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A Practical Guide to Phylogenetics for Nonexperts
Published on: February 5, 2014
Alignment-free sequence comparison for biologically realistic sequences of moderate length
Conrad J Burden1, Junmei Jing, Susan R Wilson
1Australian National University.
Summary
The D(2) statistic and its variants D(2)* and D(2)c offer fast, alignment-free methods for assessing biological sequence similarity. These methods effectively identify cis-regulatory modules, with D(2) and D(2)c showing strong performance.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- The D(2) statistic is a computationally efficient, alignment-free method for measuring biological sequence similarity.
- Concerns exist regarding the D(2) statistic's suitability due to potential noise dominance from individual sequences.
- Mean-centered variants, D(2)* and D(2)c, were developed to address these concerns.
Purpose of the Study:
- To evaluate the suitability of the D(2) statistic for biological sequence similarity analysis.
- To assess the effectiveness of D(2)* and D(2)c in mitigating noise-related variability.
- To determine the utility of these statistics in identifying cis-regulatory modules.
Main Methods:
- Comparison of D(2), D(2)*, and D(2)c statistics.
- Examination of statistical variability and noise impact.
- Performance evaluation in classifying cis-regulatory modules using p-value estimation under a null hypothesis.
Main Results:
- All three statistics (D(2), D(2)*, D(2)c) are potentially useful for sequence similarity.
- Accurate p-values can be estimated for these statistics assuming independent and identically distributed sequence letters.
- D(2) and D(2)c demonstrated strong performance in classifying cis-regulatory modules, with D(2)* showing moderate effectiveness.
Conclusions:
- The D(2) statistic and its variants are valuable tools for biological sequence analysis.
- These alignment-free methods, particularly D(2) and D(2)c, show promise for identifying functional genomic elements like cis-regulatory modules.
- The study validates the utility of these statistics and their p-value estimation under specific hypotheses.
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