Translational Proteomics: Solving the Reproducibility Riddle
Data analysis in proteomics is not fit for purpose – here’s how we can get on track
David Chiang |
Proteomics, with its unlimited potential for biomedicine, has so far fallen short. I believe the reason is simple: sophisticated big data is being processed by simplistic bioinformatics with underpowered computers. Novices are dazzled by thousands of proteins characterized at the push of a button. But experts find that it is mostly common proteins that are correctly identified, much of the quantitation is suspect, and – critically – it is hard to tell whether an identification is correct. How can we improve the utility of proteomics for identifying important low-abundance proteins? The trick is to borrow data analysis from numerical data mining in physics, not abstract statistics.
Let’s say we run a pneumonia sample to identify pathogens from proteins with a mass spectrometer. We process a gigabyte file with 50K raw spectra with a fast PC program that identifies and quantifies peptides and proteins from 20 percent of the spectra at 1 percent error. When analysis is so easy, who needs hypotheses or data understanding? We just need “better” software – defined as faster and cheaper and reporting more proteins. Of course, this assumes 1 percent error is enough, a self-estimated error is always robust, and quantity means quality – all of which are obviously incorrect.
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