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Similarity pattern analysis in mutational distributions.
N N Khromov-Borisov1, I B Rogozin, J A Pêgas Henriques
1Institute of Cytology and Genetics, Russian Academy of Sciences, Novosibirsk, Russia. nikita@dna.cbiot.ufrgs.br
Mutation Research
|December 11, 1999
Summary
Similarity Pattern Analysis (SPAN) effectively analyzes mutational distributions by clustering mutagens and genetic profiles. This statistical method reveals relationships between mutagens and their induced mutations, aiding in understanding mutagenicity.
Area of Science:
- Genetics
- Statistical Analysis
- Molecular Biology
Background:
- Mutational distributions (MDs) require robust analytical methods.
- Understanding mutagenic mechanisms relies on comparing mutation profiles across different agents and genetic contexts.
Purpose of the Study:
- To demonstrate the validity and applicability of Similarity Pattern Analysis (SPAN) for studying mutational distributions.
- To analyze mutational spectra (MS) from Escherichia coli and Neurospora crassa using SPAN.
Main Methods:
- Applied SPAN to analyze mutational spectra data as large two-way contingency tables.
- Utilized Kastenbaum-Hirotsu squared distance (KHi(2)) to measure profile similarity.
- Developed the COLLAPSE computer program to facilitate profile clustering into 'collapsets'.
Main Results:
- SPAN successfully identified similarity patterns in both Escherichia coli (lacI gene) and Neurospora crassa (ad-3 region) datasets.
- Five distinct clusters (collapsets) of mutagens were identified in the Neurospora crassa data based on their mutational profiles.
- Confirmed dose-dependency of mutational distributions for X-ray-induced mutations and identified similarities between mutagens with comparable modes of action.
Conclusions:
- SPAN is a powerful and interpretable methodology for analyzing mutational spectra.
- The revealed similarity patterns provide insights into mutagen mechanisms and their effects on DNA.
- SPAN, when combined with descriptive cluster analysis, offers a fruitful approach for MS analysis.