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Efficient secondary database driven annotation using model organism sequences.

Alessandra C Faria-Campos1, Sergio V A Campos, Francisco Prosdocimi

  • 1Departamento de Bioquimica e Imunologia, ICB-UFMG, 31270-010, Brazil.

In Silico Biology
|February 6, 2007
PubMed
Summary
This summary is machine-generated.

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Sequences from model organisms like C. elegans, D. melanogaster, and H. sapiens can reliably annotate Schistosoma mansoni transcripts. This approach offers a high-quality, restricted dataset for functional annotation.

Area of Science:

  • Genomics
  • Bioinformatics
  • Parasitology

Background:

  • Accurate annotation of Schistosoma mansoni sequences is crucial for understanding its biology.
  • Evaluating the utility of model organism databases for non-model organisms is essential for efficient annotation.

Purpose of the Study:

  • To determine if sequences from model organisms (Caenorhabditis elegans, Drosophila melanogaster, Homo sapiens) are sufficient for annotating Schistosoma mansoni sequences.
  • To assess the quality and informativeness of using model organism datasets compared to the comprehensive NCBI nr database.

Main Methods:

  • Performed local BLAST searches of S. mansoni sequences against the NCBI nr database.
  • Computed and compared BLAST hits to sequences from three model organisms versus other organisms in nr.

Related Experiment Videos

  • Registered and analyzed alignment score values for annotation quality assessment.

    • Main Results:

    • A significant fraction of S. mansoni orthologous proteins were found in the selected model organisms.
    • Annotation using model organism datasets yielded results comparable to using the entire nr database.
    • Hits to model organism sequences provided substantial information, similar to broader database searches.

    Conclusions:

    • Model organisms offer a reliable and high-quality reference dataset for Schistosoma mansoni sequence annotation.
    • Using restricted, quality-controlled model organism datasets supports efficient and accurate functional annotation.
    • This strategy validates secondary database-driven annotation approaches for parasitic organisms.