MOTIVATION: Genomics features with similar genome-wide distributions are 
generally hypothesized to be functionally related, for example, colocalization 
of histones and transcription start sites indicate chromatin regulation of 
transcription factor activity. Therefore, statistical algorithms to perform 
spatial, genome-wide correlation among genomic features are required.
RESULTS: Here, we propose a method, StereoGene, that rapidly estimates 
genome-wide correlation among pairs of genomic features. These features may 
represent high-throughput data mapped to reference genome or sets of genomic 
annotations in that reference genome. StereoGene enables correlation of 
continuous data directly, avoiding the data binarization and subsequent data 
loss. Correlations are computed among neighboring genomic positions using kernel 
correlation. Representing the correlation as a function of the genome position, 
StereoGene outputs the local correlation track as part of the analysis. 
StereoGene also accounts for confounders such as input DNA by partial 
correlation. We apply our method to numerous comparisons of ChIP-Seq datasets 
from the Human Epigenome Atlas and FANTOM CAGE to demonstrate its wide 
applicability. We observe the changes in the correlation between epigenomic 
features across developmental trajectories of several tissue types consistent 
with known biology and find a novel spatial correlation of CAGE clusters with 
donor splice sites and with poly(A) sites. These analyses provide examples for 
the broad applicability of StereoGene for regulatory genomics.
AVAILABILITY AND IMPLEMENTATION: The StereoGene C ++ source code, program 
documentation, Galaxy integration scripts and examples are available from the 
project homepage http://stereogene.bioinf.fbb.msu.ru/.
CONTACT: favorov@sensi.org.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
online.