title: [Prediction of transcription and genomic sequences].
abstract: Technological developments have enhanced DNA sequencing at genomic scale. On the basis of the resulting sequences, computational biologists now attempt to localise the most important functional regions, starting with genes, but also importantly the regulatory motifs and conditions controlling their expression. In a recent paper published in Cell, M.A. Beer and S. Tavazoie report the results obtained by combining statistical classifications (clustering) of transcriptome data (DNA chips), software for the discovery of cis-regulatory patterns, together with a probabilistic learning method to infer regulatory rules tentatively accounting for the observed transcriptional profiles.
authors.affiliation: Laboratoire de Génétique et de physiologie du développement, LGPD-IBDM, CNRS, Case 907, Université de la Méditerranée, Campus Scientifique de Luminy, 13288 Marseille 9, France.
authors.forename: David
authors.initials: D
authors.lastname: Martin
authors.forename: Badih
authors.initials: B
authors.lastname: Ghattas
authors.forename: Denis
authors.initials: D
authors.lastname: Thieffry
mesh_terms: D005544:Forecasting; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D011337:Probability Learning; D017422:Sequence Analysis, DNA; D012984:Software; D014158:Transcription, Genetic