Protein Prediction II for Computer Scientists

Introduction: What is a protein? What is protein function? Overview over prediction of protein function.
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 learn the basic principles of protein sequence analysis with focus on protein function and protein function prediction. They will be confronted with the biological and computer science background of the methods toward these objectives in computational biology. Particular focus will be on learning safeguards to correctly estimate performance of machine learning. As opposed to the first part (Protein Prediction I for Computer Scientists), protein structure plays at most a minor role: it will be introduced only if it has been helpful to further our understanding of function.
Students will acquire the theoretical background consisting of the presented knowledge to develop and implement simple independent solutions towards the presented problems.

None required (recommended: IN2322: Protein Prediction I for Computer Scientists)

Lectures, Exercises, Questions & Answers (Q&A) sessions
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.

Lectures presented as interactive seminars using projector and white board; some lectures will be given on the white board, only. If supported: All lectures will be video-taped and both the slides and the recordings will be made available shortly after the lecture.

Will be announced in the lecture. For formal reasons: Anna Tramontano: Introduction to Bioinformatics, or Arthur Lesk: Introduction to Bioinformatics, or Amit Kessel & Nir Ben-Tal: Introduction to Proteins

The module is graded by a written exam at the end of the semester. The exam takes 120 minutes.
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.