Machine learning reveals the diversity of human 3D chromatin contact patterns (example predictions genome wide)

Example data for the paper: Machine learning reveals the diversity of human 3D chromatin contact patterns

GitHub: https://github.com/erin-n-gilbertson/3DGenome-diversity/tree/main

biorXiv: https://www.biorxiv.org/content/10.1101/2023.12.22.573104v1.full

Manuscript accepted at Molecular Biology and Evolution

Of primary interest will be the example predictions genome wide for hg38 reference, human-archaic hominin ancestor and most divergent 1KG individual per genome along with the Jupyter notebook tutorial for making your own Akita predictions given any input 1MB sequence.

bin: contains python script for and qsub array shell script for generating example predictions. These scripts can be modified to take in any fasta files as input.

akita_predictions: contains both Akita prediction output arrays and SVG files with predicted contact maps for the hg38 reference, human-archaic hominin ancestor and most divergent 1KG individual in each of 4,873 1MB windows

anc_window_spearman.csv: spearman correlation between each 1KG individual and the ancestor for each 1MB window. To calculate 3D divergence subtract these values from 1.

basenji: basenji dir from their github, necessary in the directory to run predictions - https://github.com/calico/basenji/tree/master

genomes: fasta genomes for hg38 reference and human-archaic hominin ancestor used to make akita predictions

divergent_windows: variants and expected divergence distributions for 392 more divergent than expected windows. Defined in the manuscript as windows where 3D divergence between 1KG indiivudals and the ancestor is greater than what would be expected based on sequence divergence. See manuscript Fig. S9 for more details. 

windows.txt: 4,873 1MB genomic windows with 100% coverage in hg38 used for Akita predictions

making_examples.ipynb: jupyter notebook with tutorial instructions for making Akita predictions on any human genome sequence.