Education and data-intensive science in the beginning of the 21st century.

Data-intensive science will open up new avenues to explore, new questions to ask, and new ways to answer. Yet, this potential cannot be unlocked without new emphasis on education of the researchers gathering data, the analysts analyzing data and the cross-disciplinary participants working together to make it happen. This article is a summary of the education issues and challenges of data-intensive sciences and cloud computing as discussed in the Data-Intensive Science (DIS) workshop in Seattle, September 19-20, 2010.

Design and initial characterization of the SC-200 proteomics standard mixture.

High-throughput (HTP) proteomics studies generate large amounts of data. Interpretation of these data requires effective approaches to distinguish noise from biological signal, particularly as instrument and computational capacity increase and studies become more complex. Resolving this issue requires validated and reproducible methods and models, which in turn requires complex experimental and computational standards. The absence of appropriate standards and data sets for validating experimental and computational workflows hinders the development of HTP proteomics methods. Most protein standards are simple mixtures of proteins or peptides, or undercharacterized reference standards in which the identity and concentration of the constituent proteins is unknown. The Seattle Children's 200 (SC-200) proposed proteomics standard mixture is the next step toward developing realistic, fully characterized HTP proteomics standards. The SC-200 exhibits a unique modular design to extend its functionality, and consists of 200 proteins of known identities and molar concentrations from 6 microbial genomes, distributed into 10 molar concentration tiers spanning a 1,000-fold range. We describe the SC-200's design, potential uses, and initial characterization. We identified 84% of SC-200 proteins with an LTQ-Orbitrap and 65% with an LTQ-Velos (false discovery rate = 1% for both). There were obvious trends in success rate, sequence coverage, and spectral counts with protein concentration; however, protein identification, sequence coverage, and spectral counts vary greatly within concentration levels.