261-5100-00L Computational Biomedicine (Autumn 2018)

Semester Autumn Semester 2018
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). Tuesdays 10-12 and 13-14.

Course Overview

Date Topic Course Material
18.09.2018 Lecture: Introduction to the topic and patient genomics Lecture Slides 01 (with annotations)
Exercise: Organization and presentation of projects Exercise Slides 01
25.09.2018 Lecture: String algorithms, indexing and search Lecture Slides 02 (with annotations)
Exercise: Hand out of project 1, Tutorial Project 1 Description
02.10.2018 No Lecture
Exercise: Tutorial / Project work
09.10.2018 Lecture: Indexes of linear sequences and alignment Lecture Slides 03 (with annotations)
Exercise: Tutorial / Project work
16.10.2018 Lecture: Variation-aware alignment, Indexes on graphs, succinct data structures Lecture Slides 04
Exercise: Tutorial / Project work
23.10.2018 Lecture: Transcript identification and quantification Lecture Slides 05 (with annotations)
Exercise: Tutorial / Project work Exercise Slides 05
30.10.2018 Lecture: Variant identification and variant imputation Lecture Slides 06 (with annotations)
Exercise: Hand in project 1 / Hand out project 2 Project 1 presentations Project 2 Description
06.11.2018 Lecture: Linking genotypic information to clinical phenotypes Lecture Slides 07
Exercise: Tutorial / Project work
13.11.2018 Lecture: Variant calling (germline) Lecture Slides 08 (with annotations)
Exercise: Tutorial / Leader board project 1
20.11.2018 Lecture: Variant interpretation and effect prediction Lecture Slides 09 (with annotations)
Exercise: Tutorial / Project work Exercise Slides 09 (Eval) Exercise Slides 09 (Content)
27.11.2018 Lecture: Ontologies and Variant Interpretation Lecture Slides 10 (with annotations)
Exercise: Tutorial / Project work
04.12.2018 No Lecture
Exercise: Hand in project 2 / Project 2 presentations
11.12.2018 Lecture: Somatic Variant calling and Tumor Heterogeneity Lecture Slides 11 (with annotations)
Leaderboard Project 2
18.12.2018 Lecture: Repetition and Outlook TBA