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Recovering motifs from biased genomes: application of signal correction.

Hasan S, Schreiber M - Nucleic Acids Res. (2006)

Bottom Line: We find that the average Euclidian distance between RBS signal frequency matrices of different genomes can be significantly reduced by using the correction technique.Within this reduced average distance, we can find examples of class-specific RBS signals.Our results have implications for motif-based prediction, particularly with regards to the estimation of reliable inter-genomic model parameters.

View Article: PubMed Central - PubMed

Affiliation: Novartis Institute for Tropical Diseases (NITD), 10 Biopolis Road, #05-01 Chromos, Singapore 138670.

ABSTRACT
A significant problem in biological motif analysis arises when the background symbol distribution is biased (e.g. high/low GC content in the case of DNA sequences). This can lead to overestimation of the amount of information encoded in a motif. A motif can be depicted as a signal using information theory (IT). We apply two concepts from IT, distortion and patterned interference (a type of noise), to model genomic and codon bias respectively. This modeling approach allows us to correct a raw signal to recover signals that are weakened by compositional bias. The corrected signal is more likely to be discriminated from a biased background by a macromolecule. We apply this correction technique to recover ribosome-binding site (RBS) signals from available sequenced and annotated prokaryotic genomes having diverse compositional biases. We observed that linear correction was sufficient for recovering signals even at the extremes of these biases. Further comparative genomics studies were made possible upon correction of these signals. We find that the average Euclidian distance between RBS signal frequency matrices of different genomes can be significantly reduced by using the correction technique. Within this reduced average distance, we can find examples of class-specific RBS signals. Our results have implications for motif-based prediction, particularly with regards to the estimation of reliable inter-genomic model parameters.

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Related in: MedlinePlus

Compositional biases of major prokaryotic classes represented by %GC. The data are grouped and sorted in ascending order by the average GC content of the class.
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fig1: Compositional biases of major prokaryotic classes represented by %GC. The data are grouped and sorted in ascending order by the average GC content of the class.

Mentions: The information in DNA and RNA sequences can be encoded using four symbols but in most genomes, these symbols are not observed at equal frequencies (see Figure 1). These skewed distributions have consequences on the ability to predict features on one genome from another. Korf (8) highlighted these issues while comparing the prediction accuracy of eukaryotic gene finders that were trained on foreign genomes:


Recovering motifs from biased genomes: application of signal correction.

Hasan S, Schreiber M - Nucleic Acids Res. (2006)

Compositional biases of major prokaryotic classes represented by %GC. The data are grouped and sorted in ascending order by the average GC content of the class.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC1636444&req=5

fig1: Compositional biases of major prokaryotic classes represented by %GC. The data are grouped and sorted in ascending order by the average GC content of the class.
Mentions: The information in DNA and RNA sequences can be encoded using four symbols but in most genomes, these symbols are not observed at equal frequencies (see Figure 1). These skewed distributions have consequences on the ability to predict features on one genome from another. Korf (8) highlighted these issues while comparing the prediction accuracy of eukaryotic gene finders that were trained on foreign genomes:

Bottom Line: We find that the average Euclidian distance between RBS signal frequency matrices of different genomes can be significantly reduced by using the correction technique.Within this reduced average distance, we can find examples of class-specific RBS signals.Our results have implications for motif-based prediction, particularly with regards to the estimation of reliable inter-genomic model parameters.

View Article: PubMed Central - PubMed

Affiliation: Novartis Institute for Tropical Diseases (NITD), 10 Biopolis Road, #05-01 Chromos, Singapore 138670.

ABSTRACT
A significant problem in biological motif analysis arises when the background symbol distribution is biased (e.g. high/low GC content in the case of DNA sequences). This can lead to overestimation of the amount of information encoded in a motif. A motif can be depicted as a signal using information theory (IT). We apply two concepts from IT, distortion and patterned interference (a type of noise), to model genomic and codon bias respectively. This modeling approach allows us to correct a raw signal to recover signals that are weakened by compositional bias. The corrected signal is more likely to be discriminated from a biased background by a macromolecule. We apply this correction technique to recover ribosome-binding site (RBS) signals from available sequenced and annotated prokaryotic genomes having diverse compositional biases. We observed that linear correction was sufficient for recovering signals even at the extremes of these biases. Further comparative genomics studies were made possible upon correction of these signals. We find that the average Euclidian distance between RBS signal frequency matrices of different genomes can be significantly reduced by using the correction technique. Within this reduced average distance, we can find examples of class-specific RBS signals. Our results have implications for motif-based prediction, particularly with regards to the estimation of reliable inter-genomic model parameters.

Show MeSH
Related in: MedlinePlus