A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics.
This manuscript provides a comprehensive review of the peptide and protein identification process using tandem mass spectrometry (MS/MS) data generated in shotgun proteomic experiments. The commonly used methods for assigning peptide sequences to MS/MS spectra are critically discussed and compared, from basic strategies to advanced multi-stage approaches. A particular attention is paid to the problem of false-positive identifications. Existing statistical approaches for assessing the significance of peptide to spectrum matches are surveyed, ranging from single-spectrum approaches such as expectation values to global error rate estimation procedures such as false discovery rates and posterior probabilities. The importance of using auxiliary discriminant information (mass accuracy, peptide separation coordinates, digestion properties, and etc.) is discussed, and advanced computational approaches for joint modeling of multiple sources of information are presented. This review also includes a detailed analysis of the issues affecting the interpretation of data at the protein level, including the amplification of error rates when going from peptide to protein level, and the ambiguities in inferring the identifies of sample proteins in the presence of shared peptides. Commonly used methods for computing protein-level confidence scores are discussed in detail. The review concludes with a discussion of several outstanding computational issues.
Nesvizhskii
Alexey I
AI
Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA. nesvi@umich.edu
D019295:Computational Biology / Q000379:methods* / Q000706:statistics & numerical data; D030562:Databases, Protein; D005189:False Positive Reactions; D013058:Mass Spectrometry / Q000379:methods / Q000706:statistics & numerical data; D010455:Peptides / Q000032:analysis* / Q000737:chemistry; D011506:Proteins / Q000032:analysis* / Q000737:chemistry; D040901:Proteomics / Q000379:methods / Q000706:statistics & numerical data
D010455:Peptides; D011506:Proteins
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