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B-cell epitope prediction facilitates the design and synthesis of short peptides for various immunological applications.Several algorithms have been developed to predict B-cell linear epitopes (LEs) from primary sequences of antigens,providing important information for immunobiological experiments and antibody design.This paper describes two robust methods, linear epitope prediction with/without local peak extraction (LEP-LP and LEP-NLP), based on antigenicity scale and mathematical morphology for the prediction of B-cell LEs.Previous studies revealed that LEs occur in regions with low-to-moderate antigenicity but relatively high local antigenicity scales.Hence, we developed a method adopting mathematical morphology to extract local peaks from a linear combination of the propensity scales of physico-chemical characteristics at each antigen residue.Comparison among LEP-LP/LEPNLP, BepiPred and BEPITOPE revealed that our algorithms performed better in retrieving epitopes with low-tomoderate antigenicity and achieved comparable performance according to receiver operation characteristics (ROC) curve analysis.Of the identified LEs, over 30% were unable to be predicted by existing prediction methods employing anaverage threshold of antigenicity index or default settings.Our LEP-LP method provides a bioinformatic approach for predicting B-cell LEs with low-to-moderate antigenicity.The web-based server was established at http://biotools.cs.ntou.edu.tw/lepd_antigenicity.php for free use.