AniProtDB

AniProtDB provides de novo-assembled proteomes and predicted protein and protein-domain annotations derived from reads in the NCBI Sequence Read Archive for 100 species spanning 21 animal phyla to support comparative genomics, functional annotation, and evolutionary biology.


Key Features:

  • Comprehensive Proteome Collection: De novo-assembled metazoan proteomes covering 100 species across 21 animal phyla.
  • Source Data and Assembly Pipeline: Proteomes generated from publicly available sequence data in the NCBI Sequence Read Archive using a de novo assembly pipeline.
  • Predicted Proteins and Protein Domains: Per-taxon predicted protein sets with associated protein-domain annotations.
  • Sequence Similarity Searches: Capability to perform sequence similarity searches across all assembled proteomes.
  • Taxonomic Breadth: Inclusion of proteomes from a wide range of non-model and traditionally understudied animal phyla.

Scientific Applications:

  • Comparative Genomics: Identification of homologous proteins across species for cross-taxon comparisons.
  • Functional Annotation: Use of predicted proteins and domain annotations to assign putative functions to protein sequences.
  • Evolutionary Biology: Analysis of protein diversity and domain architecture evolution across 21 animal phyla.
  • Biomedical Research: Exploration of species-specific or conserved proteins from non-traditional model organisms relevant to biomedical questions.

Methodology:

De novo assembly of reads from the NCBI Sequence Read Archive to produce proteomes for 100 species (21 phyla), followed by protein prediction and protein-domain annotation, with support for sequence similarity searches across assembled proteomes.

Topics

Details

Tool Type:
web application
Added:
1/18/2021
Last Updated:
1/23/2021

Operations

Publications

Barreira SN, Nguyen A, Fredriksen MT, Wolfsberg TG, Moreland RT, Baxevanis AD. AniProtDB: A Collection of Uniformly Generated Metazoan Proteomes for Comparative Genomics Studies. Unknown Journal. 2020. doi:10.1101/2020.10.17.342964.