Project description:A common technique used for sensitive and specific diagnostic virus detection in clinical samples is PCR. However, an unbiased diagnostic microarray containing probes for all human pathogens could replace hundreds of individual PCR-reactions and remove the need for a clear clinical hypothesis regarding a suspected pathogen. We have established such a diagnostic platform for unbiased random amplification and subsequent microarray identification of viral pathogens in clinical samples. We show that Phi29 polymerase-amplification of a diverse set of clinical samples generates enough viral material for successful identification by the Microbial Detection Array developed at the Lawrence Livermore National Laboratory, California, USA, demonstrating the potential of the microarray technique for broad-spectrum pathogen detection. We conclude that this method detects both DNA and RNA virus, present in the same sample, as well as differentiates between different virus subtypes. We propose this assay for unbiased diagnostic analysis of all viruses in clinical samples.
Project description:This project contains raw data, intermediate files and results is a re-analysis of the publicly available dataset from the PRIDE dataset PXD005780. The RAW files were processed using ThermoRawFileParser, SearchGUI and PeptideShaker through standard settings (see ‘Data Processing Protocol’). This reanalysis work is part of the MetaPUF (MetaProteomics with Unknown Function) project, which is a collaboration between EMBL-EBI and the University of Luxembourg. The dataset was selected with the following conditions: 1. It has been made publicly available in PRIDE and focuses on metaproteomics of the human gut; 2. The corresponding metagenomics assemblies were also available from ENA (European Nucleotide Archive) or MGnify. The processed peptide reports for each sample are available to view at the contig level on the MGnify website. In total, the reanalysis identified 15,417 unique proteins from 15 samples.
