261-5100-00L Computational Biomedicine (Autumn 2019)
|Semester||Autumn Semester 2019|
|Lecturers||Gunnar Rätsch; Valentina Boeva; Natalie Davidson|
|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.
|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||Lecture Slides 02|
|Exercise: Hand out of project 1, Tutorial||Project 1 Description|
|01.10.2019||Lecture: Indexes of linear sequences and alignment||Lecture Slides 03|
|Exercise: Tutorial / Project work||Exercise Slides 03|
|08.10.2019||Lecture: Variation-aware alignment, Indexes on graphs, succinct data structures||Lecture Slides 04|
|Exercise: Tutorial / Project work||Exercise Slides 04|
|15.10.2019||Lecture: Transcript identification and quantification||Lecture Slides 05|
|Exercise: Tutorial / Project work||Exercise Slides 05|
|22.10.2019||Lecture: Differential Gene expression||Lecture Slides 06|
|Exercise: Tutorial / Project work||Exercise Slides 06|
|29.10.2019||Lecture: Single Cell expression data||Lecture Slides 07|
|Exercise: Tutorial / Project work||Exercise Slides 07|
|05.11.2019||Lecture: Variant calling (germline)||Lecture Slides 08 (with annotations)|
|Exercise: Hand in project 1 (due 11:59pm) / Project 1 presentations|
|12.11.2019||Lecture: Linking genotypic information to clinical phenotypes||Lecture Slides 09|
|Exercise: Project 1 presentations|
|19.11.2019||Lecture: Variant interpretation and effect prediction||Lecture Slides 10|
|Exercise: Hand out of project 2, Tutorial||Project 2 Description|
|26.11.2019||Lecture: Ontologies and Variant Interpretation|
|Exercise: Tutorial / Project work|
|03.12.2019||Lecture: Somatic Variant calling and Tumor Heterogeneity|
|Exercise: Tutorial / Project work|
|Exercise: Hand in project 2 / Project 2 presentations|
|17.12.2019||Lecture: Repetition and Outlook|
|Exercise: Project 2 presentations|