Greenscreen decreases Type I Errors and increases true peak detection in genomic datasets including ChIP-seq

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is used widely to identify both factor binding to genomic DNA and chromatin modifications. Analysis of ChIP-seq data is impacted by regions of the genome which generate ultra-high artifactual signals. To remove these signals from ChIP-seq data, ENCODE developed blacklists, comprehensive sets of regions defined by low mappability and ultra-high signals for human, mouse, worm, and flies. Currently, blacklists are not available for many model and non-model species. Here we describe an alternative approach for removing false-positive peaks we called “greenscreen”. Greenscreen is facile to implement, requires few input samples, and uses analysis tools frequently employed for ChIP-seq. We show that greenscreen removes artifact signal as effectively as blacklists in Arabidopsis and human ChIP-seq datasets while covering less of the genome, dramatically improving ChIP-seq data quality. Greenscreen filtering reveals true factor binding overlap and of occupancy changes in different genetic backgrounds or tissues. Because it is effective with as few as three inputs, greenscreen is readily adaptable for use in any species or genome build. Although developed for ChIP-seq, greenscreen also identifies artifact signals from other genomic datasets including CUT&RUN. Finally, we present an improved ChIP-seq pipeline which incorporates greenscreen, that detects more true peaks than published methods.