Conference Paper

Selection of putative cis-regulatory motifs through regional and global conservation

Nat. Res. Council of Canada, Ottawa, Ont., Canada
DOI: 10.1109/CSB.2004.1332545 Conference: Computational Systems Bioinformatics Conference, 2004. CSB 2004. Proceedings. 2004 IEEE
Source: IEEE Xplore

ABSTRACT Cis-regulatory motifs are often overrepresented in promoters and may exhibit frequency biases in subpromoter regions (SPRs). Many probabilistic algorithms have been used to predict such motifs, but they tend to generate many false positives. We devised a novel algorithm, MotifFilter, that computes representation indices (RIs) for putative motifs. MotifFilter's RI is a ratio of the actual over expected frequency of a motif in promoters, SPRs or random genomic DNA that takes into account of the nucleotide probability distributions in these regions. This approach was applied to a genome-wide survey of putative cAMP-response elements (CREs) for motifs generated by a profile hidden Markov model. Twenty of 144 putative CRE motifs found in the survey were retained by the MotifFilter.

Download full-text


Available from: Youlian Pan, Jul 01, 2015
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In biological sequence research, the positional weight matrix (PWM) is often used to search for putative transcription factor binding sites. A log-odd score is usually applied to measure the closeness of a subsequence to the PWM. However, the log-odd score is motif-length-dependent and thus there is no universally applicable threshold. In this paper, we propose an alternative scoring index (G) varying from zero, where the subsequence is not much different from the background, to one, where the subsequence fits best to the PWM. We also propose a measure evaluating the statistical expectation at each G index. We investigated the PWMs from the TRANSFAC and found that the statistical expectation is significantly ( p < 0.0001) correlated with both the length of the PWMs and the threshold G value. We applied this method to two PWMs (GCN4_C and ROX1_Q6) of yeast transcription factor binding sites and two PWMs (HIC1-02, HIC1_03) of the human tumor suppressor (HIC-1) binding sites from the TRANSFAC database. Finally, our method compares favorably with the broadly used Match method. The results indicate that our method is more flexible and can provide better confidence. Dans le domaine de la recherche de séquences en biologie, on a souvent recours à la matrice position-poids pour chercher les sites de fixation présumés des facteurs de transcription. On utilise généralement un score log-odd pour mesurer le degré de concordance d'une sous-séquence avec la matrice position-poids. Cependant, comme le score log-odd dépend de la longueur du motif, on ne peut donc pas appliquer un seuil universel. Dans cet article, nous proposons un autre index de scores (G) variant à partir de zéro, où la sous-séquence n'est pas très différente du bruit de fond, par rapport à un, et où la sous-séquence concorde le plus à la matrice position-poids. Nous proposons également une mesure évaluant l'espérance statistique de chaque index G. Nous avons étudié les matrices position-poids de la banque TRANSFAC et avons établi que l'espérance statistique est corrélée de manière statistiquement significative ( p < 0,0001) avec à la fois la longueur des matrices position-poids et le seuil de G. Nous avons appliqué cette méthode à deux matrices position-poids (GCN4_C et ROX1_Q6) correspondant aux sites de fixation d'un facteur de transcription chez la levure et deux matrices position-poids (HIC1-02 et HIC1_03) correspondant aux sites de fixation de HIC-1, un facteur de transcription suppresseur de tumeur chez l'humain, tirées de la banque TRANSFAC. Finalement, notre méthode se compare avantageusement à Match, la méthode couramment utilisée. Les résultats indiquent que notre méthode est plus souple et peut fournir un plus grand degré de certitude.
  • Article: Famili
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Discovery of transcription regulatory elements has been an enormous challenge, both to biologists and computational scientists. Over the last three decades, significant progress has been achieved by various laboratories around the world. Earlier, laborious experimental methods were used to detect one or handful of elements at a time. With recent advances in DNA sequencing technology, many completed genomes became available. High throughput biological techniques and computational methods emerged. Comparative genomic approaches and their integration with microarray gene expression data provided promising results. In this review, we discuss the development of technology to decipher the complex transcription regulation system with a focus on the discovery of cis-regulatory elements in eukaryotes.
    Current Bioinformatics 08/2006; 1(3):321-336. DOI:10.2174/157489306777828026 · 1.73 Impact Factor