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LDDist: a Perl module for calculating LogDet pair-wise distances for protein and nucleotide sequences
1Department of Molecular Evolution, Evolutionary Biology Centre and Linnaeus Centre for Bioinformatics, Uppsala University, Norbyvägen 18C, SE-752 36 Uppsala, Sweden. lddist@artedi.ebc.uu.se
Bioinformatics (Oxford, England)
|February 13, 2004
Summary
LDDist calculates LogDet genetic distances for DNA and protein sequences, accounting for variable rates across sites. This tool aids in evolutionary analysis by estimating evolutionary rates and site variation.
Area of Science:
- Bioinformatics
- Computational Biology
- Evolutionary Genetics
Background:
- Accurate genetic distance estimation is crucial for phylogenetic analysis.
- Sequence data often exhibits variation in evolutionary rates across sites.
- Existing methods may not fully capture complex rate variation patterns.
Purpose of the Study:
- To introduce LDDist, a novel Perl module for calculating LogDet genetic distances.
- To provide a robust method for handling site-to-site rate variation in sequence data.
- To offer flexible options for specifying or estimating rate variation parameters.
Main Methods:
- LDDist is implemented in C++ and accessible via a Perl interface.
- It calculates pair-wise genetic distances for both amino acid and nucleotide sequences.
- Handles rate variation using invariant sites and/or discrete rate categories.
- Rate parameters can be user-defined or estimated using capture-recapture methods.
- Shannon-Wiener index can be used for rate-class assignment without a phylogeny.
Main Results:
- LDDist successfully computes LogDet distances, accommodating site-specific evolutionary rates.
- The module provides accurate estimations of invariant site proportions and rate-class assignments.
- Capture-recapture methods offer reliable parameter estimation.
- Shannon-Wiener index provides a practical approach for rate-class tokenization.
Conclusions:
- LDDist offers a versatile and powerful tool for genetic distance calculations.
- Its ability to model rate variation enhances the accuracy of evolutionary analyses.
- The module is valuable for researchers in bioinformatics and evolutionary genetics.