Radiant DIA: A Fast, Sensitive, and Accurate Search Engine for Quantitative Proteomics
Seth JustiD, Lee S. CantrelliD, Andrew NicholsiD, Jian WangiD, Janos Kis, Iman Mohtashemi, Theo Platt, Omid Cameron FarokhzadiD, Serafim BatzoglouiD
Abstract
In mass spectrometry-based proteomics, robust and efficient search engines are essential for accurate peptide and protein identification and quantification. Advances in sample preparation and instrumentation have increased the demand for highly scalable processing tools, with datasets comprising hundreds or thousands of samples in single-cell and population studies. Here we present Radiant DIA, a novel Data-Independent Acquisition search engine which achieves 4x faster processing and 10x lower cloud compute costs for large experiments while ensuring rigorous control of false discovery rate (FDR) and maintaining similar sensitivity, precision, and quantitative accuracy to widely-used tools. The Radiant DIA search engine is paired with a modular pipeline deployable on cloud and desktop environments comprising individual modules for distributed re-scoring, FDR estimation, protein inference and quantification. Unlike traditional monolithic applications, this architecture enables high-performance, cloud-scale analysis without sacrificing local usability. Together, the Radiant DIA and Fulcrum Pipeline tools enhance computational efficiency to facilitate biological discovery in large-scale proteomics, as demonstrated by analyses of real-world experiments up to thousands of MS acquisitions.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 1
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Integrating Narrow-Window DIA with AI-Powered Search Enables Deep and Reliable Functional Proteomics
Yun Xiong, Dejan Štepec, … Philip L. Lorenzi·bioRxiv·Sep 10, 2026
- Cloud-enabled Scalable Analysis of Large Proteomics Cohorts
Harendra Guturu, Andrew C. Nichols, … Serafim Batzoglou·Journal of Proteome ResearchPeer-reviewed·Feb 13, 2025
- msallsearchpy: Universal Search Engine with Enhanced Performance for High-Flow Quantitative LC-MS Proteomics
Mark V. Ivanov, Leyla A. Garibova, … Mikhail V. Gorshkov·bioRxiv·Oct 22, 2026
- Pioneer and Altimeter: Fast Analysis of DIA Proteomics Data Optimized for Narrow Isolation Windows
Nathan T. Wamsley, Emily Wilkerson, … Dennis Goldfarb·bioRxiv·Sep 10, 2026
- DIAFree enables untargeted open-search identification for Data-Independent Acquisition data
I. Xu·bioRxiv·Aug 30, 2020
- One-stop analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform
Fengchao Yu, Guo Ci Teo, … Alexey I. Nesvizhskii·bioRxiv·Oct 28, 2022