Allergenicity detection software tools | Immune system data analysis
Accurately identifying and eliminating allergens from biotechnology-derived products are important for human health. From a biomedical research perspective, it is also important to identify allergens in sequenced genomes. Many allergen prediction tools have been developed during the past years.
Allows users to make searches about both known allergens and allergen prediction. ProAP permits to search allergens by species or by categories. Users can select among the most frequently used methods and supplying individual or combination allergen prediction, in addition to the data search of known allergens, according to their own purpose. Furthermore, the tool also includes batch prediction.
The prediction of allergenic proteins is becoming very important in present time due to use of modified proteins in foods (genetically modified foods), therapeutics, bio-pharmaceuticals, etc. AlgPred has been developed for the predicting allergenic proteins and for mapping IgE epitopes on allergenic proteins.
Predicts the potential allergenicity of proteins and analyzes the key factors resulting in allergenicity. PREAL can report the putative allergenicity for a given query protein sequence. It also provides batch prediction, which returns the results by e-mail. It integrates various biochemical and physico-chemical properties, as well as sequential features and subcellular locations.
A comprehensive webtool that combines all bioinformatics approaches required to assess the allergenicity of protein sequences according to the current guidelines in the Codex. The application will be kept up to date with the FAO/WHO criteria and the SwissProt and WHO-IUIS allergen lists. It will be extended with other, supplementary methods to support and refine the prediction of allergenicity.
A bioinformatics tool for allergenicity prediction. AllerTOP is based on amino acid descriptors, accounting for residue hydrophobicity, size, abundance, helix- and β-strand forming propensities. The protein strings were transformed into uniform vectors by auto- and cross-covariance and a machine learning method using k nearest neighbours was used to classify allergens and non-allergens. The comparison between several servers for allergen prediction indicates that AllerTOP v.2 has the highest accuracy. AllerTOP v.2 offers a useful, robust, and strongly complimentary approach to allergen prediction that should provide researchers with important and persuasive new approach to identifying allergens in both existing and newly developed materials.
A cross-reactive allergen prediction program built on a combination of support vector machine (SVM) and pairwise sequence similarity. Cross-reactivity is based on similarity of proteins to allergens. However, not all proteins with similar sequence or structure to known allergens are cross-reactive allergens. AllerHunter aims to predict allergens and non-allergens with high sensitivity and specificity, without compromising efficiency at classification of proteins with similar sequence to known allergens.
A fast and accurate sequence-based allergen prediction tool that models protein sequences as text documents and uses support vector machine in text classification for allergen prediction. Test results on multiple highly skewed datasets demonstrated that Allerdictor predicted allergens with high precision over high recall at fast speed. For example, Allerdictor only took approximately 6 min on a single core PC to scan a whole Swiss-Prot database of approximately 540 000 sequences and identified <1% of them as allergens.