https://socictopen.socict.org/files/original/3e4ba02223b46ef4107ff1e9ccab43b0.pdf d44713bee6a6c2337364c87cd1bdb9db 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 MiCoP: microbial community profiling method for detecting viral and fungal organisms in metagenomic samples Creator An entity primarily responsible for making the resource Nathan LaPierre, Serghei Mangul, Mohammed Alser, Igor Mandric, Nicholas C. Wu, David Koslicki, Eleazar Eskin Description An account of the resource Abstract Background High throughput sequencing has spurred the development of metagenomics, which involves the direct analysis of microbial communities in various environments such as soil, ocean water, and the human body. Many existing methods based on marker genes or k-mers have limited sensitivity or are too computationally demanding for many users. Additionally, most work in metagenomics has focused on bacteria and archaea, neglecting to study other key microbes such as viruses and eukaryotes. Results Here we present a method, MiCoP (Microbiome Community Profiling), that uses fast-mapping of reads to build a comprehensive reference database of full genomes from viruses and eukaryotes to achieve maximum read usage and enable the analysis of the virome and eukaryome in each sample. We demonstrate that mapping of metagenomic reads is feasible for the smaller viral and eukaryotic reference databases. We show that our method is accurate on simulated and mock community data and identifies many more viral and fungal species than previously-reported results on real data from the Human Microbiome Project. Conclusions MiCoP is a mapping-based method that proves more effective than existing methods at abundance profiling of viruses and eukaryotes in metagenomic samples. MiCoP can be used to detect the full diversity of these communities. The code, data, and documentation are publicly available on GitHub at: https://github.com/smangul1/MiCoP. 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, virome, eukaryome, Abundance estimation, Community profiling, alignment Identifier An unambiguous reference to the resource within a given context DOI: 10.1186/s12864-019-5699-9 Source A related resource from which the described resource is derived BMC Genomics 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 Genetics, Biotechnology Language A language of the resource EN