title: Detecting correlations among functional-sequence motifs.
abstract: Sequence motifs are words of nucleotides in DNA with biological functions, e.g., gene regulation. Identification of such words proceeds through rejection of Markov models on the expected motif frequency along the genome. Additional biological information can be extracted from the correlation structure among patterns of motif occurrences. In this paper a log-linear multivariate intensity Poisson model is estimated via expectation maximization on a set of motifs along the genome of E. coli K12. The proposed approach allows for excitatory as well as inhibitory interactions among motifs and between motifs and other genomic features like gene occurrences. Our findings confirm previous stylized facts about such types of interactions and shed new light on genome-maintenance functions of some particular motifs. We expect these methods to be applicable to a wider set of genomic features.
authors.affiliation: LEM, Scuola Superiore Sant'Anna, 56127 Pisa, Italy. d.pirino@sssup.it
authors.forename: Davide
authors.initials: D
authors.lastname: Pirino
authors.forename: Jacopo
authors.initials: J
authors.lastname: Rigosa
authors.forename: Alice
authors.initials: A
authors.lastname: Ledda
authors.forename: Luca
authors.initials: L
authors.lastname: Ferretti
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D004247:DNA / Q000737:chemistry* / Q000235:genetics*; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D017422:Sequence Analysis, DNA / Q000379:methods*; D013329:Structure-Activity Relationship
chemical_list: D004247:DNA