Changes between Initial Version and Version 1 of Internships

Timestamp:

2011-02-01T10:33:32+01:00 (13 years ago)

Author:

jvelde

Comment:

--

Internships

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+[[TOC()]]
+
+= Available internship projects at the GCC =
+
+This list is just a grasp of nearly limitless possibilities. If you have another fruitful proposal yourself, feel free to come over and discuss.
+
+=== Comparative analysis of genetic screens for protein aggregation in  yeast, worms and flies ===
+
+Groningen Bioinformatics Centre: www.rug.nl/gbic
+
+Supervisor: Alex Kanterakis, Joeri van der Velde, Morris Swertz, Rainer Breitling
+
+Duration: 6 or 9 months
+
+Toxic protein aggregation is characteristic for many diseases including Alzheimer’s, Parkinson’s and polyglutamine diseases. Several genetic screens have been performed in small model organisms (yeast, worms (C. elegans) and fruitflies (Drosophila) for modifier genes that alter toxicity or aggregation of disease related proteins (alpha-synuclein and polyglutamine). In this project you will perform the first large-scale comparative analysis of the results of these screens in a standardized way, using bioinformatics and statistics tools. The aim is to detect common pathways and mechanisms, as well as specific differences between the various protein aggregation diseases. Results will also be integrated with data from genetic screen results (GWAS) in human populations of, e.g., Parkison’s disease patients. The project will include the unified annotation of screening results using KEGG, UniGene and Ensembl; Gene Ontology (GO) enrichment analysis using DAVID, GeneTrail and GOstat (R package), as well as pathway analysis and data integration using custom-developed bioinformatics tools.
+
+References:
+
+van Ham TJ, Breitling R, Swertz M, Nollen EAA (2009): Neurodegenerative diseases: Lessons from genome-wide screens in small model organisms. EMBO Molecular Medicine in press.
+
+van Ham TJ, Thijssen KL, Breitling R, Hofstra RMW, Plasterk RHA, Nollen EAA (2008): C. elegans model identifies genetic modifiers of α-synuclein inclusion formation during aging. PLoS Genetics 4:e10000027. 
+
+
+=== 3D Visualization of genetical genomic analysis ===
+
+Supervisor: Danny Arends, Joeri v/d Velde, M. Swertz
+
+Outline:
+
+O3D is a new initiative to bring 3D accelerated graphics to the web browser. During the last Google summer 
+of code advances were made by the open source community an a stable API is now available for initial development.
+We predict that open3D is a good way of visualizing biological data. Especially when we are looking at/trough multiple levels 
+(Organism, Cell, Protein, Transcription). In QTL analysis researchers search for association between traits and molecular 
+markers, however after identifying these QTLs there is still no easy way to extract knowledge. We think that 3D visualization 
+together with the use of real time user interaction would greatly enhance the toolbox of the current day life science researcher.
+This project is aimed a developing a pilot plug-in for the molgenis database system, that allows the user to navigate through a 
+3D space in which genetic data is visualized.
+
+Duration: ~ 6-9 Months
+
+Student Sketch:
+
+- Interested in 3D programming (and some experience)
+
+- Semi-familiar with JavaScript, Pythagoras
+
+References / Start:
+
+- O3D - http://code.google.com/intl/nl/apis/o3d/
+
+- genetical genomics - http://www.google.nl/search?q=genetical+genomics
+
+- JavaScript event handling
+
+=== Estimating optimal genetic marker placement ===
+
+Supervisor: Danny Arends, R.C Jansen
+
+Outline:
+
+Quantitative Trait Likelihood (QTL) analysis tries to determine genetic locations governing quantitative traits like blood pressure. MQM (Multiple QTL Mapping) is an algorithm which enables researchers to estimate in an automated way the importance of genetic markers. This is useful when large datasets need unsupervised clustering or classification of data. However usually too many markers are available in based on prior observations. The MQMalgorithm has already been successfully applied in several fields of biology, like plant breeding, drug discovery and . MQM has multiple advantages over other QTL mapping approaches. One of the main advantages are: Ability to 'compensate' for the effect of other markers, quick because the computational heavy work is programmed in C, and is available for R programming environment (+ other mappings). The R/qtl package brings together several QTL mapping strategies and also includes MQM. To increase detection of QTLs by using MQM cofactors are used. However the placement of these cofactors along the genome is still an issue of debate. To investigate cofactor placement several approaches can be adopted. The goal is to investigate multiple placement strategies, and their effect on detection of QTLs
+
+Duration: ~ 6 Months
+
+Student Sketch:
+
+- Interested in 2D maps, statistics
+
+- Semi-familiar with R (or C++), linear regression
+
+References / Start:
+
+- R/qtl - http://www.rqtl.org/
+
+- MQM - http://www.google.nl/search?q=MQM+Jansen
+
+- QTL mapping
+
+
+