Domain Prediction Using Context
The dPUC2 (Domain Prediction Using Context) software improves domain prediction by considering every domain in the context of other domains, rather than independently as standard approaches do. Our framework maximized the probability of the data under an approximation, which reduces to a pairwise context problem, and we have shown that our probabilistic method is indeed more powerful than competing methods.
ProtDomain allows you to use dPUC2 as a Domain Finder for your sequences. dPUC2 is developed and maintained as a Perl application by Alejandro Ochoa García, an Assistant Professor at Duke University, and a former student at SinghLab. To directly install and use dPUC2, please refer to the GitHub repository for dPUC2 which also serves as the software's manual.
2017-04-12. Alejandro Ochoa, Mona Singh. Domain prediction with probabilistic directional context. Bioinf 33(16) 2471-2478. Article, bioRxiv 2016-12-14.
2015-11-17. Alejandro Ochoa, John D Storey, Manuel Llinás, and Mona Singh. Beyond the E-value: stratified statistics for protein domain prediction. PLoS Comput Biol. 11 e1004509. Article, arXiv 2014-09-23.
2011-03-31. Alejandro Ochoa, Manuel Llinás, and Mona Singh. Using context to improve protein domain identification. BMC Bioinformatics, 12:90. Article.