https://www.socictopen.socict.org/files/original/d89fc02a4a039eabb7e029c09a1da56d.pdf e7f38827e5c78fbe093f5a6dcaff0759 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 K-mer Content, Correlation, and Position Analysis of Genome DNA Sequences for the Identification of Function and Evolutionary Features Creator An entity primarily responsible for making the resource Aaron Sievers, Katharina Bosiek, Marc Bisch, Chris Dreessen, Jascha Riedel, Patrick Froß, Michael Hausmann, Georg Hildenbrand Description An account of the resource In genome analysis, k-mer-based comparison methods have become standard tools. However, even though they are able to deliver reliable results, other algorithms seem to work better in some cases. To improve k-mer-based DNA sequence analysis and comparison, we successfully checked whether adding positional resolution is beneficial for finding and/or comparing interesting organizational structures. A simple but efficient algorithm for extracting and saving local k-mer spectra (frequency distribution of k-mers) was developed and used. The results were analyzed by including positional information based on visualizations as genomic maps and by applying basic vector correlation methods. This analysis was concentrated on small word lengths (1 ≤ k ≤ 4) on relatively small viral genomes of Papillomaviridae and Herpesviridae, while also checking its usability for larger sequences, namely human chromosome 2 and the homologous chromosomes (2A, 2B) of a chimpanzee. Using this alignment-free analysis, several regions with specific characteristics in Papillomaviridae and Herpesviridae formerly identified by independent, mostly alignment-based methods, were confirmed. Correlations between the k-mer content and several genes in these genomes have been found, showing similarities between classified and unclassified viruses, which may be potentially useful for further taxonomic research. Furthermore, unknown k-mer correlations in the genomes of Human Herpesviruses (HHVs), which are probably of major biological function, are found and described. Using the chromosomes of a chimpanzee and human that are currently known, identities between the species on every analyzed chromosome were reproduced. This demonstrates the feasibility of our approach for large data sets of complex genomes. Based on these results, we suggest k-mer analysis with positional resolution as a method for closing a gap between the effectiveness of alignment-based methods (like NCBI BLAST) and the high pace of standard k-mer analysis. Date A point or period of time associated with an event in the lifecycle of the resource 2017 Subject The topic of the resource k-mer, k-mer analysis, Sequence analysis, alignment-free, positional features Identifier An unambiguous reference to the resource within a given context DOI: 10.3390/genes8040122 Source A related resource from which the described resource is derived Genes Publisher An entity responsible for making the resource available MDPI AG 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 Language A language of the resource EN