Gene Loss DB: A curated database for gene loss in vertebrate species.
Themudo, G. E.; Ruivo, R.; Valente, R.; Artilheiro, N.; Oliveira, D.; Amorim, I.; Pinto, B.; Castro, L. F. C.; Fernandes, S.; Lopes-Marques, M.
Show abstract
Molecular databases are essential resources for both experimental and computational biologists. The rapid increase in high-quality genome assemblies has led to a surge in publications describing secondary gene loss events associated with lineage-specific adaptations across diverse vertebrate groups. This growing volume of information underscores the urgent need for organized, searchable, and curated resources that facilitate data discovery, allow detection of broad evolutionary patterns, and support downstream analyses. Currently, no existing database compiles manually curated and validated information on published secondary gene loss events. Here, we introduce the Gene Loss Database (GLossDB), a platform designed to centralize and present this data in an easy-to-search and user-friendly format (https://geneloss.org/). GLossDB compiles gene loss events alongside supporting evidence, including the inferred mechanism of gene loss (exon deletion, gene deletion, loss of function mutation), the type of data used to support inactivation (genomic, transcriptomic, single/multiple individual sequence reads, synteny maps) and, when available, whether the event is shared across all lineages within a taxon. Each entry also includes a short excerpt from the original publication to provide context. This information is structured in the database to be searchable by species, gene, taxa, or by GO-terms linked to the gene in question. The initial release of GLossDB focuses on cetaceans, a lineage with numerous gene loss events linked to aquatic adaptations. This first collection comprises 1866 gene loss events identified across 57 cetacean species. In addition, the database includes 1321 gene loss events from other taxa, which were also reported in the same studies and collected simultaneously.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Unveiling the Microbial Realm with VEBA 2.0: A modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic, and viral multi-omics from either short- or long-read sequencing 93%
- FEVER: An interactive web-based resource for evolutionary transcriptomics across fishes 92%
- HRT Atlas v1.1 database: redefining human and mouse housekeeping genes and candidate reference transcripts by mining massive RNA-seq datasets 92%
Similar papers in this journal
- Active Notch Signaling is Required for Arm Regeneration in a Brittle Star 94%
- A unique single nucleotide polymorphism in Agouti Signalling Protein (ASIP) gene changes coat colour of Sri Lankan Leopard (Panthera pardus kotiya) to dark black 93%
- Transfer of Knowledge from Model Organisms to Evolutionarily Distant Non-Model Organisms: The Coral Pocillopora damicornis Membrane Signaling Receptome 93%
Similar papers in this journal
Similar papers in this journal
- FLYNC: A Machine Learning-Driven Framework for Discovering Long Non-Coding RNAs in Drosophila melanogaster 93%
- Cluefish: mining the dark matter of transcriptional data series with over-representation analysis enhanced by aggregated biological prior knowledge 93%
- GEGA (Gallus Enriched Gene Annotation): an online tool providing genomics and functional information across 47 tissues for a chicken gene-enriched atlas gathering Ensembl & Refseq genome annotations 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.