Lavaburst

Chromatin domains bursting with flavor!

Let's get started! See IPython Notebook.

Optimal domain segmentation

Produce the highest scoring domain segmentation according to the model.

Marginal domain boundary probabilities

Produce the marginal probabilities for each bin edge being a domain boundary. The marginal boundary probability of a single bin edge is the total probability over all possible domain segmentations for which that bin edge serves as a border between domains. The output is a 1D array.

Marginal domain probabilities

Produce the marginal probabilities of every unique segment. The output is a 2D array where prob[a,b] describes the overall frequency of the domain spanning bin edges a and b to occur in the ensemble of all possible domain segmentations.

Domain boundary co-occurence probabilities

Produce the marginal co-occurrence probabilities of every pair of bin edges as domain boundaries. The output is a 2D array where prob[a,b] describes the overall frequency all segmentations in which both bin edges a and b occur as domain borders.

Locus co-segmentation probabilities

Produce the marginal probability of every pair of bins to co-occur within the same domain. The output is a 2D array where prob[i,j] describes the overall probability of genomic bins i and j being part of the same domain, over all possible segmentations.

Exact statistical sampling of domain segmentations

Sample individual segmentations from the probabilistic model.