Abstract
BackgroundThe precision of transcriptional regulation is made possible by the specificity of physical interactions between transcription factors and their cognate binding sites on DNA. A major challenge is to decipher transcription factor binding sites from sequence and functional genomic data using computational means. While current methods can detect strong binding sites, they are less sensitive to degenerate motifs.ResultsWe present fREDUCE, a computational method specialized for the detection of weak or degenerate binding motifs from gene expression or ChIP-chip data. fREDUCE is built upon the widely applied program REDUCE, which elicits motifs by global statistical correlation of motif counts with expression data. fREDUCE introduces several algorithmic refinements that allow efficient exhaustive searches of oligonucleotides with a specified number of degenerate IUPAC symbols. On yeast ChIP-chip benchmarks, fREDUCE correctly identified motifs and their degeneracies with accuracies greater than its predecessor REDUCE as well as other known motif-finding programs. We have also used fREDUCE to make novel motif predictions for transcription factors with poorly characterized binding sites.ConclusionWe demonstrate that fREDUCE is a valuable tool for the prediction of degenerate transcription factor binding sites, especially from array datasets with weak signals that may elude other motif detection methods.
Highlights
The precision of transcriptional regulation is made possible by the specificity of physical interactions between transcription factors and their cognate binding sites on DNA
The direct computation of the Pearson correlation coefficient is computationally laborious and is not well suited for analyzing large spaces of degenerate oligonucleotides. fREDUCE uses the following strategy to efficiently compute the Pearson coefficients of the most significant degenerate motifs (Figure 1): 1) A list of degenerate motifs that can be derived from the sequence data is generated
3) Actual Pearson coefficients are computed and the top motif is found and 4) The contribution from the top motif is subtracted from the expression data to form a residual, which is used for subsequent rounds of motif searching
Summary
A major challenge is to decipher transcription factor binding sites from sequence and functional genomic data using computational means. From a computational standpoint, a major challenge is to develop techniques that can extract maximal regulator specificity information from imperfect data. The delineation of signal from background may be poor for noisy experimental data, where cutoffs can lead to significant loss of information. Other algorithms, such as dictionary- [11] or steganalysis-based [12] methods, do not rely on clustering but can benefit from subgroup selection
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
