Abstract
The new technology of protein binding microarrays (PBMs) allows simultaneous measurement of the binding intensities of a transcription factor to tens of thousands of synthetic double-stranded DNA probes, covering all possible 10-mers. A key computational challenge is inferring the binding motif from these data. We present a systematic comparison of four methods developed specifically for reconstructing a binding site motif represented as a positional weight matrix from PBM data. The reconstructed motifs were evaluated in terms of three criteria: concordance with reference motifs from the literature and ability to predict in vivo and in vitro bindings. The evaluation encompassed over 200 transcription factors and some 300 assays. The results show a tradeoff between how the methods perform according to the different criteria, and a dichotomy of method types. Algorithms that construct motifs with low information content predict PBM probe ranking more faithfully, while methods that produce highly informative motifs match reference motifs better. Interestingly, in predicting high-affinity binding, all methods give far poorer results for in vivo assays compared to in vitro assays.
Highlights
Understanding gene regulation is a fundamental problem in biological research
In this paper we present a systematic comparison of four algorithms for identifying TF binding sites (TFBSs) motifs from protein binding microarrays (PBMs) profiles: Seedand-Wobble (SW) [4], RankMotif++ (RM) [9], BEEML-PBM (BE) [10] and the algorithm Amadeus-PBM (AM) introduced here
For each transcription factors (TFs), we measured the distance between the PBM-based position weight matrix (PWM) to the PWM of the same TF as published in JASPAR [12]
Summary
Understanding gene regulation is a fundamental problem in biological research. A principal way to regulate gene expression in the cell is via transcription, which is governed primarily by transcription factors (TFs). A TF is a protein that binds to the promoter region of a gene at specific sequences, called TF binding sites (TFBSs). The binding of one or several TFs enables or impedes the transcription of the gene. A TF binds to similar short nucleotide sequences at different affinities. Finding these cisregulatory elements and modeling the affinity of TF binding to them is a central challenge in understanding gene regulation
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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.
