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AlphaDIA enables DIA Transfer Learning for Feature-Free Proteomics

ABSTRACT: The scale of data generated for mass spectrometry-based proteomics as well as modern acquisition strategies pose a challenge to bioinformatic analysis. Search engines need to make optimal use of the data for biological discoveries while remaining statistically rigorous, transparent and performant. Here we present alphaDIA, a modular open-source search framework for data independent acquisition (DIA) proteomics. We developed a feature-free identification algorithm that performs machine learning directly on the raw signal and is particularly suited for detecting patterns in data produced by time-of-flight instruments. Benchmarking demonstrates competitive identification and quantification performance. While the method supports empirical spectral libraries, we propose a search strategy named DIA transfer learning that uses fully predicted libraries. This entails continuously optimizing a deep neural network for predicting machine- and experiment-specific properties, enabling the generic DIA analysis of any post-translational modification. AlphaDIA provides a high performance and accessible framework running locally or in the cloud, opening DIA analysis to the community.

INSTRUMENT(S): Orbitrap Astral

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Matthias Mann

PROVIDER: MSV000098448 | MassIVE | Tue Jul 08 05:07:00 BST 2025

SECONDARY ACCESSION(S): PXD065896

REPOSITORIES: MassIVE

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