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Related Experiment Videos

Recent enhancements to the Blocks Database servers

J G Henikoff1, S Pietrokovski, S Henikoff

  • 1Fred Hutchinson Cancer Research Center, 1124 Columbia Street, Seattle, WA 98104, USA.

Nucleic Acids Research
|January 1, 1997
PubMed
Summary
This summary is machine-generated.

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The Blocks Database offers enhanced tools for protein sequence classification. New features improve scoring matrices, integrate fingerprint databases, and introduce advanced search methods like LAMA for better protein family analysis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Protein families share conserved regions crucial for function.
  • Databases of these conserved regions aid in sequence classification.
  • Existing search methods require continuous improvement for accuracy and scope.

Purpose of the Study:

  • To detail recent enhancements to the Blocks Database servers.
  • To introduce new functionalities for protein and nucleotide sequence classification.
  • To improve the utility and search capabilities of the Blocks Database.

Main Methods:

  • Utilizing multiple alignments of conserved protein regions.
  • Implementing improved position-specific scoring matrices.
  • Integrating the Prints protein fingerprint database.

Related Experiment Videos

  • Developing the Local Alignment of Multiple Alignments (LAMA) method.

    • Main Results:

    • Enhanced calculation of position-specific scoring matrices.
    • Availability of the Prints database in Blocks format.
    • Introduction of a representative sequence biased towards protein family blocks.
    • Generation of trees from protein family blocks.
    • Inclusion of links to related World Wide Web pages.
    • Successful implementation of the LAMA method for block database searching.

    Conclusions:

    • The enhanced Blocks Database servers provide more powerful tools for sequence analysis.
    • New features facilitate more accurate protein family classification and relationship discovery.
    • The LAMA method offers a novel approach for searching conserved blocks within large datasets.