Protein Prediction II for Computer Scientists
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
IN2291 is a semester module in English language at Master’s level which is offered in winter semester.
This Module is included in the following catalogues within the study programs in physics.
- Catalogue of non-physics elective courses
|Total workload||Contact hours||Credits (ECTS)|
|240 h||90 h||8 CP|
Content, Learning Outcome and Preconditions
Predicting protein function using sequence: motifs, annotation transfer by homology (homology-based inference), de novo predictions. Predicting protein function using structure: structural motifs, annotation transfer via structure similarity. 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 (focus on single amino acid variants). Prediction of phenotype from genotype.
As opposed to the first part (Protein Prediction I), protein structure plays a minor role confined to what is helpful to further our understanding of protein function. Another major difference is that alignment methods will not be discussed although their results (evolutionary information) will be central to almost all prediction methods.
Students will acquire the theoretical background consisting of the presented knowledge to develop and implement simple independent solutions towards the presented problems.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VI||6||Protein Prediction II for Computer Scientists (IN2291)||Rost, B.||
Thu, 10:00–12:00, LMU-HS
Tue, 10:00–12:00, LMU-HS
Thu, 12:00–14:00, virtuell
Learning and Teaching Methods
Lectures (include Q&A): Theoretical background for all topics will be presented in traditional lecture style with slides, as well as, interactively through white board presentations and Q&A sessions.
Exercises (include Q&A): Programming of a particular novel prediction method; this will deepen and apply the material presented in the lectures; occasionally, presentation of additional material needed for better understanding; exercises also include interactive Q&A sessions, and presentations from the students.
Description of exams and course work
Weekly programming exercises and questions are not graded; they may bring a bonus of +0.3 in the final grade.
In the exam, the participants demonstrate their ability to devise and discuss an appropriate computational approach solving a biological problem in the area of protein function prediction. For instance, they choose the appropriate methods depending on the type of data they have (e.g. sequence or annotation data) along with the appropriate data abstraction level (e.g. GO level, EC classes) depending on the particular biological question.
Students demonstrate their understanding of the concepts in the choice and/or design of appropriate solutions and they can evaluate pros and cons of given their answers and of alternative approaches. They can demonstrate their ability to create a usable tool implementing a solution approach down to the level of pseudo-code.
More details will be announced early on in the lecture.
The exam may be repeated at the end of the semester.