Lecture: Thu 08:30-10:00 in OH14/104
Practicals: Thu 12:00-14:00 in OH14/202 [?] or individually
Exams (Module INF-MSc-514 in Computer Science or Applied CS):
Lectures and material can be found in the gitlab repository (request access from the instructor):
| Date | Class | Topics |
|---|---|---|
| 11.10. | Lecture | Introduction, DNA structure, number of DNA k-mers, DNA sequencing technologies, file formats (FASTA, FASTQ), Phred quality values, Lander-Waterman formula, Poisson approximation. |
| Practical | Introduction to conda and Python; Installation of Jupyter Notebooks. | |
| Tasks | Install conda and get the notebook of lecture 1 running. Watch and understand some of the sequencing technology videos: Illumina, Pacific Biosciences, Oxford Nanopore. Play with Python, e.g., write or inspect a tools that counts nucleotides in FASTA files. |
|
| 18.10. | Lecture | The DNA sequencing error correction problem, k-mer statistics, Bloom filters |
| Practical | The tool "Lighter" for error correction | |
| Tasks | (1) Summarize the Lighter article; (2) solve the problems given in the linked repository. |
More will be added soon.
The following links can be useful in addition to the lectures.
For read/fork access, please send an email with your gitlab username to the lecturer.
Link: https://gitlab.com/rahmannlab/comics-2018/
Computational Omics is a course about algorithms for computational and statistical analysis of "big data" in the life sciences. Its contents are a mixture of algorithms, statistics, data science and molecular biology. It cosists of lectures (2 hours per week) and a practical session (2 hours per week), where participants implement some of the methods discussed in class and apply them to real datasets. The course is not recommended for the casual student!
Some of the topics discussed in this course are
Die Vorlesung gibt eine Übersicht über die aktuellen informatischen Methoden zur Datenanalyse in den in den “Omiken” der Lebenswissenschaften (Genomik, Transkriptomik, Epigenomik, Proteomik, Metabolomik, Interaktomik, ...). Sie besteht aus mehreren Einheiten, die sich jeweils einem dieser Themenbereiche und zugehörigen Technologien widmen. Die diskutierten Technologien und die daraus entstehenden Daten können sich von Jahr zu Jahr ändern, da die technologische Entwicklung auf diesem Gebiet schnell voranschreitet. Wichtig ist, dass in der Vorlesung theoretische Grundlagen und Prinzipien vermittelt werden. Diese werden beim Studium aktueller Arbeiten zu Technologien und Algorithmen vertieft und von den Studierenden ausgearbeitet. Im zugehörigen Praktikum werden eigene Miniprojekte bearbeitet. Beispiele für Themen sind:
In den Einheiten wird jeweils eine Einführung in die zugrundeliegenden Technologien gegeben; dabei wird der Art und Erzeugung der Daten besondere Aufmerksamkeit gewidmet. Es schließen sich typische Fragestellungen an, die anhand der gewonnenen Daten gestellt und beantwortet werden können. Dazu werden jeweils die wichtigsten Datenanalysemethoden besprochen. Diese unterteilen sich häufig in sogenannte low-level-Verfahren zur Vorverarbeitung, die sich vor allem nach der Art der Daten richten und high-level-Verfahren aus dem Bereich des maschinellen Lernens, die die gewünschten Informationen aus den Daten extrahieren. Aufgrund des hierbei auftretenden Datenvolumens stehen dabei besonders ressourceneffiziente Algorithmen im Vordergrund. Aus statistischer Sicht geht es zusätzlich darum, sinnvoll mit dem Problem hochdimensionaler Daten bei kleiner Stichprobengröße umzugehen ($n \ll p$ - Problematik).
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This website is informational and for educational purposes. It contains lecture material for a course offered by the following responsible person, to whom comments and questions about this site should be sent:
Prof. Dr. Sven Rahmann
Institut für Humangenetik / Genominformatik
Medizinische Fakultät der Universität Duisburg-Essen
Universitätsklinikum Essen
Hufelandstr. 55
45147 Essen
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