期刊
NATURE METHODS
卷 17, 期 1, 页码 41-+出版社
NATURE PORTFOLIO
DOI: 10.1038/s41592-019-0638-x
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资金
- Francis Crick Institute from Cancer Research UK [FC001134]
- UK Medical Research Council [FC001134]
- Wellcome Trust [FC001134, 200829/Z/16/Z]
- BBSRC [BB/N015215/1, BB/N015282/1]
- Crick Idea to Innovation (i2i) initiative [10658]
- MRC [MC_UP_1202/8] Funding Source: UKRI
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. 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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