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.

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    ABSTRACT: In biological sequence research, the positional weight matrix (PWM) is often used to search for putative transcription factor binding sites. A set of experimentally verified oligonucleotides known to be functional motifs are collected and aligned. The frequency of each nucleotide A, C, G, or T at each column of the alignment is calculated in the matrix. Once a PWM is constructed, it can be used to search from a nucleotide sequence for subsequences that are possibly perform the same function. The match between a subsequence and a PWM is usually described by a score function, which measures the closeness of the subsequence to the PWM as compared with the given background. Nevertheless, the score function is usually 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
    Engineering Letters 01/2008; 16(4):498-504.
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    ABSTRACT: In biological sequence research, the positional weight matrix (PWM) is often used for motif signal detection. A set of experimentally verified oligonucleotides known to be functional subsequences, which can be bound by a transcription factor (TF), as translational initiation sites or pre-mRNA splicing sites, are collected and aligned. The frequency of each nucleotide A, C, G, or T at each column of the alignment is calculated in the matrix. Once a PWM is constructed, it can be used to search from a nucleotide sequence for the subsequences that possibly perform the same function. The match between a subsequence and a PWM is usually described by a score function, which measures the closeness of the subsequence to the PWM as compared with the given background. However, selection of threshold scores that legitimately qualify a functional subsequence has been a great challenge. Many laboratories have attempted tackling this problem; but there is no significant breakthrough so far. In this chapter, we discuss the characteristics of a PWM and factors that affect motif predictions and propose a new score function that is tied into information content and statistical expectation of a PWM. We also apply this score function in the PWMs from public databases and compare it favorably with the broadly used Match method.
    01/2008: pages 421-440; , ISBN: 978-1-60456-542-3

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