Computer aided manual validation of mass spectrometry-based proteomic data.

TitleComputer aided manual validation of mass spectrometry-based proteomic data.
Publication TypeJournal Article
Year of Publication2013
AuthorsCurran, TG, Bryson, BD, Reigelhaupt, M, Johnson, H, White, FM
Date Published2013 Jun 15

Advances in mass spectrometry-based proteomic technologies have increased the speed of analysis and the depth provided by a single analysis. Computational tools to evaluate the accuracy of peptide identifications from these high-throughput analyses have not kept pace with technological advances; currently the most common quality evaluation methods are based on statistical analysis of the likelihood of false positive identifications in large-scale data sets. While helpful, these calculations do not consider the accuracy of each identification, thus creating a precarious situation for biologists relying on the data to inform experimental design. Manual validation is the gold standard approach to confirm accuracy of database identifications, but is extremely time-intensive. To palliate the increasing time required to manually validate large proteomic datasets, we provide computer aided manual validation software (CAMV) to expedite the process. Relevant spectra are collected, catalogued, and pre-labeled, allowing users to efficiently judge the quality of each identification and summarize applicable quantitative information. CAMV significantly reduces the burden associated with manual validation and will hopefully encourage broader adoption of manual validation in mass spectrometry-based proteomics.

Alternate JournalMethods
PubMed ID23500044
PubMed Central IDPMC3700613
Grant ListR24 DK090963 / DK / NIDDK NIH HHS / United States
R24DK090963 / DK / NIDDK NIH HHS / United States
U01 CA154966 / CA / NCI NIH HHS / United States
U54 CA112967 / CA / NCI NIH HHS / United States
U54CA112967 / CA / NCI NIH HHS / United States