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  Alignment Statistics for Long-Range Correlated Genomic Sequences

Messer, P. W., Bundschuh, R., Viingron, M., & Arndt, P. F. (2006). Alignment Statistics for Long-Range Correlated Genomic Sequences. Berlin/Heidelberg: Springer.

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 Creators:
Messer, Philipp W.1, Author
Bundschuh, Ralf, Author
Viingron, Martin1, Author
Arndt, Peter F.2, Author           
Affiliations:
1Max Planck Society, ou_persistent13              
2Evolutionary Genomics (Peter Arndt), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479638              

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 Abstract: It is well known that the base composition along eukaryotic genomes is long-range correlated. Here, we investigate the effect of such long-range correlations on alignment score statistics. We model the correlated score-landscape by means of a Gaussian approximation. In this framework, we can calculate the corrections to the scale parameter λ of the extreme value distribution of alignment scores. To evaluate our approximate analytic results, we perform a detailed numerical study based on a simple algorithm to efficiently generate long-range correlated random sequences. We find that the mean and the exponential tail of the score distribution are in fact influenced by the correlations along the sequences. Therefore, the significance of measured alignment scores in biological sequences will change upon incorporation of the correlations in the null model.

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Language(s): eng - English
 Dates: 2006-08-12
 Publication Status: Issued
 Pages: 612
 Publishing info: Berlin/Heidelberg : Springer, 1st edition
 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 311242
ISBN: 978-3-540-33295-4
DOI: 10.1007/11732990_36
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Title: 10th Annual International Conference, RECOMB 2006
Place of Event: Venice, Italy
Start-/End Date: 2006-04-02 - 2006-04-05

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Title: Lecture Notes in Computer Science
Source Genre: Series
 Creator(s):
Hofmann, Alfred, Editor
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Pages: - Volume / Issue: 3909/2006 Sequence Number: - Start / End Page: - Identifier: -