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Related Experiment Videos

Massive sequence comparisons as a help in annotating genomic sequences.

A Louis1, E Ollivier, J C Aude

  • 1Laboratoire Génome et Informatique, Université de Versailles, 78035 Versailles Cedex, France. louis@genetique.uvsq.fr

Genome Research
|July 4, 2001
PubMed
Summary
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This study compared plant protein sequences, identifying potential errors in 10% of clusters. The automated analysis aids in detecting inconsistencies in protein sequence data.

Area of Science:

  • Genomics
  • Bioinformatics
  • Plant Science

Background:

  • Publicly available plant protein sequences are crucial for evolutionary and functional studies.
  • Identifying and correcting errors in sequence data is essential for accurate biological interpretation.

Purpose of the Study:

  • To perform an all-by-all comparison of plant protein sequences.
  • To develop and apply an automated method for cluster analysis, domain delineation, and error detection.

Main Methods:

  • All-by-all comparison of publicly available plant protein sequences.
  • Clusterization into 1064 groups of orthologous and paralogous sequences.
  • Automated pyramidal classification and domain delineation within clusters.
  • DNA sequence alignment to identify probable sequencing or annotation errors.

Related Experiment Videos

Main Results:

  • 1064 clusters of plant protein sequences were generated.
  • An automated procedure identified inconsistencies and potential errors in 10% of clusters.
  • Errors included probable sequencing frameshifts and incorrect intron/exon predictions.

Conclusions:

  • The automated analysis effectively detects inconsistencies in protein sequence data.
  • This method aids in improving the quality of plant protein sequence databases.
  • Findings highlight the importance of rigorous data validation in bioinformatics.