ArticleBMC bioinformatics2005
Computing expectation values for RNA motifs using discrete convolutions.
Article in BMC bioinformatics, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Parametric bootstrapping for biological sequence motifs.BMC bioinformatics · 2016Article
- Informatic resources for identifying and annotating structural RNA motifs.Molecular biotechnology · 2009Review
- PSSMTS: position specific scoring matrices on tree structures.Journal of mathematical biology · 2008Article
- Evolutionary dynamics of eukaryotic selenoproteomes: large selenoproteomes may associate with aquatic life and small with terrestrial life.Genome biology · 2007Article
- Searching for IRES.RNA (New York, N.Y.) · 2006Review
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4 authors.
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Abstract
backgroundComputational biologists use Expectation values (E-values) to estimate the number of solutions that can be expected by chance during a database scan. Here we focus on computing Expectation values for RNA motifs defined by single-strand and helix lod-score profiles with variable helix spans. Such E-values cannot be computed assuming a normal score distribution and their estimation previously required lengthy simulations.
resultsWe introduce discrete convolutions as an accurate and fast mean to estimate score distributions of lod-score profiles. This method provides excellent score estimations for all single-strand or helical elements tested and also applies to the combination of elements into larger, complex, motifs. Further, the estimated distributions remain accurate even when pseudocounts are introduced into the lod-score profiles. Estimated score distributions are then easily converted into E-values.
conclusionA good agreement was observed between computed E-values and simulations for a number of complete RNA motifs. This method is now implemented into the ERPIN software, but it can be applied as well to any search procedure based on ungapped profiles with statistically independent columns.
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