Gibbs Recursive Sampler: finding transcription factor binding sites

By W. Thompson, E. Rouchka, Charles E. Lawrence. Nucleic Acids Res., 2003

348 citations recorded by the source corpus.

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The Bayesian revolution in genetics (2004, methodology): “, and have also been used to identify transcription-factor binding site”
Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training. (2008, methodology): “Additionally, we generated a type I test set for S. pombe, the most studied genome, by using annotated complete genes with protein products exactly matching S. pombe proteins in the SWISS-PROT database (Watanabe and Harayama 2001).”
Discovering Motifs in Ranked Lists of DNA Sequences (2007, background): “Examples of approaches that search for combinatorial patterns and modules underlying TF binding and gene expression include [30, 31, 32, 33, 34].”
A survey of DNA motif finding algorithms (2007, methodology): “They compared PhyME with the algorithms MEME, OrthoMEME, Phylo-Gibbs [16], EMnEm [15] and GIBBS (Wadsworth Gibbs sampler) [67] and reported that the motif detection ability of PhyME was better than the other algorithms in most cases.”
Weeder Web: discovery of transcription factor binding sites in a set of sequences from co-regulated genes (2004, positive): “Thus, given a set of sequences, the best way to proceed is perhaps to try different methods based on wholly different principles, such as the one presented here and traditional alignment-driven methods such as Consensus (9), MEME (10) and the Gibbs sampler (11), and to compare the results obtained.”