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AlphaDIA enables End-to-End Transfer Learning for Feature-Free Proteomics

ABSTRACT: Mass spectrometry (MS)-based proteomics continues to evolve rapidly, opening more and more application areas. The scale of data generated on novel instrumentation and 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 particularly suited for detecting patterns in data produced by sensitive time-of-flight instruments. It naturally adapts to novel, more efficient scan modes that are not yet accessible to previous algorithms. Rigorous benchmarking demonstrates competitive identification and quantification performance. While supporting empirical spectral libraries, we propose a new search strategy named end-to-end transfer learning using 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 (PTM). AlphaDIA provides a high performance and accessible framework running locally or in the cloud, opening DIA analysis to the community.

INSTRUMENT(S): timsTOF Ultra, Orbitrap Astral, ZenoTOF 7600

ORGANISM(S): Arabidopsis Thaliana (ncbitaxon:3702) Escherichia Coli (ncbitaxon:562) Homo Sapiens (ncbitaxon:9606) Saccharomyces Cerevisiae (ncbitaxon:4932)

SUBMITTER: Matthias Mann

PROVIDER: MSV000095138 | MassIVE | Mon Jun 24 15:02:00 BST 2024

REPOSITORIES: MassIVE

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