DIA-NN: Neural networks and interference correction enable deep proteome coverage in high throughput
By V. Demichev, C. Messner, S. Vernardis, K. Lilley, M. Ralser. Nature Methods, 2019
2,586 citations recorded by the source corpus.
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.
Subfields: Molecular Biology, Spectroscopy
View Paper: https://www.semanticscholar.org/paper/23fe9729cc5a95e0c59d08cca7dbe458989600be
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Citations in the corpus
- Transcription factors interact with RNA to regulate genes (2022, methodology): “1 (Demichev et al., 2020) against a FASTA file of the Homo sapiens UniProtKB database (release 2022_02, containing Swiss-Prot + TrEMBL and alternative isoforms).”