Protein Prediction II for Bioinformaticians

Modul IN2230

Dieses Modul wird durch Fakultät für Informatik bereitgestellt.

Diese Modulbeschreibung enthält neben den eigentlichen Beschreibungen der Inhalte, Lernergebnisse, Lehr- und Lernmethoden und Prüfungsformen auch Verweise auf die aktuellen Lehrveranstaltungen und Termine für die Modulprüfung in den jeweiligen Abschnitten.

Modulversion vom WS 2011/2

Von dieser Modulbeschreibung gibt es historische Versionen. Eine Modulbeschreibung ist immer so lange gültig, bis sie von einer neuen abgelöst wird.

verfügbare Modulversionen
SS 2015WS 2011/2

Basisdaten

IN2230 ist ein Semestermodul in Englisch auf Master-Niveau das im Wintersemester angeboten wird.

Das Modul ist Bestandteil der folgenden Kataloge in den Studienangeboten der Physik.

  • Allgemeiner Katalog der nichtphysikalischen Wahlfächer
GesamtaufwandPräsenzveranstaltungenUmfang (ECTS)
240 h 90 h 8 CP

Inhalte, Lernergebnisse und Voraussetzungen

Inhalt

Intro: What is a protein? What is protein function? Overview over prediction of protein function.
Predicting protein function using sequence: sequence alignments, multiple sequence alignments, motifs, domain assignment, annotation transfer by homology, de novo predictions. Predicting protein function using structure: structural alignments, structural motifs, annotation transfer via structure similarity. From structure prediction to function prediction: comparative modeling; prediction of: subcellular localization, protein-protein interactions, protein-DNA and –RNA interactions, protein-substrate interactions, protein networks, GeneOntology (GO), Enzyme Classification, prediction of enzymatic activity, prediction of functional classes (e.g. GO classes).
Prediction of the effect of single point mutations (sequence variants) on protein function and the organism. Prediction of phenotype from genotype.
As for the first part (Protein Prediction I), the lectures include an introduction into machine learning with particular focus on how to avoid over-estimating performance.
The students learn how to write a prediction method starting from data in varying form (depending on the problem at hand). In some cases, they will get the complete input from the tutors, in others, they will have to write database parsers and generate the input/output data they will need during the labwork.
Each team will thoroughly estimate the performance of the tool they created and the team will present their results to their peers and to the tutors.

Lernergebnisse

Students have learned the principle concepts in protein sequence analysis with focus on protein function, in protein function, and protein function prediction. They know the state-of-the-art methods toward these objectives in computational biology and can differentiate between them. As opposed to the first part (Protein Prediction I), protein structure will only play a minor role, it will be introduced where it is helpful to further our understanding of function.
Student also have learned how to develop their own prediction method (in groups guided by tutors by combining existing methods, or algorithms, and/or to develop a new method).
Students have learned especially better how to gauge published methods (as readers of the publication, as peer-reviewers, and as competitors). They are able to develop a ready-to-use tool for experimental and computational biologists.

Voraussetzungen

keine

Lehrveranstaltungen, Lern- und Lehrmethoden und Literaturhinweise

Lehrveranstaltungen und Termine

ArtSWSTitelDozent(en)Termine
VU 6 Protein Prediction II for Bioinformaticians (IN2230) Reeb, J. Rost, B. Di, 12:00–14:00, 5609.01.034
Do, 12:00–14:00, 5609.01.034
Do, 14:00–15:30, 5609.01.034

Lern- und Lehrmethoden

Vorlesung, Übung, Aufgaben zum Selbststudium

Medienformen

Lectures presented in form of interactive seminars using projector and white board; some lectures will be given on the white board, only. All lectures will be video taped and both the slides and the recordings will be made available shortly after the lecture.

Literatur

Wird in der Vorlesung bekanntgegeben

Modulprüfung

Beschreibung der Prüfungs- und Studienleistungen

The exam consists of different elements, graded (Prüfungsleistung) and an ungraded program achievement (Studienleistung).
The graded part is combined of a written exam (90 min) and a scientific report which are weighted in ratio 40:60. The ungraded part consists of mandatory homework and presentations covering milestones for the final report.

In the written exam the students demonstrate to which extend they know the concepts and algorithms of protein structure prediction in condensed form and limited time. In the scientific report they demonstrate that they are able to describe elaborately a problem from the field of protein prediction and their approach to a technical solution. The program achievement ensures that the students get a minimum of practical experience and results in preparation of the final report.

Details are announced at the beginning of the lecture.

Kondensierte Materie

Wenn Atome sich zusammen tun, wird es interessant: Grundlagenforschung an Festkörperelementen, Nanostrukturen und neuen Materialien mit überraschenden Eigenschaften treffen auf innovative Anwendungen.

Kern-, Teilchen-, Astrophysik

Ziel der Forschung ist das Verständnis unserer Welt auf subatomarem Niveau, von den Atomkernen im Zentrum der Atome bis hin zu den elementarsten Bausteinen unserer Welt.

Biophysik

Biologische Systeme, vom Protein bis hin zu lebenden Zellen und deren Verbänden, gehorchen physikalischen Prinzipien. Unser Forschungsbereich Biophysik ist deutschlandweit einer der größten Zusammenschlüsse in diesem Bereich.