencing III: Genomic Compression: Storage, Transmission, and Analytics

This series of talks is part of the Algorithmic Challenges in Genomics Boot Camp.

Speakers: Noah M. Daniels, (MIT), Lior Pachter (UC Berkeley), and Pavel Pevzner (UC San Diego)

Deep sequencing has become ubiquitous in genomics research due to plummeting costs and massive data volumes. However, it raises formidable algorithmic challenges.

In the first mini course, a flipped class, we will learn how graph theory can be used to assemble genomes and will review the recent advances in DNA sequencing. To prepare for the class, students will be provided with an opportunity to enroll in the short Genome Sequencing MOOC on Coursera before the class starts. Instead of lecturing in the class, the instructor will interact with students to answer their questions about the material and the recent trends in genome assembly.

In the second mini course we will describe algorithms for mapping and alignment of deep sequencing data.

The third mini course will describe methods for genomic compression: how to store, transmit, and analyze efficiently huge datasets that accumulate in the era of deep sequencing.