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Related Experiment Videos

Applications for protein sequence-function evolution data: mRNA/protein expression analysis and coding SNP scoring

Paul D Thomas1, Anish Kejariwal, Nan Guo

  • 1Evolutionary Systems Biology Group, SRI International 333 Ravenswood Ave., Menlo Park CA 94025, USA paul.thomas@sri.com

Nucleic Acids Research
|August 17, 2006
PubMed
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The PANTHER database models protein evolution and function using sequence data. Its web services offer protein classification, expression data analysis, and coding single-nucleotide polymorphism scoring for biological insights.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Large-scale protein sequence data and functional knowledge enable modeling of protein evolution.
  • The PANTHER database aims to model evolutionary sequence-function relationships extensively.

Purpose of the Study:

  • To present web services for analyzing protein sequence and function evolution.
  • To provide tools for protein classification, expression data analysis, and coding single-nucleotide polymorphism scoring.

Main Methods:

  • Protein classification based on amino acid sequences into evolutionary families and subfamilies.
  • Functional annotation of protein families and subfamilies.
  • Analysis of genome-wide expression data using functional classification.

Related Experiment Videos

  • Scoring coding single-nucleotide polymorphisms based on evolutionary protein relationships.

    • Main Results:

    • Implementation of three distinct web services: protein classification, expression data analysis, and coding single-nucleotide polymorphism scoring.
    • Demonstration of how functional classification aids in identifying biological patterns in expression data.
    • Application of evolutionary protein information to predict the impact of single amino acid substitutions.

    Conclusions:

    • The PANTHER web services provide valuable tools for large-scale analysis of protein sequence-function evolution.
    • These services facilitate biological discovery by enabling protein classification, expression data interpretation, and assessment of genetic variation effects.
    • The integrated approach supports diverse applications in bioinformatics and computational biology.