Distribution patterns of over-represented k-mers in non-coding yeast DNA.
BACKGROUND
Over-represented k-mers in genomic DNA regions are often of particular biological interest. For example, over-represented k-mers in co-regulated families of genes are associated with the DNA binding sites of transcription factors. To measure over-representation, we introduce a statistical background model based on single-mismatches, and apply it to the pooled 500 bp ORF Upstream Regions (USRs) of yeast. More importantly, we investigate the context and spatial distribution of over-represented k-mers in yeast USRs.


RESULTS
Single and double-stranded spatial distributions of most over-represented k-mers are highly non-random, and predominantly cluster into a small number of classes that are robust with respect to over-representation measures. Specifically, we show that the three most common distribution patterns can be related to DNA structure, function, and evolution and correspond to: (a) homologous ORF clusters associated with sharply localized distributions; (b) regulatory elements associated with a symmetric broad hill-shaped distribution in the 50-200 bp USR; and (c) runs of As, Ts, and ATs associated with a broad hill-shaped distribution also in the 50-200 bp USR, with extreme structural properties. Analysis of over-representation, homology, localization, and DNA structure are essential components of a general data-mining approach to finding biologically important k-mers in raw genomic DNA and understanding the 'lexicon' of regulatory regions.
Hampson
Steven
S
Department of Information and Computer Science, Institute for Genomics and Bioinformatics, University of California, Irvine, Irvine, CA 92697-3425, USA. hampson@ics.uci.edu
Kibler
Dennis
D
Baldi
Pierre
P
D020816:Amino Acid Motifs / Q000235:genetics*; D001483:Base Sequence; D016000:Cluster Analysis; D017124:Conserved Sequence / Q000235:genetics; D030561:Databases, Nucleic Acid; D005786:Gene Expression Regulation / Q000235:genetics*; D016681:Genome, Fungal; D008957:Models, Genetic; D015233:Models, Statistical; D008969:Molecular Sequence Data; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods* / Q000706:statistics & numerical data; D012689:Sequence Homology, Nucleic Acid; D015003:Yeasts / Q000235:genetics*