https://socictopen.socict.org/files/original/63e34a074f8b5b28e9c11b6414d25762.pdf 95ce12b9be92730593cb6e11d7db19d8 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource MetaCon: unsupervised clustering of metagenomic contigs with probabilistic k-mers statistics and coverage Creator An entity primarily responsible for making the resource JIA QIAN, Matteo Comin Description An account of the resource Abstract Motivation Sequencing technologies allow the sequencing of microbial communities directly from the environment without prior culturing. Because assembly typically produces only genome fragments, also known as contigs, it is crucial to group them into putative species for further taxonomic profiling and down-streaming functional analysis. Taxonomic analysis of microbial communities requires contig clustering, a process referred to as binning, that is still one of the most challenging tasks when analyzing metagenomic data. The major problems are the lack of taxonomically related genomes in existing reference databases, the uneven abundance ratio of species, sequencing errors, and the limitations due to binning contig of different lengths. Results In this context we present MetaCon a novel tool for unsupervised metagenomic contig binning based on probabilistic k-mers statistics and coverage. MetaCon uses a signature based on k-mers statistics that accounts for the different probability of appearance of a k-mer in different species, also contigs of different length are clustered in two separate phases. The effectiveness of MetaCon is demonstrated in both simulated and real datasets in comparison with state-of-art binning approaches such as CONCOCT, MaxBin and MetaBAT. Date A point or period of time associated with an event in the lifecycle of the resource 2019 Subject The topic of the resource metagenomics, Unsupervised clustering, K-mers statistics Identifier An unambiguous reference to the resource within a given context DOI: 10.1186/s12859-019-2904-4 Source A related resource from which the described resource is derived BMC Bioinformatics Publisher An entity responsible for making the resource available BMC Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Biology (General), Computer applications to medicine. Medical informatics Language A language of the resource EN