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

Nat Methods. 2020 Jan;17(1):41-44. doi: 10.1038/s41592-019-0638-x. Epub 2019 Nov 25.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • HeLa Cells
  • High-Throughput Screening Assays / methods*
  • Humans
  • Mass Spectrometry / methods*
  • Neural Networks, Computer*
  • Proteome / analysis*
  • Proteomics / methods*
  • Software*
  • Species Specificity
  • Zea mays / metabolism*


  • Proteome