Solving Data Overload
Today’s mass specs produce far more data than researchers can use. Experimental design is affected by this paradigm; massive amounts of data are discarded to create smaller, biased datasets that can be managed with limited computational tools.
Smaller, more manageable datasets result when researchers treat their samples in ways that select which molecules enter the mass spec or whether they allow mass spec software to select a subset of molecules for identification. Selection of molecules to be analyzed is independent of the experimental question and many molecules of potential interest are ignored.
DNA sequencing once suffered from this limitation. Limitations on sequencing capacity forced researchers to ask whether specific sequences were connected to biological processes. The arrival of next-generation sequencing technology allowed for genome-wide association studies (GWAS), where entire genomes could be analyzed to find small sequences linked to biological effects.
GWAS studies consider the biological question before the data is queried, eliminating biases and allowing unexpected discovery. Magellan Bioanalytics allows this same change in approach with mass spec data.