Biology subjects

Freestone, J. A.

Publications and source records attributed to Freestone, J. A..

4 recordsLinked to original sources

bioRxiv2024

Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment

A pressing statistical challenge in the field of mass spectrometry proteomics is how to assess whether a given software tool provides accurate error control. Each software tool for searching such data uses its own internally implemented methodology for reporting and controlling the error. Many of these software tools are closed source, with incompletely documented methodology, and the strategies for validating the error are inconsistent across tools. In this work, we identify three different methods for validating false discovery rate (FDR) control in use in the field, one of which is invalid, one of which can only provide a lower bound rather than an upper bound, and one of which is valid but under-powered. The result is that the field has a very poor understanding of how well we are doing with respect to FDR control, particularly for the analysis of data-independent acquisition (DIA) data. We therefore propose a theoretical formulation of entrapment experiments that allows us to rigorously characterize the behavior of the various entrapment methods. We also propose a more powerful method for evaluating FDR control, and we employ that method, along with other existing techniques, to characterize a variety of popular search tools. We empirically validate our entrapment analysis in the fairly well-understood DDA setup before applying it in the DIA setup. We find that none of the DIA search tools consistently controls the FDR at the peptide level, and the tools struggle particularly with analysis of single cell datasets.

bioinformatics↗

bioRxiv2023

How to train a post-processor for tandem mass spectrometry proteomics database search while maintaining control of the false discovery rate

Decoy-based methods are a popular choice for the statistical validation of peptide detections in tandem mass spectrometry proteomics data. Such methods can achieve a substantial boost in statistical power when coupled with post-processors such as Percolator that use auxiliary features to learn a better-discriminating scoring function. However, we recently showed that Percolator can struggle to control the false discovery rate (FDR) when reporting the list of discovered peptides. To address this problem, we introduce Percolator-RESET, which is an adaptation of our recently developed RESET meta-procedure to the peptide detection problem. Specifically, Percolator-RESET fuses Percolators iterative SVM training procedure with RESETs general framework of determining the list of reported discoveries in a target-decoy competition setup, where each putative discovery is augmented with a list of relevant features. Percolator-RESET operates in both a standard single-decoy mode and a two-decoy mode, the latter requiring the generation of two decoys per target. We demonstrate that Percolator-RESET controls the FDR in both modes, both theoretically and empirically, while typically reporting only a marginally smaller number of discoveries than Percolator in single-decoy mode. The two-decoy mode is marginally more powerful than both Percolator and the single-decoy mode and exhibits less variability than the latter.

bioinformatics↗

bioRxiv2023

Re-investigating the correctness of decoy-based false discovery rate control in proteomics tandem mass spectrometry

Traditional database search methods for the analysis of bottom-up proteomics tandem mass spectrometry (MS/MS) data are limited in their ability to detect peptides with post-translational modifications (PTMs). Recently, "open modification" database search strategies, in which the requirement that the mass of the database peptide closely matches the observed precursor mass is relaxed, have become popular as a way to find a wider variety of types of PTMs. Indeed, in one study, Kong et al. reported that the open modification search tool MSFragger can achieve higher statistical power to detect peptides than a traditional "narrow window" database search. At the same time, Kong et al. reported that their empirical results suggest a problem with false discovery (FDR) control in the narrow window setting. We investigated these claims empirically and, in the process, uncovered a potential problem with FDR control in the machine learning post-processors Percolator and PeptideProphet. However, we also found that, after accounting for chimeric spectra as well as for the inherent difference in the number of candidates in open and narrow searches, the data does not provide sufficient evidence that FDR control in proteomics MS/MS database search is problematic.

bioinformatics↗

bioRxiv2023

Analysis of tandem mass spectrometry data with CONGA: Combining Open and Narrow searches with Group-wise Analysis

Searching tandem mass spectrometry proteomics data against a database is a well-established method for assigning peptide sequences to observed spectra but typically cannot identify peptides harboring unexpected post-translational modifications (PTMs). Open modification searching aims to address this problem by allowing a spectrum to match a peptide even if the spectrums precursor mass differs from the peptide mass. However, expanding the search space in this way can lead to a loss in statistical power to detect peptides. We therefore developed a method, called CONGA, that takes into account results from both types of searches--a traditional "narrow window" search and an open modification search--while carrying out rigorous false discovery rate (FDR) control. The result is an algorithm that provides the best of both worlds: the ability to detect unexpected PTMs without a concomitant loss of power to detect unmodified peptides.

bioinformatics↗