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Inference of horizontal genetic transfer from molecular data: an approach using the bootstrap
1Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110.
Genetics
|July 1, 1992
Summary
Detecting horizontal gene transfer is crucial for understanding evolutionary inconsistencies. A new statistical method identifies significant genetic transfer events, distinguishing them from convergent evolution or rate variations.
Area of Science:
- Genomics
- Evolutionary Biology
- Bioinformatics
Background:
- Inconsistencies in taxonomic relationships can arise from horizontal gene transfer (HGT).
- Existing methods may not detect subtle HGT events or distinguish them from other evolutionary processes.
Purpose of the Study:
- To propose a novel nonparametric statistical method for detecting significant horizontal gene transfer.
- To differentiate HGT from convergent evolution and variations in evolutionary rates.
Main Methods:
- A similarity coefficient is calculated from ranked pairwise sequence identities.
- A distribution of similarity coefficients is generated from resampled data for statistical evaluation.
- Partial data sets are analyzed to identify specific taxa contributing to significance and distinguish HGT.
Main Results:
- The proposed method successfully identified significant inconsistencies in taxonomic relationships attributed to HGT.
- It outperformed existing methods in detecting significant differences in certain datasets.
- The approach can distinguish HGT from convergent evolution and evolutionary rate variations.
Conclusions:
- The new statistical framework provides a robust tool for inferring horizontal gene transfer from nucleic acid sequences.
- This method is also applicable to restriction fragment length polymorphism and protein sequence data.
- It enhances our ability to accurately reconstruct evolutionary histories.