SORTALLER: predicting allergens using substantially optimized algorithm on allergen family featured peptides

Bioinformatics. 2012 Aug 15;28(16):2178-9. doi: 10.1093/bioinformatics/bts326. Epub 2012 Jun 12.


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.

Availability and implementation:

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Allergens / chemistry*
  • Allergens / immunology
  • Amino Acid Sequence
  • Peptides / chemistry*
  • Peptides / immunology
  • Sensitivity and Specificity
  • Sequence Analysis, Protein / methods*
  • Software*
  • Support Vector Machine


  • Allergens
  • Peptides