https://socictopen.socict.org/files/original/e5f46aa5f1f33d61b82ff3e541ca1cf4.pdf d3924f93ca6a35aacbb9c08b61c00a51 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 Efficient Mining of Variants From Trios for Ventricular Septal Defect Association Study Creator An entity primarily responsible for making the resource Peng Jiang, Yaofei Hu, Yiqi Wang, JIN ZHANG, Qinghong Zhu, Lin Bai, Qiang Tong, Tao Li, Liang Zhao Description An account of the resource Ventricular septal defect (VSD) is a fatal congenital heart disease showing severe consequence in affected infants. Early diagnosis plays an important role, particularly through genetic variants. Existing panel-based approaches of variants mining suffer from shortage of large panels, costly sequencing, and missing rare variants. Although a trio-based method alleviates these limitations to some extent, it is agnostic to novel mutations and computational intensive. Considering these limitations, we are studying a novel variants mining algorithm from trio-based sequencing data and apply it on a VSD trio to identify associated mutations. Our approach starts with irrelevant k-mer filtering from sequences of a trio via a newly conceived coupled Bloom Filter, then corrects sequencing errors by using a statistical approach and extends kept k-mers into long sequences. These extended sequences are used as input for variants needed. Later, the obtained variants are comprehensively analyzed against existing databases to mine VSD-related mutations. Experiments show that our trio-based algorithm narrows down candidate coding genes and lncRNAs by about 10- and 5-folds comparing with single sequence-based approaches, respectively. Meanwhile, our algorithm is 10 times faster and 2 magnitudes memory-frugal compared with existing state-of-the-art approach. By applying our approach to a VSD trio, we fish out an unreported gene—CD80, a combination of two genes—MYBPC3 and TRDN and a lncRNA—NONHSAT096266.2, which are highly likely to be VSD-related. Date A point or period of time associated with an event in the lifecycle of the resource 2019 Subject The topic of the resource trio-sequencing, k-mer filtering, Variant calling, Ventricular septal defect, Association study, long non-coding RNA Identifier An unambiguous reference to the resource within a given context DOI: 10.3389/fgene.2019.00670 Source A related resource from which the described resource is derived Frontiers in Genetics Publisher An entity responsible for making the resource available Frontiers Media S.A. 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