Proseminar 'Proteins and Disease' SS 2014

Application / Registration

Application is organised centrally for all bioinformatics seminars. After you have been assigned to our seminar, we will distribute the topics.

Content

Topics related to the research interests of the group: protein sequence analysis, sequence based predictions, 
protein structure prediction and analysis; interaction networks. The list below shows topics from the last seminar WS12/13. The topics for the new seminar will be updated, but you can expect similar talks.

Pre-meeting

tba in Room MI 01.09.034

The rules and hints for preparation of the seminar discussed  in the pre-meeting are also summarised in our Checklist and on these slides.

Slides from the pre-meeting. Topics and schedule have been moved to the table below. Timetable last updated at Feb. 24th 

Sequence alignment and searches: heuristic methods

Dr. Lothar Richter

This talk shall explain the heuristic approximations made to speed up sequence alignment and sequence searches (BLAST, FASTA).

Literature:

Korf, Ian et al. BLAST. O'Reilly Media, Inc. 2003

Potassium channels: structure and function

Dr. Edda Kloppmann

Potassium channels comprise a large and important group of integral membrane receptors and are the most widely distributed class of ion channels. The 2003 Nobel Prize for Chemistry was awarded jointly to Roderick MacKinnon and Peter Agre "for discoveries concerning channels in cell membranes". Rod MacKinnon in particular worked on potassium channels. This talk shall introduce the structure, function and molecular mechanism of potassium channels.

Literature:

• Alberts
• ...

PiNat: Assessment of protein networks

Tatyana Goldberg

A platform for data integration shall be presented in this talk. The platform generates networks on the macro system-level, analyzes the molecular characteristics of each protein on the micro level, and then combines the two levels by using the molecular characteristics to assess networks. It also annotates the function and subcellular localization of each protein and displays the process on an image of a cell, rendering each protein in its respective cellular compartment.

Literature:

• Ofran Y, Rost B, et al. (2006). Create and assess protein networks through molecular characteristics of individual proteins. Bioinformatics; 22(14):e402-7. http://www.ncbi.nlm.nih.gov/pubmed/16873500

• Barabási AL & Oltvai ZN (2004). Network biology: understanding the cell's functional organization. Nature Reviews Genetics 5, 101-113. http://www.ncbi.nlm.nih.gov/pubmed/14735121

Predicting subcellular localization using functional hierarchies

Tatyana Goldberg

Identification of a protein’s subcellular localization is an important step towards elucidating its function. In this seminar, a machine-learning-based method for predicting localization in prokaryotes and eukaryotes shall be presented. The method is original in incorporating a hierarchical ontology of subcellular localization classes. Furthermore, it uses predicted features like the secondary structure of a protein and evolutionary information in form of sequence profiles to improve prediction accuracy considerably.

Literature:

• Alberts B, Bray D, Lewis J, Raff M, Roberts K, Watson JD. Molecular Biology of the Cell. New York: Garland Science, 2002

• Nair R, Rost B (2005). Mimicking cellular sorting improves prediction of subcellular localization. J Mol Biol. Apr 22;348(1):85-100. www.ncbi.nlm.nih.gov/pubmed/15808855

Conditional Random Fields for Named-Entity Recognition

Juan Miguel Cejuela

Conditional random fields (CRF) are popular methods in named-entity recognition (NER) and generally in sequential labeling tasks. This talk shall present the CRF models and their advantages in comparison to other popular models like hidden markov models (HMMs). An example case will focus on the recognition of protein names.

• Andrew McCallum Charles Sutton. An Introduction to Conditional Random Fields for Relational Learning. In Lise Getoor and Ben Taskar, editors, Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning), chapter 4
• John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In Proceedings of the Eighteenth International Conference on Machine Learning, ICML ’01,

Biological Databases

Juan Miguel Cejuela

Huge volumes of primary data are archived in numerous open-access databases, and with new generation technologies becoming more common in laboratories. This seminar shall give an overview of different Databases, how to access them and problems associated.

• Arthur M. Lesk. Introduction to bioinformatics (Third Edition) Oxford University Press