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Tools for interpreting large-scale protein profiling in microbiology.

E L Hendrickson1, R J Lamont, M Hackett

  • 1Departments of Chemical Engineering, Universityof Washington, Box 355014, Seattle, WA 98195, USA.

Journal of Dental Research
|October 24, 2008
PubMed
Summary

Analyzing microbial proteomic data is challenging. This study adapts transcriptome tools for analyzing large proteomic datasets, aiding in the interpretation of microbial gene expression and regulatory pathways.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Proteomics

Background:

  • Quantitative proteomic analysis yields large datasets complex for interpretation.
  • Existing data display and gene-clustering tools from transcriptomics can be adapted for proteomic analysis.

Purpose of the Study:

  • To present methods for interpreting large-scale microbial proteomic data.
  • To highlight the utility of adapting transcriptome analysis tools for proteomic data interpretation.

Main Methods:

  • Utilizing abundance ratio vs. total signal/spectral counts plots to identify errors and changes.
  • Mapping protein data to genomic physical order to identify potential operons.
  • Applying classification and clustering algorithms to group proteins by abundance changes.
  • Overlaying differential protein abundance data onto metabolic pathways.
  • Employing ontology tools to analyze altered protein levels in metabolic pathways, molecular functions, and cellular localizations.

Main Results:

  • Adapted transcriptome tools effectively aid in interpreting large proteomic datasets.
  • Genomic ordering reveals potential operons, offering insights into regulatory pathways and corroborating proteomic findings.
  • Pathway and ontology analyses provide biological context for differential protein abundance, though database curation impacts effectiveness.

Conclusions:

  • Transcriptome data analysis tools are valuable for interpreting microbial proteomic datasets.
  • Identifying operons and analyzing metabolic pathways/ontologies enhances understanding of microbial systems.
  • The effectiveness of pathway and ontology analysis is dependent on the completeness of organism-specific databases.