ArticleNature methods2020
DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput.
Article in Nature methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1,954 papers.
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Who cites it
1,954 citing papers in PubMed.
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- Implementation of Nonisobaric TMT Analogs for Accurate Precursor-Level Quantification by plexDIA.Rapid communications in mass spectrometry : RCM · 2026Article
- OpenDVP: an experimental and computational framework for community-empowered deep visual proteomics.Nature methods · 2026Article
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1,894 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
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.
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