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: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 host model is a Named Entity Recognition (NER) model that identifies and annotates the host 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 host 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