Abstract
The principal goal in proteomics is to extract biologically or clinically meaningful information from large-scale studies in order to provide new insights into fundamental biological processes or find new means to diagnose or treat disease. Many labs now have methods and machinery in place that make possible the robust generation of many thousands of protein identifications each day. This, however, has exposed a new major bottleneck in the proteomics workflow—the problem of analyzing the wealth of protein identifications to find the relatively few proteins that actually render support for biological hypotheses or that have potential medical relevance.
This presentation shows the application of a new bioinformatics tool that almost entirely removes this bottleneck. The new technology was specifically developed to help researchers gain a fast overview of biologically relevant features in vast protein datasets and rapidly zoom in on single proteins or subsets of proteins of particular interest. In a matter of minutes, the output from the MS database search software was turned into information that was biologically more meaningful. This was done by means of sequential steps that: (1) collapsed all protein redundancies into non-redundant lists; (2) filtered/sorted these lists based on experimental observations and biological sequence annotation that was automatically added; and (3) compared/combined lists of annotated proteins to elucidate differences and overlaps between multiple experimental datasets.
The presentation will show through examples how the new technology can be used in proteomics experiments in order to accelerate the otherwise tedious process of making biological sense of lists of protein accession codes. We present the efficient data mining and categorizing of several large datasets of proteins, including data uploaded from the PRIDE database and HUPO projects.