An improved map of conserved regulatory sites for Saccharomyces cerevisiae

By Kenzie D. MacIsaac, Ting Wang, D. Gordon, David K. Gifford, Gary D. Stormo, E. Fraenkel. BMC Bioinformatics, 2006

782 citations recorded by the source corpus.

BackgroundThe regulatory map of a genome consists of the binding sites for proteins that determine the transcription of nearby genes. An initial regulatory map for S. cerevisiae was recently published using six motif discovery programs to analyze genome-wide chromatin immunoprecipitation data for 203 transcription factors. The programs were used to identify sequence motifs that were likely to correspond to the DNA-binding specificity of the immunoprecipitated proteins. We report improved versions of two conservation-based motif discovery algorithms, PhyloCon and Converge. Using these programs, we create a refined regulatory map for S. cerevisiae by reanalyzing the same chromatin immunoprecipitation data.ResultsApplying the same conservative criteria that were applied in the original study, we find that PhyloCon and Converge each separately discover more known specificities than the combination of all six programs in the previous study. Combining the results of PhyloCon and Converge, we discover significant sequence motifs for 36 transcription factors that were previously missed. The new set of motifs identifies 636 more regulatory interactions than the previous one. The new network contains 28% more regulatory interactions among transcription factors, evidence of greater cross-talk between regulators.ConclusionCombining two complementary computational strategies for conservation-based motif discovery improves the ability to identify the specificity of transcriptional regulators from genome-wide chromatin immunoprecipitation data. The increased sensitivity of these methods significantly expands the map of yeast regulatory sites without the need to alter any of the thresholds for statistical significance. The new map of regulatory sites reveals a more elaborate and complex view of the yeast genetic regulatory network than was observed previously.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d881a1c09a2e04d631a7fdc499bd45e862f8095d

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Citations in the corpus

MEME Suite: tools for motif discovery and searching (2009, background): “The figure shows the TOMTOM output from searching a single DNA motif against a collection of yeast transcription factor binding site motifs identified via ChIP-seq (9).”
Distinct Modes of Regulation by Chromatin Encoded through Nucleosome Positioning Signals (2008, background): “transcription factor were obtained from [47,67].”
Global mapping of protein-DNA interactions in vivo by digital genomic footprinting (2009, background): “Taken together, these data suggest that the mean nucleo-tide-level DNA accessibility patterns derived from digital genomic footprinting of speciﬁc factors represent structural motifs that parallel protein-DNA interactions in vivo .”
Comprehensive Genome-wide Protein-DNA Interactions Detected at Single Nucleotide Resolution (2011, background): “Transcription Factor Phd1 Recognizes Distinct Motifs In an effort to evaluate whether ChIP-exo could define the specificity of a protein whose consensus site has been reported to be ambiguous (Badis et al., 2008; Harbison et al., 2004; MacIsaac et al., 2006; Zhu et al., 2009 ).”
JASPAR 2010: the greatly expanded open-access database of transcription factor binding profiles (2009, methodology): “The sources used, in order of preference, were a recent in vitro binding screen (22), a protein-binding microarray (PBM) experiment (23), the compiled SCPD binding profile database (24), the SwissRegulon computational re-analysis of multiple data collections (25) and a motif discovery-based collecti”
Discovering Motifs in Ranked Lists of DNA Sequences (2007, background): “We note that BS Aro8 shares some similarity with a previously reported Aro80 motif [29,32].”
New methods for separating causes from effects in genomics data (2012, background): “To overcome these challenges we focused on transcription factor-gene regulatory interactions that have been discovered on the genome wide level and experimentally verified in model organisms [20,21] and more recently in human cell lines [22].”
A barrier nucleosome model for statistical positioning of nucleosomes throughout the yeast genome. (2008, background): “For these genes, we counted the number of conserved cis-regulatory elements defined in (MacIsaac et al. 2006 ) as " c3 " (regardless of binding status) across the interval of ‫053מ‬ to ‫051מ‬ (defined as the location of the ‫1מ‬ nucleosome) and from ‫051מ‬ to +150 (defined as the location of the +1…”
Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks (2008, methodology): “We also gathered transcription factor binding site (TFBS) data derived from multiple source”
Wisdom of crowds for robust gene network inference (2012, methodology): “For S. cerevisiae, we considered several alternative gold standards derived from orthogonal datasets, namely ChIP binding data and evolutionary conserved transcription factor binding motifs 18 , as well as systematic transcription factor deletions 54 (Supplementary Note 3).”
STAMP: a web tool for exploring DNA-binding motif similarities (2007, background): “(14)], (iv) Drosophila motifs [DNase I footprinting data from (15), motifs generated by Dan Pollard], (v) DPInteract Escherichia coli motifs (16) and (vi) RegTransBase prokaryotic motifs (17).”
Learning the Structural Vocabulary of a Network (2017, methodology): “We collected 20 protein-protein interaction networks, each containing a set of S. cerevisiae proteins involved in a unique biological process, along with their induced interactions (Chatr-Aryamontri et al., 2015; MacIsaac et al., 2006).”