Ensembl Genomes


NAR Molecular Biology Database Collection entry number 119
Kersey, Paul; Allen, James; Allot, Alexis; Barba, Matthieu; Boddu, Sanjay; Bolt, Bruce; Carvalho-Silva, Denise; Christensen, Mikkel; Davis, Paul; Grabmueller, Christoph; Kumar, Navin; Liu, Zicheng; Maurel, Thomas; Moore, Benjamin; McDowall, Mark; Maheswari, Uma; Naamati, Guy; Newman, Victoria; Ong, ChuangKee; Paulini, Michael; Pedro, Helder; Perry, Emily,; Russell, Matthew; Sparrow, Helen; Tapanari, Electra; Taylor, Kieron; Vullo, Alessandro; Williams, Gareth; Zadissa, Amonida; Olson, Andrew J; Stein, Joshua; Wei, Sharon; Tello-Ruiz, Marcela; Ware, Doreen; Luciani, Aurelien; Potter, Simon; Finn, Robert; Urban, Martin; Hammond-Kosack, Kim; Bolser, Daniel; De Silva, Nishadi; Howe, Kevin; Langridge, Nicholas; Maslen, Gareth; Staines, Daniel; Yates, Andrew

Database Description

Ensembl Genomes (http://www.ensemblgenomes.org) is an integrating resource for genome-scale data from non-vertebrate species, complementing the resources for vertebrate genomics developed in the Ensembl project (http://www.ensembl.org). Together, the two resources provide a consistent set of programmatic and interactive interfaces to a rich range of data including genome sequence, gene models, transcript sequence, genetic variation, and comparative analysis. This paper provides an update to the previous publications about the resource, with a focus on recent developments and expansions. These include the incorporation of almost 20 000 additional genome sequences and over 35 000 tracks of RNA-Seq data, which have been aligned to genomic sequence and made available for visualization. Other advances since 2015 include the release of the database in Resource Description Framework (RDF) format, a large increase in community-derived curation, a new high-performance protein sequence search, additional cross-references, improved annotation of non-protein-coding genes, and the launch of pre-release and archival sites. Collectively, these changes are part of a continuing response to the increasing quantity of publicly-available genome-scale data, and the consequent need to archive, integrate, annotate and disseminate these using automated, scalable methods.


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