Deep learning for peptide identification from metaproteomics datasets.

ABSTRACT: Metaproteomics is becoming widely used in microbiome research for gaining insights into the functional state of the microbial community. Current metaproteomics studies are generally based on high-throughput tandem mass spectrometry (MS/MS) coupled with liquid chromatography. In this paper, we proposed a deep-learning-based algorithm, named DeepFilter, for improving peptide identifications from a collection of tandem mass spectra. The key advantage of the DeepFilter is that it does not need ad hoc training or fine-tuning as in existing filtering tools. DeepFilter is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/DeepFilter. SIGNIFICANCE: The identification of peptides and proteins from MS data involves the computational procedure of searching MS

SUBMITTER: Feng S 

PROVIDER: S-EPMC8435027 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature