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DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput
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DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput
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Journal Article
DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput
2020
Overview
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.
Publisher
Nature Publishing Group US,Nature Publishing Group
