Metadata-Version: 2.4
Name: metaloci
Version: 1.3.5
Summary: METALoci: spatially auto-correlated signals in 3D genomes
Home-page: https://github.com/3DGenomes/METALoci/
Author: Leo Zuber, Iago Maceda, Juan Antonio Rodríguez
Author-email: Marc Martí-Renom <martirenom@cnag.eu>
License: GPL-3.0-only
Project-URL: Homepage, https://github.com/3DGenomes/METALoci/
Classifier: Programming Language :: Python :: 3.12
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Operating System :: POSIX :: Linux
Requires-Python: >=3.12
Description-Content-Type: text/markdown
License-File: LICENSE
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Dynamic: license-file


# METALoci

#### 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 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.

* 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).

#### Have a look at the [documentation](https://metaloci.readthedocs.io)!

## Install metaloci from PyPI:

```bash
conda create -n metaloci -c bioconda python==3.12 bedtools
conda activate metaloci
pip install metaloci
```
This package requires the external tool:

- bedtools (>=2.30)

Make sure it is installed and available in your PATH.

Do you have trouble compiling dependencies? Perhaps you need an upgraded version of libcurl.

In Ubuntu, you can do this with:

```bash
sudo apt install -y libcurl4-openssl-dev
```

If 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 install it with:

```bash
sudo apt install -y gawk
```

## Contributors

METALoci is currently being developed at the [MarciusLab](http://www.marciuslab.org) 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.

