Journal
MOLECULAR & CELLULAR PROTEOMICS
Volume 6, Issue 3, Pages 527-536Publisher
AMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INC
DOI: 10.1074/mcp.T600049-MCP200
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Funding
- NATIONAL CANCER INSTITUTE [R01CA095586] Funding Source: NIH RePORTER
- NATIONAL CENTER FOR RESEARCH RESOURCES [M01RR000040] Funding Source: NIH RePORTER
- NATIONAL HEART, LUNG, AND BLOOD INSTITUTE [P50HL070128, P50HL081012, P01HL062250, P50HL054500] Funding Source: NIH RePORTER
- NCI NIH HHS [R01CA95586] Funding Source: Medline
- NCRR NIH HHS [M01RR00040] Funding Source: Medline
- NHLBI NIH HHS [P50 HL81012, HL 62250, P50 HL70128, HL 54500, HL 70128] Funding Source: Medline
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MS/MS combined with database search methods can identify the proteins present in complex mixtures. High throughput methods that infer probable peptide sequences from enzymatically digested protein samples create a challenge in how best to aggregate the evidence for candidate proteins. Typically the results of multiple technical and/or biological replicate experiments must be combined to maximize sensitivity. We present a statistical method for estimating probabilities of protein expression that integrates peptide sequence identifications from multiple search algorithms and replicate experimental runs. The method was applied to create a repository of 797 non-homologous zebrafish (Danio rerio) proteins, at an empirically validated false identification rate under 1 %, as a resource for the development of targeted quantitative proteomics assays. We have implemented this statistical method as an analytic module that can be integrated with an existing suite of open-source proteomics software.
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