261-5100-00L Computational Biomedicine (Autumn 2019)

Semester Autumn Semester 2019
Lecturers Gunnar Rätsch; Valentina Boeva; Natalie Davidson
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 LEE E 101 (link to location) and CAB G 56 (link to location). Tuesdays 10-12 (LEE E 101) and 13-14 (CAB G 56).

Course Overview

Date Topic Course Material
17.09.2019 Lecture: Introduction to the topic and patient genomics Lecture Slides 01
Exercise: Organization and presentation of projects Exercise Slides 01
24.09.2019 Lecture: String algorithms, indexing and search
Exercise: Hand out of project 1, Tutorial
01.10.2019 Lecture: Indexes of linear sequences and alignment
Exercise: Tutorial / Project work
08.10.2019 Lecture: Variation-aware alignment, Indexes on graphs, succinct data structures
Exercise: Tutorial / Project work
15.10.2019 Lecture: Transcript identification and quantification
Exercise: Tutorial / Project work
22.10.2019 Lecture: Differential Gene expression
Exercise: Tutorial / Project work
29.10.2019 Lecture: Single Cell expression data
Exercise: Tutorial / Project work
05.11.2019 Lecture: Variant calling (germline)
Exercise: Tutorial / Leader board project 1
12.11.2019 Lecture: Linking genotypic information to clinical phenotypes
Exercise: Tutorial / Project work
19.11.2019 Lecture: Variant interpretation and effect prediction
Exercise: Tutorial / Project work
26.11.2019 Lecture: Ontologies and Variant Interpretation
Exercise: Tutorial / Project work
03.12.2019 Lecture: Somatic Variant calling and Tumor Heterogeneity
Leaderboard Project 2
10.12.2019 No Lecture
Exercise: Hand in project 2 / Project 2 presentations
17.12.2019 Lecture: Repetition and Outlook