TFBlearner data

Data accompanying the TFBlearner manuscript. This includes: TFBlearner MultiAssayExperiment object (necessary for training and prediction). This is saved as a R serialized object ( mea_object.rds ), along with its underlying HDF5-backed assays ( *mapped.h5 files). Some files needed to reconstruct the object from scratch are also included (the sequence embeddings in sequenceEmbeddings.rds, the coordinates of the Putative Regulatory Elements in PERs.bed.gz ). The (shifted) ATAC fragments overlapping PREs across all (training and prediction) cellular contexts, prepared as (serialized) GRanges R objects (one per context) for speed, in fragments.tar. This is necessary for training, as well as for prediction on one of those contexts. The motif matches nearby PREs in motifs.tar (one GRanges rds file per motif), needed for training and prediction. The top interactors per TF (used for training and predictions). The binding predictions for all TFs across the 43 prediction cell types (see below). The GAM models to infer replicability from peaks ( reproducibilityGAM.rds ). A table of TF metadata (TF_annotation.tsv) collected from various sources (not needed for the workflow). Large supplementary tables listing all datasets used ( Supplementary_tables_* ). Binding predictions The binding predictions (in all_predictions_h5.tar.* ) are h5 files, one per TF, each containing two objects: 1) 'predictions' is a matrix of binding probabilities (with PRE regions as rows and cellular contexts as columns), and 2) 'contexts' is a vector of context codes, corresponding to columns of the 'predictions' matrix. The contexts/columns are the same across all prediction files, and are described in 'preds_h5_context_order.tsv'.To untar the predictions, simply do : cat all_predictions_h5.tar.0* | tar -xzf - Note that binding predictions are also browsable (and downloadable on TF at a time) using the web platform www.ethz-ins.org/TFBPlatform .