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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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Corrected and averaged RBS sequence logos of prokaryotic classes. The sequence logos are ordered here by ascending average GC-content of the class. Classes that form sub-clusters on either of the two major principal components (Figure 6b), based on their RBS signal similarity (Table 1), are underlined.
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fig8: Corrected and averaged RBS sequence logos of prokaryotic classes. The sequence logos are ordered here by ascending average GC-content of the class. Classes that form sub-clusters on either of the two major principal components (Figure 6b), based on their RBS signal similarity (Table 1), are underlined.

Mentions: We observed four bacterial classes (Figure 8g,i,q,s) and one archaeal class (Figure 8d) that tend to conserve their RBS signal both in terms of SD motif and corrected triplet noise. These form sub-clusters on PCA1 (Table 1) and PCA1 can be explained largely by SD motif and corrected triplet noise (Figure 7b).


Recovering motifs from biased genomes: application of signal correction.

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

Corrected and averaged RBS sequence logos of prokaryotic classes. The sequence logos are ordered here by ascending average GC-content of the class. Classes that form sub-clusters on either of the two major principal components (Figure 6b), based on their RBS signal similarity (Table 1), are underlined.
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Related In: Results  -  Collection

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

fig8: Corrected and averaged RBS sequence logos of prokaryotic classes. The sequence logos are ordered here by ascending average GC-content of the class. Classes that form sub-clusters on either of the two major principal components (Figure 6b), based on their RBS signal similarity (Table 1), are underlined.
Mentions: We observed four bacterial classes (Figure 8g,i,q,s) and one archaeal class (Figure 8d) that tend to conserve their RBS signal both in terms of SD motif and corrected triplet noise. These form sub-clusters on PCA1 (Table 1) and PCA1 can be explained largely by SD motif and corrected triplet noise (Figure 7b).

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