Comparative Analysis of DNA Word Abundances in Four Yeast Genomes Using a Novel Statistical Background Model
Identifieur interne : 002034 ( Main/Exploration ); précédent : 002033; suivant : 002035Comparative Analysis of DNA Word Abundances in Four Yeast Genomes Using a Novel Statistical Background Model
Auteurs : Ramkumar Hariharan [Inde, Japon] ; Reji Simon [Inde] ; M. Radhakrishna Pillai [Inde] ; Todd D. Taylor [Japon]Source :
- PLoS ONE [ 1932-6203 ] ; 2013.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , analysis : DNA, Fungal.
- genetics : Saccharomyces.
- methods : Genomics.
- Algorithms, Binding Sites, DNA, Intergenic, Genome, Fungal, Markov Chains, Models, Genetic, Models, Statistical, Open Reading Frames, Sequence Analysis, DNA, Species Specificity.
Abstract
Previous studies have shown that the identification and analysis of both abundant and rare k-mers or “DNA words of length k” in genomic sequences using suitable statistical background models can reveal biologically significant sequence elements. Other studies have investigated the uni/multimodal distribution of k-mer abundances or “k-mer spectra” in different DNA sequences. However, the existing background models are affected to varying extents by compositional bias. Moreover, the distribution of k-mer abundances in the context of related genomes has not been studied previously. Here, we present a novel statistical background model for calculating k-mer enrichment in DNA sequences based on the average of the frequencies of the two (k-1) mers for each k-mer. Comparison of our null model with the commonly used ones, including Markov models of different orders and the single mismatch model, shows that our method is more robust to compositional AT-rich bias and detects many additional, repeat-poor over-abundant k-mers that are biologically meaningful. Analysis of overrepresented genomic k-mers (4≤k≤16) from four yeast species using this model showed that the fraction of overrepresented DNA words falls linearly as k increases; however, a significant number of overabundant k-mers exists at higher values of k. Finally, comparative analysis of k-mer abundance scores across four yeast species revealed a mixture of unimodal and multimodal spectra for the various genomic sub-regions analyzed.
Url:
DOI: 10.1371/journal.pone.0058038
PubMed: 23472131
PubMed Central: 3589456
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en"><p>Previous studies have shown that the identification and analysis of both abundant and rare k-mers or “DNA words of length k” in genomic sequences using suitable statistical background models can reveal biologically significant sequence elements. Other studies have investigated the uni/multimodal distribution of k-mer abundances or “k-mer spectra” in different DNA sequences. However, the existing background models are affected to varying extents by compositional bias. Moreover, the distribution of k-mer abundances in the context of related genomes has not been studied previously. Here, we present a novel statistical background model for calculating k-mer enrichment in DNA sequences based on the average of the frequencies of the two (k-1) mers for each k-mer. Comparison of our null model with the commonly used ones, including Markov models of different orders and the single mismatch model, shows that our method is more robust to compositional AT-rich bias and detects many additional, repeat-poor over-abundant k-mers that are biologically meaningful. Analysis of overrepresented genomic k-mers (4≤k≤16) from four yeast species using this model showed that the fraction of overrepresented DNA words falls linearly as k increases; however, a significant number of overabundant k-mers exists at higher values of k. Finally, comparative analysis of k-mer abundance scores across four yeast species revealed a mixture of unimodal and multimodal spectra for the various genomic sub-regions analyzed.</p>
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