AI-TAC is a deep convoluional network for predicting mouse immune cell ATAC-seq signal from peak sequences using data from the Immunological Genome Project: http://www.immgen.org/.

The code was written in python v3.6.3 and pytorch v0.4.0, and run on NVIDIA P100 Pascal GPUs.

The required input is a bed file with ATAC-seq peak locations, the reference genome and a file with normalized peak heights. The code for processing raw data is in data_processing/; for example, to convert the ImmGen mouse data set to one-hot encoded sequences and save in the data directory, run:

python process_data.py "../data/ImmGenATAC1219.peak.filteredSM0.05.bed" "../data/ATAC_Data_Intensity_FilteredPeaksLogQuantile.txt" "../mm10/" "../data/"

The model can then be trained by running:

python train_test_aitac.py model_name '../data/one_hot_seqs.npy' '../data/cell_type_array.npy' '../data/peak_names.npy'

To extract first layer motifs run:

python -u extract_motifs.py model_name '../data/one_hot_seqs.npy' '../data/cell_type_array.npy' '../data/peak_names.npy'

Learning immune cell differentiation. Alexandra Maslova, Ricardo N. Ramirez, Ke Ma, Hugo Schmutz, Chendi Wang, Curtis Fox, Bernard Ng, Christophe Benoist, Sara Mostafavi, the Immunological Genome Project, bioRxiv 2019.12.21.885814; doi: https://doi.org/10.1101/2019.12.21.885814