SORTALLER: predicting allergens using substantially optimized algorithm on allergen family featured peptides.
SORTALLER is an online allergen classifier based on allergen family featured peptide (AFFP) dataset and normalized BLAST E-values, which establish the featured vectors for support vector machine (SVM). AFFPs are allergen-specific peptides panned from irredundant allergens and harbor perfect information with noise fragments eliminated because of their similarity to non-allergens. SORTALLER performed significantly better than other existing software and reached a perfect balance with high specificity (98.4%) and sensitivity (98.6%) for discriminating allergenic proteins from several independent datasets of protein sequences of diverse sources, also highlighting with the Matthews correlation coefficient (MCC) as high as 0.970, fast running speed and rapidly predicting a batch of amino acid sequences with a single click.


METHODS
http://sortaller.gzhmc.edu.cn/.
Plant Biotechnology Research Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200030, China.
Lida
L
Zhang
Yuyi
Y
Huang
Zehong
Z
Zou
Ying
Y
He
Ximo
X
Chen
Ailin
A
Tao
D000465:Algorithms; D000485:Allergens / Q000737:chemistry* / Q000276:immunology; D000595:Amino Acid Sequence; D010455:Peptides / Q000737:chemistry* / Q000276:immunology; D012680:Sensitivity and Specificity; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software; D060388:Support Vector Machine
D000485:Allergens; D010455:Peptides