Simon Twigger, PhD Assistant Professor, Physiology Specialization: Bioinformatics, Model Organism Databases and Data Mining Tel: (414)456-8802 Dr. Twigger's CV Email Dr. Twigger
The term 'Information Age' has been used to describe our current era - we are spending more and more time dealing with an ever growing pile of information than ever before in history. In the biomedical area we are perhaps one step back, in the 'Data Age', buried in data that has to be converted to meaningful information before it can be used.
Bioinformatics tackles this challenge through the use of computers, software and related technologies. Our goal is to manage data, convert it to information useable by scientists and facilitate its conversion to actual knowledge about a biological system.
We have a variety of projects addressing all aspects of this problem. The NHLBI national proteomics center is developing new software and computational architectures to analyze and process mass spec proteomics data. The Rat Genome Database combines human expertise and bioinformatics to house the genomic and physiological data for the laboratory Rat. As part of the Gene Ontology Consortium we annotate rat genes and other data with functional information, aiding interpretation of their biological roles. These are tied together with web-based annotation tools we are developing to annotate Human Stem Cell proteomics data in a collaboration with Drs Jamie Thomson and Lloyd Smith at UW. Madison. Working with the National Center for Biomedical Ontology we are a driving biological project investigating the use of biomedical ontologies to extract information from the wealth of raw data stored in the GEO microarray database.
The data tsunami is blowing away our concepts of data storage and analysis. Terra bytes of data are a normal consequence of a single 'NextGen' sequencing run. Our emerging research focus is on developing solutions to problems of this scale. Using Amazon's cloud computing solutions we are creating a virtual proteomics analysis platform that can scale on demand as the data increases. We are going to be using web services to extract billions of annotations from data repositories so these can be added to the wealth of biological knowledge available to scientists. Figuring out how to process, store, visualize and interpret all this information will be our significant challenge going forward.
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