https://www.socictopen.socict.org/files/original/d141bd0e45546a30d4b26daed43332e1.pdf 1fa5d0a51b60c912fc9ab9a6ba5fd328 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 CircMarker: a fast and accurate algorithm for circular RNA detection Creator An entity primarily responsible for making the resource Xin Li, Chong Chu, Jingwen Pei, Ion Mandoiu, Yu-Feng Wu Description An account of the resource Abstract Background While RNA is often created from linear splicing during transcription, recent studies have found that non-canonical splicing sometimes occurs. Non-canonical splicing joins 3’ and 5’ and forms the so-called circular RNA. It is now believed that circular RNA plays important biological roles such as affecting susceptibility of some diseases. During the past several years, multiple experimental methods have been developed to enrich circular RNA while degrade linear RNA. Although several useful software tools for circular RNA detection have been developed as well, these tools are based on reads mapping may miss many circular RNA. Also, existing tools are slow for large data due to their dependence on reads mapping. Method In this paper, we present a new computational approach, named CircMarker, based on k-mers rather than reads mapping for circular RNA detection. CircMarker takes advantage of transcriptome annotation files to create the k-mer table for circular RNA detection. Results Empirical results show that CircMarker outperforms existing tools in circular RNA detection on accuracy and efficiency in many simulated and real datasets. Conclusions We develop a new circular RNA detection method called CircMarker based on k-mer analysis. Our results on both simulation data and real data demonstrate that CircMarker runs much faster and can find more circular RNA with higher consensus-based sensitivity and high accuracy ratio compared with existing tools. Date A point or period of time associated with an event in the lifecycle of the resource 2018 Subject The topic of the resource circular RNA, high-throughput sequencing, Genomics, RNA-Seq Identifier An unambiguous reference to the resource within a given context DOI: 10.1186/s12864-018-4926-0 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