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Bioinformatics. 2017 Oct 15;33(20):3158-3165. doi: 10.1093/bioinformatics/btx379.

StereoGene: rapid estimation of genome-wide correlation of continuous or interval feature data.

Author information

Department of Bioengineering and Bioinformatics, Moscow State University, Moscow 119992, Russia.
Institute for Information Transmission Problems, RAS, Moscow 127994, Russia.
Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Laboratory of Systems Biology and Computational Genetics, Vavilov Institute of General Genetics, RAS, Moscow 119333, Russia.
Laboratory of Bioinformatics, Research Institute of Genetics and Selection of Industrial Microorganisms, Moscow 117545, Russia.



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.


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


Supplementary information:

Supplementary data are available at Bioinformatics online.

[Indexed for MEDLINE]
Free PMC Article

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