261-5100-00L Computational Biomedicine (Autumn 2017)

Semester Autumn Semester 2017
Lecturers G. Rätsch
Periodicity yearly course
Language of instruction English

The course critically reviews central problems in Biomedicine and discusses the technical foundations and solutions for these problems.

Over the past years, rapid technological advancements have transformed classical disciplines such as biology and medicine into fields of apllied data science. While the sheer amount of the collected data often makes computational approaches inevitable for analysis, it is the domain specific structure and close relation to research and clinic, that call for accurate, robust and efficient algorithms. In this course we will critically review central problems in Biomedicine and will discuss the technical foundations and solutions for these problems.

The course will consist of three topic clusters that will cover different aspects of data science problems in Biomedicine:

1) String algorithms for the efficient representation, search, comparison, composition and compression of large sets of strings, mostly originating from DNA or RNA Sequencing. This includes genome assembly, efficient index data structures for strings and graphs, alignment techniques as well as quantitative approaches.
2) Statistical models and algorithms for the assessment and functional analysis of individual genomic variations. this includes the identification of variants, prediction of functional effects, imputation and integration problems as well as the association with clinical phenotypes.
3) Models for organization and representation of large scale biomedical data. This includes ontolgy concepts, biomedical databases, sequence annotation and data compression.

Prerequisites / Notice
Data Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line.


The lecture will be held at ETH in CAB G 56 (link to location).

Course Overview

Date Topic Course Material
26.09.2017 Lecture: Introduction to the topic and patient genomics Lecture Slides 01 (with annotations)
Exercise: Organization and presentation of projects
03.10.2017 Lecture: String algorithms, indexing and search Lecture Slides 02 (with annotations)
Exercise: Hand out of project 1, Tutorial Exercise Sheet 1
Exercise Slides
10.10.2017 Lecture: Indexes of linear sequences and alignment Lecture Slides 03 (with annotations)
Exercise: Tutorial / Project work
17.10.2017 Lecture: Variation-aware alignment, Indexes on graphs, succinct data structures Lecture Slides 04
Exercise: Tutorial / Project work
24.10.2017 Lecture: Transcript identification and quantification Lecture Slides 05 (with annotations)
Exercise: Tutorial / Project work Exercise Slides
31.10.2017 Lecture: Variant identification and variant imputation Lecture Slides 06 (with annotations)
Exercise: Tutorial / Project work
07.11.2017 Lecture: Linking genotypic information to clinical phenotypes Lecture Slides 07 (with annotations)
Exercise: Hand in project 1 / Hand out project 2 / Project 1 presentations Exercise Sheet 2
14.11.2017 Lecture: Variant effects and genetic drivers Lecture Slides 08 (with annotations)
Exercise: Tutorial / Project work
21.11.2017 Lecture: Variant calling Lecture Slides 09
Exercise: Evaluation / Leader board project 1 Exercise Slides
28.11.2017 No Lecture
Exercise: Tutorial / Project work
05.12.2017 Lecture: Variant effect prediction and tumor heterogeneity Lecture Slides 10
Exercise: Tutorial / Project work
12.12.2017 Lecture: Data representation and Ontologies Lecture Slides 11 (with annotations)
Exercise: Hand in project 2 / Project 2 presentations
19.12.2017 Lecture: Ontologies in a human health setting, summary and outlook Lecture Slides 12 (with annotations)
Exercise: Evaluation / Leader board project 2