261-5100-00L Computational Biomedicine (Autumn 2020)

Semester Autumn Semester 2020
Lecturers Gunnar Rätsch; Valentina Boeva; Natalie Davidson
Periodicity yearly course
Language of instruction English

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

Objective
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.

Content
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.

Course Overview

Date Topic Course Material
15.09.2020 Lecture: Introduction to the topic and patient genomics Lecture Slides 01 Lecture Video 01
Exercise: Organization and presentation of projects Tutorial Slides 01
22.09.2020 Lecture: String algorithms, indexing and search Lecture Slides 02 Lecture Video 02
Exercise: Hand out of project 1, Tutorial Tutorial Slides 02 Tutorial Video 02
Project 1 Description
29.09.2020 Lecture: Indexes of linear sequences and alignment Lecture Slides 03 Lecture Video 03
Exercise: Tutorial / Project Q & A Tutorial Slides 03
06.10.2020 Lecture: Variation-aware alignment, Indexes on graphs, succinct data structures
Exercise: Tutorial / Project Q & A
13.10.2020 Lecture: Transcript identification and quantification
Exercise: Tutorial / Project work
20.10.2020 Lecture: Differential Gene expression
Exercise: Tutorial / Project work
27.10.2020 Lecture: Single Cell expression data
Exercise: Tutorial / Project work
03.11.2020 Lecture: Variant calling (germline)
Exercise: Hand in project 1 (due 11:59pm) / Project 1 presentations
10.11.2020 Lecture: Linking genotypic information to clinical phenotypes
Exercise: Project 1 presentations
17.11.2020 Lecture: Variant interpretation and effect prediction
24.11.2020 Lecture: Ontologies and Variant Interpretation
Exercise: Tutorial / Project work
01.12.2020 Lecture: Somatic Variant calling and Tumor Heterogeneity
Exercise: Tutorial / Project work
08.12.2020 No Lecture
Exercise: Hand in project 2 / Project 2 presentations
15.12.2020 Lecture: Research talk, Summary of course, Q & A for exam
Exercise: Project 2 presentations