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Alignment-free sequence comparison (II): theoretical power of comparison statistics
Lin Wan1, Gesine Reinert, Fengzhu Sun
1Molecular and Computational Biology, University of Southern California , Los Angeles, California 90089-2910, USA.
Summary
This study evaluates D2, D2*, and D2S statistics for alignment-free sequence comparison. D2* is recommended for detecting shared motifs, showing high power in simulations for motif discovery.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Alignment-free sequence comparison methods are crucial for large-scale sequence analysis.
- The D2 statistic and its variants (D2*, D2S) offer rapid sequence comparison by counting k-tuples.
Purpose of the Study:
- To theoretically and numerically assess the power of D2, D2*, and D2S statistics for sequence comparison.
- To identify the most effective statistic for different sequence comparison scenarios, including motif detection and pattern transfer.
Main Methods:
- Theoretical analysis using limit distributions under null and alternative hidden Markov models.
- Numerical simulations with varying sequence lengths and motif occurrences.
- Application to a dataset of 323 transcription factor binding motifs from JASPAR CORE.
Main Results:
- Asymptotically, D2S generally shows the highest power, followed by D2*, while D2 can have zero power.
- In simulations, D2* often demonstrates the highest power, especially for detecting shared motifs, approaching 100% power with sufficient motifs.
- For pattern transfer models, none of the statistics showed increased power with longer sequences.
Conclusions:
- D2* is recommended for practical applications involving the detection of shared motifs due to its high power in simulations.
- The effectiveness of D2, D2*, and D2S varies depending on the underlying sequence model.
- A program for calculating the power of these statistics is available for download.
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