As biology enters the age of big data, we are faced with the challenge of analyzing and making sense of data sets that are continuously growing in size and complexity. High throughput imaging platforms allow cell biologists to collect massive numbers of high resolution images of labeled cells, but reliably extracting useful data from these large image sets can be a challenge.
Due to a wide variety of issues, such as low signal to noise ratios and image artifacts, computational algorithms are often a poor substitute for manual analysis of image data. Likewise, next-generation sequencing technologies have provided genome researchers with huge quantities of sequence data. The key to understanding the differences between health and disease states lies in the effective analysis of this data.
Small changes in the genome, such as copy number variations (CNV), can be too subtle for automated algorithms to effectively capture, however, leaving potential disease-causing mutations to be overlooked. These CNVs can be readily identified by eye, but manually indicating the location of each of these variations across numerous genomes is a herculean task for an individual researcher.
Project Quorum is a flexible gaming platform which will crowdsource the analysis of visual data — such as microscopic images or graphical charts — that is provided directly by research scientists.