ArticleJournal of computational biology : a journal of computational molecular cell biology2010
Importance sampling of word patterns in DNA and protein sequences.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Family-Based Quantitative Trait Meta-Analysis Implicates Rare Noncoding Variants in DENND1A in Polycystic Ovary Syndrome.The Journal of clinical endocrinology and metabolism · 2019Article
- Functional Genetic Variation in the Anti-Müllerian Hormone Pathway in Women With Polycystic Ovary Syndrome.The Journal of clinical endocrinology and metabolism · 2019Article
- Genetic variants influence on the placenta regulatory landscape.PLoS genetics · 2018Article
- atSNP: transcription factor binding affinity testing for regulatory SNP detection.Bioinformatics (Oxford, England) · 2015Article
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Authors and funding
3 authors.
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Abstract
Monte Carlo methods can provide accurate p-value estimates of word counting test statistics and are easy to implement. They are especially attractive when an asymptotic theory is absent or when either the search sequence or the word pattern is too short for the application of asymptotic formulae. Naive direct Monte Carlo is undesirable for the estimation of small probabilities because the associated rare events of interest are seldom generated. We propose instead efficient importance sampling algorithms that use controlled insertion of the desired word patterns on randomly generated sequences. The implementation is illustrated on word patterns of biological interest: palindromes and inverted repeats, patterns arising from position-specific weight matrices (PSWMs), and co-occurrences of pairs of motifs.
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