منابع مشابه
Machine Learning Techniques for Predicting Bacillus subtilis Promoters
One of the most important goals of bioinformatics is the ability to identify genes in uncharacterized DNA sequences. Improved promoter prediction methods can be one step towards developing more reliable ab initio gene prediction methods. In this paper, we present an empirical comparison of machine learning techniques such as Naive Bayes, Decision Trees, Support Vector Machines and Neural Networ...
متن کاملPredicting polymerase II core promoters by cooperating transcription factor binding sites in eukaryotic genes.
Several discriminate functions for predicting core promoters that based on the potential cooperation between transcription factor binding sites (TFBSs) are discussed. It is demonstrated that the promoter predicting accuracy is improved when the cooperation among TFBSs is taken into consideration. The core promoter region of a newly discovered gene CKLFSF1 is predicted to locate more than 1.5 kb...
متن کاملPredicting promoters targeted by TAL effectors in plant genomes: from dream to reality
INTRODUCTION Transcription Activator-Like (TAL) effectors from the plant pathogenic bacteria of the genus Xanthomonas are molecular weapons injected into eukaryotic cells to modulate the host transcriptome. Upon delivery, TAL effectors localize into the host cell nucleus and bind to the promoter of plant susceptibility (S) genes to activate their expression and thereby facilitate bacterial mult...
متن کاملEvolutionary population genetics of promoters: predicting binding sites and functional phylogenies.
We study the evolution of transcription factor-binding sites in prokaryotes, using an empirically grounded model with point mutations and genetic drift. Selection acts on the site sequence via its binding affinity to the corresponding transcription factor. Calibrating the model with populations of functional binding sites, we verify this form of selection and show that typical sites are under s...
متن کاملPredicting the strength of UP-elements and full-length E. coli σE promoters
Predicting the location and strength of promoters from genomic sequence requires accurate sequenced-based promoter models. We present the first model of a full-length bacterial promoter, encompassing both upstream sequences (UP-elements) and core promoter modules, based on a set of 60 promoters dependent on σ(E), an alternative ECF-type σ factor. UP-element contribution, best described by the l...
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ژورنال
عنوان ژورنال: Genome Biology
سال: 2001
ISSN: 1465-6906
DOI: 10.1186/gb-spotlight-20011128-01