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Sensommatic: an efficient pipeline to mine and predict sensory receptor genes in the era of reference-quality genomes.
Ryan, Louise; Lawless, Colleen; Hughes, Graham M.
Afiliación
  • Ryan L; School of Biology and Environmental Science, University College Dublin, Belfield, Dublin 4, Ireland.
  • Lawless C; School of Biology and Environmental Science, University College Dublin, Belfield, Dublin 4, Ireland.
  • Hughes GM; School of Biology and Environmental Science, University College Dublin, Belfield, Dublin 4, Ireland.
Bioinformatics ; 40(1)2024 01 02.
Article en En | MEDLINE | ID: mdl-38261648
ABSTRACT

SUMMARY:

Sensory receptor gene families have undergone extensive expansion and loss across vertebrate evolution, leading to significant variation in receptor counts between species. However, due to their species-specific nature, conventional reference-based annotation tools often underestimate the true number of sensory receptors in a given species. While there has been an exponential increase in the taxonomic diversity of publicly available genome assemblies in recent years, only ∼30% of vertebrate species on the NCBI database are currently annotated. To overcome these limitations, we developed 'Sensommatic', an automated and accessible sensory receptor annotation pipeline. Sensommatic implements BLAST and AUGUSTUS to mine and predict sensory receptor genes from whole genome assemblies, adopting a one-to-many gene mapping approach. While designed for vertebrates, Sensommatic can be extended to run on non-vertebrate species by generating customized reference files, making it a scalable and generalizable tool. AVAILABILITY AND IMPLEMENTATION Source code and associated files are available at https//github.com/GMHughes/Sensommatic.
Asunto(s)

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Programas Informáticos / Genoma Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Animals Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Irlanda

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Programas Informáticos / Genoma Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Animals Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Irlanda