METALoci relies on spatial autocorrelation analysis, classically employed in geostatistics, to describe how the variation of a variable depends on space at a global and local scales (e.g., identifying contamination hotspots within a city). METALoci repurposes this type of analysis to quantify spatial genome hubs of similar epigenetic properties. Briefly, the overall flowchart of METALoci consists of four steps:
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First, a genome-wide Hi-C normalized matrix is taken as input and the top interactions selected.
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Second, the selected interactions are used to build a graph layout (equivalent to a physical map) using the Kamada-Kawai algorithm with nodes representing bins in the Hi-C matrix and the 2D distance between the nodes being inversely proportional to their normalized Hi-C interaction frequency.
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Third, epigenetic/genomic signals, measured as coverage per genomic bin (e.g., ChIP-Seq signal for H3K27ac), are next mapped into the nodes of the graph layout.
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The fourth and final step involves the use of a measure of autocorrelation (specifically, the Local Moran’s I or LMI) to identify nodes and their neighborhoods with an enrichment of similar epigenetic/genomic signals.
METALoci is compatible with .cool, .mcool and .hic Hi-C formats; and with .bed signal files. The signal used in METALoci may be any numerical signal (as long as it is in a .bed file, with the location of such signal).
Have a look at the documentation!
conda create -n metaloci -c bioconda python==3.9 bedtools==2.31.1
conda activate metaloci
pip install metalociIf you are experiencing any unexpected results with METALoci (e.g. your signal after binning is 0 for every bin), we suggest to update the version of awk you are using. The recommended version is 5.1.0 or newer.
In Ubuntu, you can do this with:
sudo apt install gawkMETALoci is currently being developed at the MarciusLab by Leo Zuber, Iago Maceda and Marc A. Marti-Renom, with the original contribution of Juan Antonio Rodríguez as well as other members of the lab.