Welcome to METALoci’s documentation!
Spatially auto-correlated signals in 3D genomes.
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:
First, a genome-wide Hi-C normalized matrix is taken as input and the top interactions selected.
Second, the selected interactions are used to build a graph layout (equivalent to a physical map) using the
Kamada-Kawai algorithmwith 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.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.
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).
METALoci is meant to be use in a command line interface (CLI). Several scripts are available to run METALoci in a step-by-step fashion. Refer to the Tutorial for more information about how to use it.
The METALoci Python API is also available for more advanced users.