Article

diagFDR: Verifiable FDR Reporting in Proteomics via Scope, Calibration, and Stability Diagnostics

2026-04-20

Abstract excerpt

<h4> A bstract </h4> In mass spectrometry-based proteomics, false discovery rate (FDR) control underpins the credibility of peptide and protein identifications. In contemporary workflows, including multi-run Data Independent Acquisition (DIA), deep learning-assisted scoring, library-free searches, and extensive post-processing, the statement “1% FDR” has become increasingly ambiguous, potentially referring to d...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 0a35bdac-6380-5ba8-ad76-1d14014152f5
DOI: 10.64898/2026.04.16.718468

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

diagFDR: Verifiable FDR Reporting in Proteomics via Scope, Calibration, and Stability DiagnosticsDOI 10.64898/2026.04.16.718468Read

From variability to consensus: rescoring harmonizes peptide identification across diverse search engines and datasetsSemantic proximity Read

Protein Language Model Decoys for Target Decoy Competition in Proteomics: Quality Assessment and BenchmarksSemantic proximity Read

Focus on the spectra that matter by clustering of quantification data in shotgun proteomicsSemantic proximity Read

Removing the hidden data dependency of DIA with predicted spectral librariesSemantic proximity Read

Universal Baseline for <i>in vitro</i> Selection of Genetically Encoded LibrariesSemantic proximity Read

Translation uniformity, evolutionary conservation, and peptide properties associate with microprotein mass spectrometry detectionSemantic proximity Read

APIR: Aggregating Universal Proteomics Database Search Algorithms for Peptide Identification with FDR ControlSemantic proximity Read

LooMS: a novel peptide identification tools for data independent acquisitionSemantic proximity Read

Integration of Bulk RNA-seq Pipeline Metrics for Assessing Low-Quality SamplesSemantic proximity Read

BDQC: a general-purpose analytics tool for domain-blind validation of Big DataSemantic proximity Read

Pipelines and Systems for Threshold Avoiding Quantification of LC-MS/MS dataSemantic proximity Read

Learning Fragmentation Physics or Exploiting Sequence Priors? Benchmarking Bias in Deep Learning Models for De Novo Peptide SequencingSemantic proximity Read