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D2 is a fast and accurate tool for computing DNA Density and Distance to periphrery (DisTP). Previous works of genome structures mainly focused on interactions, e.g. loops, TADs or compartments. However, in our paper, we proved that DNA density and DisTP are tightly correlated with various genetic markers from multiple omics, by computing their enrichments on D2 plot, the two-dimensional density-DisTP matrix. Furthermore, we conclude a cross-species transcriptional activation model on D2 plot among human and mouse, to reveal the relationship between transciptional levels with density and DisTP. Based on this model, we introduce activation index, based on which we reveal the trajectories of functional genomic regions on D2 plot. To see the complete analysis, please check our paper.
D2.py D2 and D2.py D2s compute the DNA density and DisTP.
python D2.py D2 [options] <3dg_file> <index_file> <output>
python D2.py D2s [options] <3dg_dir> <index_file> <out_dir>
D2.py sta gives the density and DisTP ranges, and a scatter plot as below.
D2.py map puts the bins on density-DisTP matrix, and stores the probability of genomic bins appearing at matrix bins (states).
D2.py ave computes the average and standard deviation (sd) of density and DisTP.
python D2.py sta <den_dtp_dir> <index_file> <out_file>
python D2.py map [options] <den_dtp_dir> <index_file> <out_file>
python D2.py ave [options] <hist_file> <out_file>
D2.py marks indexes and concatenates the markers.
D2.py enrich plotted the enrichments of markers individually.
D2.py hiera ranks the matrix bins (states) by hierarchy cluster.
python D2.py marks <mark_dir> <index_file> <out_file>
python D2.py enrich <hist_file> <mark_idx_file> <output>
python D2.py hiera <hist_file> <mark_idx_file> <output>
D2.py gene computed the fold changes of selected genomic regions (e.g., active genes) on D2 plot.
D2.py act computed the activation index.
python D2.py gene <hist_file> <mark_idx_file> <gene_file> <out_file>
python D2.py act <hist_file> <out_file>