Ten steps to get started in Genome Assembly and Annotation

As a part of the ELIXIR-EXCELERATE efforts in capacity building, we present here 10 steps to facilitate researchers getting started in genome assembly and genome annotation. The guidelines given are broadly applicable, intended to be stable over time, and cover all aspects from start to finish of a...

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Detalles Bibliográficos
Autores: Dominguez Del Angel, Victoria, Hjerde, Erik, Sterck, Lieven, Capella Gutiérrez, Salvador Jesús, 1985-, Notredame, Cedric, Vinnere Pettersson, Olga, Amselem, Joëlle, Bouri, Laurent, Bocs, Stephanie, Klopp, Christophe, Gibrat, Jean-François, Vlasova, Anna, Leskosek, Brane L., Soler, Lucile, Binzer-Panchai, Mahesh, Lantz, Henrik
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2018
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/42860
Acceso en línea:http://hdl.handle.net/10230/42860
http://dx.doi.org/10.12688/f1000research.13598.1
Access Level:acceso abierto
Palabra clave:Genome
Assembly
Annotation
FAIR
NGS
Workflows
DNA
Descripción
Sumario:As a part of the ELIXIR-EXCELERATE efforts in capacity building, we present here 10 steps to facilitate researchers getting started in genome assembly and genome annotation. The guidelines given are broadly applicable, intended to be stable over time, and cover all aspects from start to finish of a general assembly and annotation project. Intrinsic properties of genomes are discussed, as is the importance of using high quality DNA. Different sequencing technologies and generally applicable workflows for genome assembly are also detailed. We cover structural and functional annotation and encourage readers to also annotate transposable elements, something that is often omitted from annotation workflows. The importance of data management is stressed, and we give advice on where to submit data and how to make your results Findable, Accessible, Interoperable, and Reusable (FAIR).