![]() More rigorously removes background ChIP-seq noise by using a large number of publicly available A single control dataset may not capture all sources of background noise. (b) An overview of the AIControlĪpproach. Then used to identify binding activities across the genome. (bottom) The learned fine scale Poisson (background) distributions are (right) Other peak calling algorithms use only oneĬontrol dataset, so they must use a broader region (typically within 5,000-10,000 bps) to estimateīackground distributions. Learns appropriate combinations of publicly available control ChIP-seq datasets to impute background Here is an overview of the AIControl framework from our paper.įigure 1: (a) Comparison of AIControl to other peak calling algorithms. AIControl makes ChIP-seq assays easier, cheaper, and more accurate by imputing background data from a massive amount of publicly available control data.
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