Project description:This series includes a 32-array training dataset used to evaluate E-Predict normalization and similarity metric parameters as well as 13 microarrays used as examples in (Urisman, et. al 2005). Training data set includes 15 independent HeLa RNAhybridizations (microarrays 1-15), 10 independent nasal lavage samples positive for Respiratory Syncytial virus (microarrays 16-25), and 7 independent nasal lavage samples positive for Influenza A virus (microarrays 26-32). Examples iclude a serum sample positive for Hepatitis B virus (microarray 33), a nasal lavage sample positive for both Influenza A virus and Respiratory Syncytial virus (microarray 34), and culture samples of 11 distinct Human Rhinovirus serotypes (microarrays 35-45). Keywords = virus detection, E-Predict, species identification, metagenomics Keywords: other
Project description:This series includes a 32-array training dataset used to evaluate E-Predict normalization and similarity metric parameters as well as 13 microarrays used as examples in (Urisman, et. al 2005). Training data set includes 15 independent HeLa RNAhybridizations (microarrays 1-15), 10 independent nasal lavage samples positive for Respiratory Syncytial virus (microarrays 16-25), and 7 independent nasal lavage samples positive for Influenza A virus (microarrays 26-32). Examples iclude a serum sample positive for Hepatitis B virus (microarray 33), a nasal lavage sample positive for both Influenza A virus and Respiratory Syncytial virus (microarray 34), and culture samples of 11 distinct Human Rhinovirus serotypes (microarrays 35-45). Keywords = virus detection, E-Predict, species identification, metagenomics
Project description:Viral infections are commonly diagnosed by the detection of viral genome fragments or proteins using targeted methods such as PCR and immunoassays. In contrast, metagenomics enables the untargeted identification of viral genomes, expanding its applicability across a broader spectrum. In this study, we introduce proteomics as a complementary approach for the untargeted identification of human-pathogenic viruses from patient samples. The viral proteomics workflow (vPro-MS) is based on an in-silico derived peptide library covering the human virome in UniProtKB (331 viruses, 20,386 genomes, 121,977 peptides), which was especially designed for diagnostic purposes. A scoring algorithm (vProID score) was developed to assess the confidence of virus identification from proteomics data. In combination with high-throughput diaPASEF-based data acquisition, this workflow enables the analysis of up to 60 samples per day. The specificity was determined to be > 99,9 % in an analysis of 221 plasma, swab and cell culture samples covering 18 different viruses (e.g. SARS, MERS, EBOV, MPXV). The sensitivity of this approach for the detection of SARS-CoV-2 in nasopharyngeal swabs corresponds to a PCR cycle threshold of 27 with comparable quantitative accuracy to metagenomics. vPro-MS enables the integration of untargeted virus identification in large-scale proteomic studies of biofluids such as human plasma to detect previously undiscovered virus infections in patient specimens.
2025-08-04 | PXD055135 | Pride
Project description:Human cytomegalovirus by clinical metagenomics
| PRJNA886483 | ENA
Project description:Clinical metagenomics analysis of wound swab samples
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:Microbiome ecoregion model is a Named Entity Recognition (NER) model that identifies and annotates the ecoregion of microbiome samples in texts. This is the final model version used to annotate metagenomics publications in Europe PMC and enrich metagenomics studies in MGnify with ecoregion metadata from literature. For more information, please refer to the following blogs: http://blog.europepmc.org/2020/11/europe-pmc-publications-metagenomics-annotations.html https://www.ebi.ac.uk/about/news/service-news/enriched-metadata-fields-mgnify-based-text-mining-associated-publications
Project description:Microbiome site model is a Named Entity Recognition (NER) model that identifies and annotates the site of microbiome samples in texts. This is the final model version used to annotate metagenomics publications in Europe PMC and enrich metagenomics studies in MGnify with site metadata from literature. For more information, please refer to the following blogs: http://blog.europepmc.org/2020/11/europe-pmc-publications-metagenomics-annotations.html https://www.ebi.ac.uk/about/news/service-news/enriched-metadata-fields-mgnify-based-text-mining-associated-publications