2014/01/31 by Dennis Kostka, Tara Friedrich, Kostka, Dennis +5
Biochemistry, Genetics and Molecular Biology · #FOS: Biological sciences #Genetic Mapping and Diversity in Plants and Animals #Genomics (q-bio.GN) #Genomics and Chromatin Dynamics #RNA and protein synthesis mechanisms
paper · pdf · doi:10.48550/arxiv.1402.0042
openalex publication_date 2014/01/31 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
Next-generation sequencing technology enables the identification of thousands\nof gene regulatory sequences in many cell types and organisms. We consider the\nproblem of testing if two such sequences differ in their number of binding site\nmotifs for a given transcription factor (TF) protein. Binding site motifs\nimpart regulatory function by providing TFs the opportunity to bind to genomic\nelements and thereby affect the expression of nearby genes. Evolutionary\nchanges to such functional DNA are hypothesized to be major contributors to\nphenotypic diversity within and between species; but despite the importance of\nTF motifs for gene expression, no method exists to test for motif loss or gain.\nAssuming that motif counts are Binomially distributed, and allowing for\ndependencies between motif instances in evolutionarily related sequences, we\nderive the probability mass function of the difference in motif counts between\ntwo nucleotide sequences. We provide a method to numerically estimate this\ndistribution from genomic data and show through simulations that our estimator\nis accurate. Finally, we introduce the R package tt motifDiverge that\nimplements our methodology and illustrate its application to gene regulatory\nenhancers identified by a mouse developmental time course experiment. While\nthis study was motivated by analysis of regulatory motifs, our results can be\napplied to any problem involving two correlated Bernoulli trials.\n