Journal
METHODS
Volume 61, Issue 3, Pages 219-226Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ymeth.2013.03.004
Keywords
Mass spectrometry; Tandem mass spectrometry; Protein identification; Protein post translational modification; Computational analysis
Funding
- NIH [R24DK090963, U54CA112967]
- Integrative Cancer Biology Program Fellowship
- Charles S. Krakauer Fellowship
- Hugh Hampton Young Fellowship
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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. (C) 2013 Elsevier Inc. All rights reserved.
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