DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput
Nature methods, 2020•nature.com
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.
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.
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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