ChIP-Seq Binding Dataset
We cloned 206 (of the estimated 214) DNA binding genes into an anhydrotetracycline -inducible Gateway shuttle vector to contain an N- or C-terminal FLAG epitope tag.Once transformed, we cultured MTB strains to a uniform growth stage and induced expression of the gene-of-interest for 18 hours – approximately one cell division. We then harvested chromatin samples for ChIP-seq as well as total RNA for high-density transcriptional profiling by custom tiled microarray.
ChIP samples were sequenced, and a custom algorithm performed read alignment and ChIP peak calling (Methods). To determine significance thresholds for peak inclusion we generated a ChIP-seq negative control compendium consisting of 10 different sequencing data sets. Because no single control captures all known or potential ChIP artifacts we included several control samples, including: wild-type H37Rv chromatin immunoprecipitated with and without anti-FLAG antibody, chromatin samples from uninduced expression-vector bearing cells immunopreciptated with and without anti-FLAG antibody, as well as chromatin samples from induced non-TF genes immunoprecipitated with anti-FLAG antibody. We subjected each control data set to peak calling, creating a negative control peak set that contained ~2000 scored final peaks. We then compared each experimental peak with this negative control peak set to define a collection of pass-filter DNA binding events (Methods). This approach identified both global and local binding patterns with associated significance scores for every TF assayed.
For more details see Minch et al. 2015, Nat Commun.ChIP-seq Profiles
You can visualize ChIP-seq profiles for a given experiment in UCSC Genome Browser by clicking on the browser icon
Displaying 1 - 10 of 261MTB TF binding locations from ChIP-seq experiments (Turkarslan et al., submitted)
Table summarizing TF binding locations for target genes from ChIP-seq experiments, expression levels of these genes in the corresponding TF overexpression tiling array experiments and overlap with regulatory network model (Source figshare).
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