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Protein Probability Model for High-Throughput Protein Identification by Mass Spectrometry-Based Proteomics
journal contribution
posted on 2020-02-25, 01:29 authored by Gorka Prieto, Jesús VázquezShotgun proteomics is the method of choice for high-throughput
protein identification; however, robust statistical methods are essential
to automatize this task while minimizing the number of false identifications.
The standard method for estimating the false discovery rate (FDR)
of individual identifications and keeping it below a threshold (typically
1%) is the target-decoy approach. However, numerous works have shown
that FDR at the protein level may become much larger than FDR at the
peptide level. The development of an appropriate scoring model to
identify proteins from their peptides using high-throughput shotgun
proteomics is highly needed. In this study, we present a novel protein-level
scoring algorithm that uses the scores of the identified peptides
and maintains all of the properties expected for a true protein probability.
We also present a refinement of the picked method
to calculate FDR at the protein level. These algorithms can be used
together as a robust identification workflow suitable for large-scale
proteomics, and we show that the identification performance of this
workflow is superior to that of other widely used methods in several
samples and using different search engines. Our protein probability
model offers the scientific community an algorithm that is easy to
integrate into protein identification workflows for the automated
analysis of shotgun proteomics data.
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protein probability modelFDRshotgun proteomics dataHigh-Throughput Protein IdentificationMass Spectrometry-Based Proteomics Shotgun proteomicsmethodpeptideprotein levelprotein identification workflowsProtein Probability Modelalgorithmhigh-throughput protein identificationhigh-throughput shotgun proteomics
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