The TAGteam motif facilitates binding of 21 sequence-specific transcription factors in the Drosophila embryo.
ABSTRACT Highly overlapping patterns of genome-wide binding of many distinct transcription factors have been observed in worms, insects, and mammals, but the origins and consequences of this overlapping binding remain unclear. While analyzing chromatin immunoprecipitation data sets from 21 sequence-specific transcription factors active in the Drosophila embryo, we found that binding of all factors exhibits a dose-dependent relationship with "TAGteam" sequence motifs bound by the zinc finger protein Vielfaltig, also known as Zelda, a recently discovered activator of the zygotic genome. TAGteam motifs are present and well conserved in highly bound regions, and are associated with transcription factor binding even in the absence of canonical recognition motifs for these factors. Furthermore, levels of binding in promoters and enhancers of zygotically transcribed genes are correlated with RNA polymerase II occupancy and gene expression levels. Our results suggest that Vielfaltig acts as a master regulator of early development by facilitating the genome-wide establishment of overlapping patterns of binding of diverse transcription factors that drive global gene expression.
Article: Fast statistical alignment.[show abstract] [hide abstract]
ABSTRACT: We describe a new program for the alignment of multiple biological sequences that is both statistically motivated and fast enough for problem sizes that arise in practice. Our Fast Statistical Alignment program is based on pair hidden Markov models which approximate an insertion/deletion process on a tree and uses a sequence annealing algorithm to combine the posterior probabilities estimated from these models into a multiple alignment. FSA uses its explicit statistical model to produce multiple alignments which are accompanied by estimates of the alignment accuracy and uncertainty for every column and character of the alignment--previously available only with alignment programs which use computationally-expensive Markov Chain Monte Carlo approaches--yet can align thousands of long sequences. Moreover, FSA utilizes an unsupervised query-specific learning procedure for parameter estimation which leads to improved accuracy on benchmark reference alignments in comparison to existing programs. The centroid alignment approach taken by FSA, in combination with its learning procedure, drastically reduces the amount of false-positive alignment on biological data in comparison to that given by other methods. The FSA program and a companion visualization tool for exploring uncertainty in alignments can be used via a web interface at http://orangutan.math.berkeley.edu/fsa/, and the source code is available at http://fsa.sourceforge.net/.PLoS Computational Biology 06/2009; 5(5):e1000392. · 5.22 Impact Factor