Workshop: Introduction into high-throughput data analysis using MapMan and Robin
10-11th May 2010 in Potsdam-Golm
Introduction
High throughput experiments like microarray-based expression studies, large-scale proteomics and metabolimics generate enormous amounts of data that is usually too confusing to be directly viewed or interpreted by the experimentator. The data has to be passed through several preprocessing steps in order to evaluate the quality, eliminate technical bias and statistically assess the data. Robin is a user-friendly graphical application that was specifically tailored to aid the individual researcher in the task of microarray data quality assessment, normalization and statistical inference of differential gene expression. Using Robin, Affymetrix, generic single channel (e.g. AGILENT) and two color microarrays can be processed in an self-explanatory and intuitive way, yielding overview of the data quality, experiment results and statistical analysis.
The results generated by Robin are formatted for direct import into the MapMan application. Using a specialized partly hand-curated functional ontology that classifies genes in so-called "Bins", MapMan offers a feature-rich environment to view and analyse the data in its biological context. MapMan is not restricted to transcript data generated in microarray analyses but can also be used with proteomics and metabolomics data and is even able to analyse all three data types together. In addition to visualizing differential expression levels on biological pathways, MapMan provides many useful functions like time-course display, clustering, Venn diagrams, statistical analysis of system responses etc.
Workshop
The workshop will be held at the Max-Planck-Institute of molecular plant physiology in Golm on the 10th and 11th of May 2010. Please find the program here. Workshop participation is free, but the participants will have to cover their travel and accomodation expenses. Up to 18 workstations are available - participants who would like to bring their own laptops are invited to do so but are asked to indicate that in their registration and install MapMan 3.5 beta and Robin beforehand. To make sure that the applications will run smoothly, the laptops should have a screen resolution of at least 1280x800, 2GB RAM and a modern processor (e.g. Core 2 Duo or comparable). Although we can only provide 12 workstations we accept up to 18 participants provided that enough people bring their own laptops. To register, simply send an email stating your interest, affiliation and scientific background to Marc Lohse. Data sets for demonstration of the applications will be provided at the workshop, nevertheless, participants are invited to bring their own data.
The first day will focus on microarray preprocessing and statistical analysis of differential expression giving a general introduction followed by hands-on demonstrations of the Robin application. On the second day, MapMan will be introduced and the various tools offered by this environment will be employed to get a deeper insight into the biology hidden in the data.
Registration
Please send an expression of interest to participate via email to Marc Lohse also stating how many people from your lab would like to attend. To make sure that as many individual labs as possible can benefit from the workshop, we may limit attendance to one person per lab if the number of interested people exceeds the workshop capacity.
Please visit the institute's website for detailed travel information. People who need accomodation should please indicate this in their registration email and also tell us how many nights should be reserved. We will try to organize accomodation in a local hotel. We cannot guarantee, though, that rooms will be available if people register at short notice - therefore we ask participants who need accomodation to contact us before 1st of april.
News, announcements and workshop material will be made available via this webpage so please check back to stay up to date. People having expressed interest via email will be informed automatically.