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We have updated the MHC class II portion of the IEDB analysis resource _II_binding.html to reflect the progress in data accumulation and algorithm development. There are now six algorithms available to predict MHC class II epitope: the previously established ARB, SMM-align and PROPRED methods, the newly established combinatorial library and NN-align predictions, and the combined consensus approach. The ARB algorithm has been re-implemented in Python to allow better integration with the website and future development. The machine learning based approaches (ARB, NN-align and SMM-align) have been retrained with the complete dataset described in this article to provide improved performance. The collection of algorithms has also been implemented as a standalone command line application that provides identical functionality as the website. This package can be downloaded from the IEDB analysis resource along with the MHC class II binding affinity datasets, the prediction scores, and the combinatorial peptide library matrices.

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