PhD Reading Course: Genome-Scale Algorithm Design
Content Providers
Learning Outcomes
- Learn a set of advanced algorithms and data structures (e.g., dynamic range minimum queries, rank-select operations, Wavelet trees, MSA, HMMs, BWT variations), and be able to reason about their correctness and time and space complexity.
- Learn the core set of problems in bioinformatic sequence analysis and some of their solutions, focusing on scalability to large genomic data sets.
- Present and summarize in a clear and structured way the acquired knowledge by delivering lecture-style presentations of the reading material.
Prerequisites & Technical Requirements
Prerequisites
To qualify for the course, you should have a basic knowledge in discrete mathematics and probability theory, as well as
intermediate knowledge in Computer Science with a focus on algorithms, data structures, and complexity analysis.
This means knowledge roughly equivalent to the following courses at SU: Mathematics I, 30 ECTS (MM2001), Programming
techniques, 7.5 ECTS (DA2005), Probability theory 1, 7.5 ECTS (MT3001), Computer Science for Mathematicians, 7.5
ECTS (DA3018), Algorithms and Complexity, 7.5 ECTS (DA4005), Data structures and algorithms, 7.5 ECTS (DA4006).
Affiliations & Networks
Associated nodes
SciLifeLab
Target audience
Computer ScientistsBioinformaticiansEngineers
Activity log
