Mass spectrometry based protein identification with accurate statistical significance assignment

TL;DR

This paper introduces a novel protein identification method in mass spectrometry that accurately assigns statistical significance at the protein level by weighting peptide evidence, improving false discovery rate estimation without empirical adjustments.

Contribution

The authors developed a protein ID approach that combines peptide evidence using a rigorous formula, providing accurate protein-level E-values and eliminating the need for empirical post-processing.

Findings

Accurate protein-level E-values achieved without empirical methods

Method yields reliable false discovery proportion estimates

Comparable retrieval efficacy to existing methods

Abstract

Motivation: Assigning statistical significance accurately has become increasingly important as meta data of many types, often assembled in hierarchies, are constructed and combined for further biological analyses. Statistical inaccuracy of meta data at any level may propagate to downstream analyses, undermining the validity of scientific conclusions thus drawn. From the perspective of mass spectrometry based proteomics, even though accurate statistics for peptide identification can now be achieved, accurate protein level statistics remain challenging. Results: We have constructed a protein ID method that combines peptide evidences of a candidate protein based on a rigorous formula derived earlier; in this formula the database $P$-value of every peptide is weighted, prior to the final combination, according to the number of proteins it maps to. We have also shown that this protein ID method provides accurate protein level $E$-value, eliminating the need of using empirical post-processing methods for type-I error control. Using a known protein mixture, we find that this protein ID method, when combined with the Soric formula, yields accurate values for the proportion of false discoveries. In terms of retrieval efficacy, the results from our method are comparable with other methods tested. Availability: The source code, implemented in C++ on a linux system, is available for download at ftp://ftp.ncbi.nlm.nih.gov/pub/qmbp/qmbp_ms/RAId/RAId_Linux_64Bit