A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics

By Alexey I. Nesvizhskii. Journal of Proteomics, 2010

525 citations recorded by the source corpus.

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.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/631fe3da480f8d903f574cfb89f6d06c1cef6947

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

In Spectroscopy

Citations in the corpus

A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets (2015, background): “Although other more sophisticated strategies exist for calculating protein scores (22, 28), using the best peptide hit or the sum of peptide scores are common practice in the proteomics community.”
DIA-Umpire: comprehensive computational framework for data independent acquisition proteomics (2015, methodology): “%) estimated via the target-decoy approach 2 .”
MSFragger: ultrafast and comprehensive peptide identification in shotgun proteomics (2017, methodology): “(1) MSFragger spectra input and pre-processing—MSFragger accesses mzXML and mzML files using MSFTBX, the Data Access Library provided as part of the BatMass project 33 and Mascot Generic File (MGF) files using an internal parser.”
iProphet: Multi-level Integrative Analysis of Shotgun Proteomic Data Improves Peptide and Protein Identification Rates and Error Estimates* (2011, methodology): “Thus, additional modeling is necessary to calculate statistical measures more suitable for filtering of large collections of MS/MS database search results, such as FDR(48) and posterior peptide and protein probabilities (37, 49–51).”
IonQuant Enables Accurate and Sensitive Label-Free Quantification With FDR-Controlled Match-Between-Runs (2020, methodology): “In all datasets, we estimated the identification false-discovery rate using the target-decoy approach (4).”
Analysis of High Accuracy, Quantitative Proteomics Data in the MaxQB Database (2012, background): “…in MS-based proteomics to control the proportion of false identifications by searching against a combined forward and de-coy sequence database and then adjusting the cutoff score to a value, such that the proportion of identified decoy hits is equal to a given false discovery rate (FDR) (31, 32).”
Trans-Proteomic Pipeline, a standardized data processing pipeline for large-scale reproducible proteomics informatics (2015, background): “Although there are a tremendous number of individual tools for processing proteomics data [6] and some developed as pipelines [7,8], the Trans-Proteomic Pipeline (TPP; [9,10]) is the most widely used free and open-source complete suite of tools for processing shotgun proteomics data with statistical”
Comet: An open‐source MS/MS sequence database search tool (2013, background): “For a comprehensive list of MS/MS search tools, see the following review article [7].”
Proteogenomics: concepts, applications, and computational strategies (2014, methodology): “In this man-KnownKnownNovel tides to obtain the same level of confidence 3 , novel peptides identified using proteogenomic approaches should be required to have stronger evidence than known peptides.”