Characterizing the D2 Statistic: Word Matches in Biological Sequences

Word matches are often used in sequence comparison methods, either as a measure of sequence similarity or in the first search steps of algorithms such as BLAST or BLAT. The D2 statistic is the number of matches of words of k letters between two sequences. Recent advances have been made in the characterization of this statistic and in the approximation of its distribution. Here, these results are extended to the case of approximate word matches. We compute the exact value of the variance of the D2 statistic for the case of a uniform letter distribution, and introduce a method to provide accurate approximations of the variance in the remaining cases. This enables the distribution of D2 to be approximated for typical situations arising in biological research. We apply these results to the identification of cis-regulatory modules, and show that this method detects such sequences with a high accuracy. The ability to approximate the distribution of D2 for both exact and approximate word matches will enable the use of this statistic in a more precise manner for sequence comparison, database searches, and identification of transcription factor binding sites.

[1]  Takuji Nishimura,et al.  Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator , 1998, TOMC.

[2]  Andrew D. Barbour,et al.  Compound Poisson approximation: a user's guide , 2001 .

[3]  Sylvain Forêt,et al.  Empirical distribution of k , 2009, Pattern Recognit..

[4]  D. Davison,et al.  d2_cluster: a validated method for clustering EST and full-length cDNAsequences. , 1999, Genome research.

[5]  M S Waterman,et al.  Identification of common molecular subsequences. , 1981, Journal of molecular biology.

[6]  Zsuzsanna Lipták,et al.  An overview of the wcd EST clustering tool , 2008, Bioinform..

[7]  Susan R. Wilson,et al.  Approximate word matches between two random sequences , 2008 .

[8]  M. F. Fuller,et al.  Practical Nonparametric Statistics; Nonparametric Statistical Inference , 1973 .

[9]  Sylvain Forêt,et al.  Asymptotic behaviour and optimal word size for exact and approximate word matches between random sequences , 2006, BMC Bioinformatics.

[10]  Jonas S. Almeida,et al.  Alignment-free sequence comparison-a review , 2003, Bioinform..

[11]  M. Waterman,et al.  Distributional regimes for the number of k-word matches between two random sequences , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Winston Hide,et al.  Biological Evaluation of d2, an Algorithm for High-Performance Sequence Comparison , 1994, J. Comput. Biol..

[13]  Saurabh Sinha,et al.  A statistical method for alignment-free comparison of regulatory sequences , 2007, ISMB/ECCB.

[14]  Robert Miller,et al.  STACK: Sequence Tag Alignment and Consensus Knowledgebase , 2001, Nucleic Acids Res..

[15]  John E. Carpenter,et al.  Assessment of the parallelization approach of d2_cluster for high‐performance sequence clustering , 2002, J. Comput. Chem..

[16]  W. J. Kent,et al.  BLAT--the BLAST-like alignment tool. , 2002, Genome research.

[17]  M. Kimura A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences , 1980, Journal of Molecular Evolution.

[18]  Arcady R. Mushegian,et al.  Distribution of words with a predefined range of mismatches to a DNA probe in bacterial genomes , 2004, Bioinform..

[19]  M. Ragan,et al.  Is Multiple-Sequence Alignment Required for Accurate Inference of Phylogeny? , 2007, Systematic biology.

[20]  E. J. Gumbel,et al.  Statistics of Extremes. , 1960 .

[21]  W. Pearson Rapid and sensitive sequence comparison with FASTP and FASTA. , 1990, Methods in enzymology.

[22]  Christoforos Nikolaou,et al.  “Word” Preference in the Genomic Text and Genome Evolution: Different Modes of n-tuplet Usage in Coding and Noncoding Sequences , 2005, Journal of Molecular Evolution.

[23]  Tiee-Jian Wu,et al.  Optimal word sizes for dissimilarity measures and estimation of the degree of dissimilarity between DNA sequences , 2005, Bioinform..

[24]  Craig A. Stewart,et al.  Introduction to computational biology , 2005 .

[25]  C. J. Burden,et al.  Asymptotic Behavior of k-Word Matches Between two Uniformly Distributed Sequences , 2007, Journal of Applied Probability.

[26]  G. Rubin,et al.  A computer program for aligning a cDNA sequence with a genomic DNA sequence. , 1998, Genome research.

[27]  Lukas Wagner,et al.  A Greedy Algorithm for Aligning DNA Sequences , 2000, J. Comput. Biol..