Generic eukaryotic core promoter prediction using structural features of DNA Export

@article{citeulike:2153687, abstract = {Despite many recent efforts, in silico identification of promoter regions is still in its infancy. However, the accurate identification and delineation of promoter regions is important for several reasons, such as improving genome annotation and devising experiments to study and understand transcriptional regulation. Current methods to identify the core region of promoters require large amounts of high-quality training data and often behave like black box models that output predictions that are difficult to interpret. Here, we present a novel approach for predicting promoters in whole-genome sequences by using large-scale structural properties of DNA. Our technique requires no training, is applicable to many eukaryotic genomes, and performs extremely well in comparison with the best available promoter prediction programs. Moreover, it is fast, simple in design, and has no size constraints, and the results are easily interpretable. We compared our approach with 14 current state-of-the-art implementations using human gene and transcription start site data and analyzed the ENCODE region in more detail. We also validated our method on 12 additional eukaryotic genomes, including vertebrates, invertebrates, plants, fungi, and protists.}, author = {Abeel, Thomas and Saeys, Yvan and Bonnet, Eric and Rouze, Pierre and Van de Peer, Yves}, citeulike-article-id = {2153687}, citeulike-linkout-0 = {http://dx.doi.org/10.1101/gr.6991408}, citeulike-linkout-1 = {http://genome.cshlp.org/content/18/2/310.full.abstract}, citeulike-linkout-2 = {http://genome.cshlp.org/content/18/2/310.full.full.pdf}, citeulike-linkout-3 = {http://www.genome.org/cgi/content/abstract/18/2/310}, citeulike-linkout-4 = {http://view.ncbi.nlm.nih.gov/pubmed/18096745}, citeulike-linkout-5 = {http://www.hubmed.org/display.cgi?uids=18096745}, day = {20}, doi = {10.1101/gr.6991408}, journal = {Genome Research}, keywords = {bioinformatics, genomics, promoter, transcription}, month = {February}, number = {2}, pages = {310--323}, posted-at = {2009-11-06 09:53:22}, priority = {2}, title = {Generic eukaryotic core promoter prediction using structural features of DNA}, url = {http://dx.doi.org/10.1101/gr.6991408}, volume = {18}, year = {2008} }

TY - JOUR ID - citeulike:2153687 L3 - citeulike-article-id:2153687 N2 - Despite many recent efforts, in silico identification of promoter regions is still in its infancy. However, the accurate identification and delineation of promoter regions is important for several reasons, such as improving genome annotation and devising experiments to study and understand transcriptional regulation. Current methods to identify the core region of promoters require large amounts of high-quality training data and often behave like black box models that output predictions that are difficult to interpret. Here, we present a novel approach for predicting promoters in whole-genome sequences by using large-scale structural properties of DNA. Our technique requires no training, is applicable to many eukaryotic genomes, and performs extremely well in comparison with the best available promoter prediction programs. Moreover, it is fast, simple in design, and has no size constraints, and the results are easily interpretable. We compared our approach with 14 current state-of-the-art implementations using human gene and transcription start site data and analyzed the ENCODE region in more detail. We also validated our method on 12 additional eukaryotic genomes, including vertebrates, invertebrates, plants, fungi, and protists. IS - 2 JF - Genome Research EP - 323 TI - Generic eukaryotic core promoter prediction using structural features of DNA VL - 18 SP - 310 KW - bioinformatics KW - genomics KW - promoter KW - transcription AU - Abeel, Thomas AU - Saeys, Yvan AU - Bonnet, Eric AU - Rouze, Pierre AU - Van de Peer, Yves PY - 2008/02/20/ UR - http://dx.doi.org/10.1101/gr.6991408 ER -