PMC:5137214 / 43144-44519 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/5137214","sourcedb":"PMC","sourceid":"5137214","source_url":"https://www.ncbi.nlm.nih.gov/pmc/5137214","text":"Quantitative shotgun proteomics has developed into a remarkably powerful technology that enables sophisticated questions on cellular physiology to be asked. The total volume of proteomics data generated per year now ranges in the petabytes. This is paralleled by an increasing number of available proteomics datasets in the public domain that can be reused and reanalyzed, with as many as 100 new datasets being made available per month on the proteomics data repository PRIDE [85]. Hence joining next-generation genomics, proteomics has become a veritable source of biomedical “big data”. As our capacity for data generation surges, opportunities for breakthroughs will increasingly come from not how much more data we can generate, but how well we can make sense of the results. As a corollary, the need for proteomics big data solutions is poised to skyrocket in the coming few years, where new resources, tools, and ways of doing science are needed to rethink how best to harness datasets and discern deeper meanings. The production of biological knowledge will involve tools and solutions devised in the field of data science, including those concerning data management, multivariate analysis, statistical learning, predictive modeling, software engineering, and crowdsourcing. Several current limitations and possible future frontiers, out of many, are discussed below:","tracks":[]}