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Related Experiment Videos

DBD: a transcription factor prediction database.

Sarah K Kummerfeld1, Sarah A Teichmann

  • 1MRC Laboratory of Molecular Biology, Hills Road, Cambridge CB2 2QH, UK. skk@mrc-lmb.cam.ac.uk

Nucleic Acids Research
|December 31, 2005
PubMed
Summary
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The DBD database identifies novel transcription factors across genomes using hidden Markov models. This resource reveals previously uncharacterized proteins, advancing our understanding of gene regulation.

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Gene expression regulation is vital for all biological processes.
  • Sequence-specific DNA-binding transcription factors are key regulators.
  • Current methods for identifying transcription factors are limited.

Purpose of the Study:

  • To develop a novel method for genome-wide transcription factor identification.
  • To create the DBD database for predicting transcription factors across diverse organisms.
  • To identify previously uncharacterized transcription factors.

Main Methods:

  • Utilized profile hidden Markov models (HMMs) of protein domains for homology-based prediction.
  • Curated a list of DNA-binding domain HMMs from Pfam and SUPERFAMILY databases.

Related Experiment Videos

  • Developed a prediction pipeline with 95-99% accuracy compared to experimental data.

    • Main Results:

    • The DBD database provides genome-wide transcription factor predictions for 150 genomes.
    • Between 25% and 50% of predicted transcription factors were previously uncharacterized.
    • The database includes domain assignments and curated HMMs.

    Conclusions:

    • The DBD database significantly expands the known repertoire of transcription factors.
    • This resource facilitates the study of gene regulation across the tree of life.
    • Identified novel proteins crucial for understanding biological processes.