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Correspondence discriminant analysis: a multivariate method for comparing classes of protein and nucleic acid
G Perrière1, J R Lobry, J Thioulouse
1Laboratoire de Biométrie, Génétique et Biologie des Populations, UMR CNRS n 5558, Université Claude Bernard, Lyon, Villeurbanne, France. perriere.lobry.thioulou@biomserv.univ-lyonl.fr
Computer Applications in the Biosciences : CABIOS
|December 1, 1996
Summary
Correspondence discriminant analysis (CDA) effectively predicts bacterial protein locations and distinguishes DNA strands. This multivariate method offers high resolution for sequence analysis in bioinformatics.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Studying classes of nucleotide or protein sequences is crucial for understanding biological functions.
- Predicting protein subcellular localization and analyzing DNA strand characteristics are key challenges in molecular biology.
Purpose of the Study:
- To demonstrate the utility of Correspondence Discriminant Analysis (CDA) for sequence analysis.
- To apply CDA for predicting the subcellular location of Escherichia coli proteins.
- To utilize CDA for discriminating coding sequences of leading and lagging DNA strands in bacteria.
Main Methods:
- Correspondence Discriminant Analysis (CDA), a multivariate statistical method.
- Application of CDA to protein sequences of *Escherichia coli* for subcellular localization.
- Application of CDA to coding sequences of leading and lagging DNA strands in *Mycoplasma genitalium*, *Haemophilus influenzae*, *E. coli*, and *Bacillus subtilis*.
Main Results:
- CDA successfully discriminated *E. coli* proteins based on subcellular location (membrane, cytoplasm, periplasm).
- The method achieved high resolution, enabling prediction of unknown subcellular locations for *E. coli* proteins.
- CDA effectively discriminated between coding sequences of leading and lagging strands in the four bacterial species studied.
Conclusions:
- Correspondence Discriminant Analysis (CDA) is a powerful multivariate method for analyzing biological sequences.
- CDA can accurately predict protein subcellular localization and differentiate DNA strand origins.
- The computational tools for CDA are publicly available, facilitating broader application in bioinformatics research.