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. A deep learning-based software tool, DIA-NN, enables deep proteome analysis from data generated using fast chromatographic approaches and data-independent acquisition mass spectrometry.

Keywords

Deep learningComputer scienceProteomeThroughputArtificial neural networkSoftwareDeep neural networksProteomicsArtificial intelligenceIdentification (biology)Mass spectrometryInterference (communication)Software suiteMachine learningBioinformaticsChemistryChromatographyBiology

MeSH Terms

HeLa CellsHigh-Throughput Screening AssaysHumansMass SpectrometryNeural NetworksComputerProteomeProteomicsSoftwareSpecies SpecificityZea mays

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Publication Info

Year
2019
Type
article
Volume
17
Issue
1
Pages
41-44
Citations
2678
Access
Closed

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Cite This

Vadim Demichev, Christoph B. Messner, Spyros I. Vernardis et al. (2019). DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nature Methods , 17 (1) , 41-44. https://doi.org/10.1038/s41592-019-0638-x

Identifiers

DOI
10.1038/s41592-019-0638-x
PMID
31768060
PMCID
PMC6949130

Data Quality

Data completeness: 86%