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Published on: February 5, 2014
Multiple alignment-free sequence comparison
Jie Ren1, Kai Song, Fengzhu Sun
1School of Mathematics, Peking University, Beijing 100871, PR China, Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089-2910, USA, MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, PR China and Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.
Bioinformatics (Oxford, England)
|August 31, 2013
Summary
This study introduces new alignment-free statistics for comparing multiple DNA sequences simultaneously. These methods help identify similar sequences and measure similarity within datasets, with an R package available for use.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Alignment-free sequence comparison methods using k-tuple content have advanced.
- Existing methods are limited to pairwise comparisons.
Purpose of the Study:
- Extend alignment-free statistics to compare multiple sequences simultaneously.
- Develop methods for identifying similar sequences within a set.
- Quantify similarity within a collection of sequences.
Main Methods:
- Introduced five new statistics: C(*)1, C(S)1, C(*)2, C(S)2, and C(geo)2.
- These statistics extend pairwise k-tuple content analysis to multiple sequences.
- Evaluated statistics using simulated data and cis-regulatory module data.
Main Results:
- All statistics performed similarly on real cis-regulatory module data.
- Star-type statistics outperformed Shepp-type statistics on simulated data.
- Multiple alignment-free statistics are more sensitive to data contamination than pairwise averages.
Conclusions:
- The developed statistics provide novel tools for multi-sequence comparison.
- The choice of statistic may depend on data characteristics (simulated vs. real).
- An R package 'multiAlignFree' is available for implementation.
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