Abstract Elucidating the Transcription Factors (TFs) that drive the gene expression changes in a given experiment is one of the most common questions asked by researchers. The existing methods rely on the predicted Transcription Factor Binding Site (TFBS) to model the changes in the motif activity. Such methods only work for TFs that have a motif and assume the TF binding profile is the same in all cell types. Given the wealth of the ChIP-seq data available for a wide range of the TFs in various cell types, we propose that the modeling can be done using ChiP-seq “signatures” directly, effectively skipping the motif finding and TFBS prediction steps. We present xcore , an R package that allows Transcription Factor activity modeling based on their ChiP-seq signatures and user’s gene expression data. We also provide xcoredata a companion data package that provides a collection of preprocessed ChiP-seq signatures. We demonstrate that xcore leads to biologically relevant predictions using TGF-beta induced epithelial-mesenchymal transition and rinderpest infection time-course CAGE data as examples. Availability and Implementation xcore and xcoredata R packages are freely available at www.github.com/bkaczkowski/xcore and www.github.com/mcjmigdal/xcoredata . xcore user guide is available www.bkaczkowski.github.io/xcore/articles/xcore_vignette.html . Contact b.kaczkowski@gmail.com

xcore: an R package for inference of gene expression regulators