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. 2003 Jul 1;31(13):3580-5.
doi: 10.1093/nar/gkg608.

Gibbs Recursive Sampler: finding transcription factor binding sites

William Thompson  1 , Eric C Rouchka, Charles E Lawrence
Affiliations

Gibbs Recursive Sampler: finding transcription factor binding sites

William Thompson et al. Nucleic Acids Res. .

Abstract

The Gibbs Motif Sampler is a software package for locating common elements in collections of biopolymer sequences. In this paper we describe a new variation of the Gibbs Motif Sampler, the Gibbs Recursive Sampler, which has been developed specifically for locating multiple transcription factor binding sites for multiple transcription factors simultaneously in unaligned DNA sequences that may be heterogeneous in DNA composition. Here we describe the basic operation of the web-based version of this sampler. The sampler may be acces-sed at http://bayesweb.wadsworth.org/gibbs/gibbs.html and at http://www.bioinfo.rpi.edu/applications/bayesian/gibbs/gibbs.html. An online user guide is available at http://bayesweb.wadsworth.org/gibbs/bernoulli.html and at http://www.bioinfo.rpi.edu/applications/bayesian/gibbs/manual/bernoulli.html. Solaris, Solaris.x86 and Linux versions of the sampler are available as stand-alone programs for academic and not-for-profit users. Commercial licenses are also available. The Gibbs Recursive Sampler is distributed in accordance with the ISCB level 0 guidelines and a requirement for citation of use in scientific publications.

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Figures

Figure 1
Figure 1
The compositional variation of a 500 bp region upstream of the translation start site of the YDR226W/ADK1 gene from Saccharomyces cerevisiae. The probability of each base at each position is calculated by the Bayesian segmentation algorithm (10). This algorithm returns the probabilities of observing each of the four bases at each position in the sequence.
Figure 2
Figure 2
The histogram shows the distribution of the distances of 182 reported TFBS from start codons in E.coli regulatory sequences. The solid line is a mixture of Gaussian functions fitted to the histogram data and represents the default prokaryotic probability distribution for site locations.
Figure 3
Figure 3
The basic Gibbs Recursive Sampler data entry form, showing the minimum amount of data required by the program.
Figure 4
Figure 4
The Gibbs Recursive Sampler advanced entry page, showing modifiable parameters.
Figure 5
Figure 5
Sample output from Gibbs Recursive Sampler.
Figure 5
Figure 5
Sample output from Gibbs Recursive Sampler.
Figure 5
Figure 5
Sample output from Gibbs Recursive Sampler.
Figure 5
Figure 5
Sample output from Gibbs Recursive Sampler.
See this image and copyright information in PMC

References

    1. Lawrence C.E., Altschul,S., Boguski,M., Liu,J., Neuwald,A. and Wootton,J. (1993) Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment. Science, 262, 208–214. - PubMed
    1. Neuwald A., Liu,J. and Lawrence,C.E. (1996) Gibbs Motif Sampling: detection of bacterial outer membrane protein repeats. Protein Sci., 4, 1618–1632. - PMC - PubMed
    1. Robison K., McGuire,A.M. and Church,G.M. (1998) A comprehensive library of DNA-binding site matrices for 55 proteins applied to the complete Escherichia coli K-12 genome. J. Mol. Biol., 248, 241–254. - PubMed
    1. McCue L.A., Thompson,W., Carmack,C.S., Ryan,M., Liu,J., Derbyshire,V. and Lawrence,C.E. (2001) Phylogenetic footprinting of transcription factor binding sites in proteobacterial genomes, Nucleic Acids Res., 39, 774–782. - PMC - PubMed
    1. Liu J., Neuwald,A. and Lawrence,C.E. (1995) Bayesian models for multiple local sequence alignment and Gibbs sampling strategies. J. Amer. Stat. Assoc., 90, 1156–1170.

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