Genome-Wide Uncertainty-Moderated Extraction of Signal Annotations from Multi-Sample Functional Genomics Data.

We present Consenrich, a simple but principled technique for genome-wide estimation of signals hidden in noisy multi-sample sequencing-based functional genomics datasets. Consenrich appeals to a sequential prediction-correction framework and models both the spatial dependencies between proximal loci and regional, sample-specific noise processes that corrupt sequencing data. Experiments reveal distinct improvement compared to benchmarks in a series of challenging estimation problems, where noisy functional genomics data samples must be reconciled. We further highlight the immediate practical appeal of this refined signal extraction for differential analyses between disease conditions and identification of functionally enriched genomic regions. A complete implementation of Consenrich is hosted at https://github.com/nolan-h-hamilton/Consenrich.