DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput.

Journal: Nature methods
Published Date: Jan 1, 2020

Abstract

We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the processing of data-independent acquisition (DIA) proteomics experiments. DIA-NN improves the identification and quantification performance in conventional DIA proteomic applications, and is particularly beneficial for high-throughput applications, as it is fast and enables deep and confident proteome coverage when used in combination with fast chromatographic methods.

Authors

  • Vadim Demichev
    Department of Biochemistry and The Milner Therapeutics Institute, University of Cambridge, Cambridge, UK.
  • Christoph B Messner
    The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK.
  • Spyros I Vernardis
    The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK.
  • Kathryn S Lilley
    Department of Biochemistry, Cambridge Centre for Proteomics, University of Cambridge, Cambridge CB2 1GA, UK.
  • Markus Ralser
    The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK. markus.ralser@crick.ac.uk.

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