Figure 1

Illustration of the dPUC framework using Pfam to identify initial domains. A. We gather candidate domain predictions using Pfam with a permissive threshold. Domains are arranged in the x-axis by their amino acid coordinates, but the y-axis arrangement is arbitrary (there may be overlapping initial predictions). B. We build a network between candidate domains. Node weights are the normalized Pfam HMM scores of the corresponding domains (raw score minus the domain threshold). Edge weights between non-overlapping domains are set to our context scores. C. The Standard Pfam will make limited predictions, while dPUC may boost weak domains over the thresholds if they are in the correct context. The dPUC solution maximizes the sum of the node and edge weights, without overlaps, and each node must satisfy the Pfam thresholds. The final normalized domain scores are shown for each framework.