Abstract
Gibbs sampler is for de novo motif discovery. Suppose we have a set of sequences each containing a regulatory motif located in different locations of the sequences, but we do not know what the motif looks like or where it is located within each sequence. Gibbs sampler will find such a motif if it is well represented in these sequences. If we have a set of yeast intron sequences each containing a branchpoint site (BPS) somewhere, but we do not know what BPS looks like or where it is located along the intron sequence, Gibbs sampler will find these BPSs. Another scenario involves the discovery of protein binding sites (e.g., transcription factor binding site) given a set of sequences from ChIP-Seq. Each of these sequences has a short sequence segment with affinity to a protein, but we do not know what the short sequence segment looks like or where it is located within the sequence. Gibbs sampler shines in discovering such protein-binding sites. This chapter breaks the black box of Gibbs sampler and numerically illustrates each of its computational steps, including the site sampler (which assumes that each input sequence harbors a signal motif) and motif sampler (which is used when some sequences may contain multiple signal motifs and some none).
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Postscript
Gibbs sampler for motif discovery illustrates the magic of a random process guided by a selection process. We start with a random set of motifs and apply a selection process that favors certain motifs against others, based on the criterion that the chosen motifs should contribute to a site-specific frequency distribution with a larger F defined in Eq. (4.1). The starting set of random motifs, shaped by the selection process, eventually converges to a final set of motifs with a strong nonrandom pattern. Sometimes we may have sequences with two or more different types of signal motifs. If we run Gibbs sampler with different starting sets of random motifs, we may converge to different sets of highly informative motifs. Thus, the same random process coupled with the same selection process may generate quite different outcomes.
My Christian friends often assert that Darwinian evolutionary theory is all wrong because random collision of molecules cannot generate highly structured patterns. Random collision of molecules indeed is limited by their potential to generate highly structured patterns. However, Darwinian evolution is not a random process. In fact, Darwin’s most significant contribution to biology is the substantiation of a particular force that he named natural selection. The combination of a random process guided by this particular force can do miracles in generating biodiversity of all colors and shades. It is when Darwin visualized the miracles generated by this ubiquitous force that he proclaimed that “There is grandeur in this view of life.”
This force has been with us, from time immemorial, and continues to shape all forms of life, including the life of those who deny its existence.
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Xia, X. (2018). Gibbs sampler. In: Bioinformatics and the Cell. Springer, Cham. https://doi.org/10.1007/978-3-319-90684-3_4
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DOI: https://doi.org/10.1007/978-3-319-90684-3_4
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