Proteomics

Dataset Information

0

Improved detection of differentially abundant proteins through FDR-control of peptide-identity-propagation


ABSTRACT: We present a method for FDR control of PIP, called “PIP-ECHO” (PIP Error Control via Hybrid cOmpetition) and devise a rigorous protocol for evaluating FDR control of any PIP method. Using three different datasets, we evaluate PIP-ECHO alongside the PIP procedures implemented by FlashLFQ, IonQuant, and MaxQuant. These analyses show that PIP-ECHO can accurately control the FDR of PIP at 1% across multiple datasets. Only PIP-ECHO was able to control the FDR in data with injected sample size equivalent to a single-cell dataset. The three other methods fail to control the FDR at 1%, yielding false discovery proportions ranging from 2-6%. We demonstrate the practical implications of this work by performing differential expression analyses on spike-in datasets, where different known amounts of yeast or E. coli peptides are added to a constant background of HeLa cell lysate peptides. In this setting, PIP-ECHO increases both the accuracy and sensitivity of differential expression analysis: our implementation of PIP-ECHO within FlashLFQ can detect twice as many differentially abundant proteins as MaxQuant and three times as many as IonQuant in the spike-in dataset. This repository contains the raw data for one of the datasets used to evaluate the error rate of PIP, as well as the database search and quantitative results obtained for all of the datasets examined.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human) Escherichia Coli

SUBMITTER: Alexander Solivais  

LAB HEAD: Lloyd M. Smith

PROVIDER: PXD057758 | Pride | 2025-10-06

REPOSITORIES: Pride

altmetric image

Publications

Improved Detection of Differentially Abundant Proteins through FDR-Control of Peptide-Identity-Propagation.

Solivais Alexander J AJ   Boekweg Hannah H   Smith Lloyd M LM   Noble William S WS   Shortreed Michael R MR   Payne Samuel H SH   Keich Uri U  

Journal of proteome research 20250730 9


The goal of proteomics is to identify and quantify peptides and proteins within a biological sample. Almost all algorithms for the identification of peptides in LC-MS/MS data employ two steps: peptide/spectrum matching and peptide-identity-propagation (PIP), also known as match-between-runs. PIP can routinely account for up to 40% of all results, with that proportion rising as high as 75% in single-cell proteomics. Unlike peptide identities derived through peptide/spectrum matches, for which err  ...[more]

Similar Datasets

2021-08-18 | PXD024584 | Pride
2022-06-09 | PXD030340 | Pride
2023-03-10 | PXD033865 | Pride
2021-07-26 | PXD026875 | Pride
2025-08-18 | GSE281782 | GEO
2021-07-09 | PXD022589 | Pride
2021-07-09 | PXD022582 | Pride
2015-04-25 | E-GEOD-57108 | biostudies-arrayexpress
2018-07-19 | GSE109916 | GEO
2022-05-23 | PXD027467 | Pride