Algorithms, Data Structures and Inference for High-Throughput Genomics / Fall 2019
- New Lecture is up: Analysis of transcriptomes with RNA-seq (II) [slides]
The list of potential projects has been posted on ELMS if you have not yet seen it. You may select a project from the list, or propose your own. However, please identify your team mates and select your project by the end of the week.
- New Lecture is up: Analysis of transcriptomes with RNA-seq [slides]
- New Assignment released: [Homework 2]
- New Lecture is up: Efficient indexing of the (compacted) colored de Bruijn graph [slides]
- New Lecture is up: Efficient compacted colored de Bruijn graph construction [slides]
- New Lecture is up: Efficient colored de Bruijn graph representation [slides]
This course will focus on recent algorithmic, data structural, and statistical advances in methods for analyzing high-throughput sequencing data. Topics will include, but are not limited to : data structures for indexing genomes, collections of genomes, and collections of experimental data, succinct data structures, kmerology, algorithms for short and long read DNA-seq and RNA-seq alignment, probabilistic modeling of sequencing experiments and related inference techniques.
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