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.
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.
An alignment-free method for allergenicity prediction, based on amino acid principal properties as hydrophobicity, size, relative abundance, helix and β-strand forming propensities. AllergenFP transforms proteins into descriptor-based fingerprints and compares them by Tanimoto coefficient. The algorithm was optimized in terms of lag length and resolution step and cross-validated by a set of 2427 known allergens and 2427 non-allergens. It recognized 87% of the allergens and 89% of the non-allergens. AllergenFP was compared with five freely available web servers for allergenicity prediction and showed the highest predictive ability.
Enables prediction of cross-reactivity between distantly related allergenic proteins using their X-ray or homology modeled structures in combination with epitope analysis. Cross-React uses a patch analysis, solvent accessible surface area of amino acids, and structural similarity between amino acids in the epitope region of a query allergen and allergens in the SDAP database. The search results are ranked based on the calculated Pearson correlation coefficient (PCC) between the amino acid composition in the query epitope and the accessible surface patches on the target allergens. Cross-React can be used as a predictive tool to assess protein allergenicity and cross-reactivity.
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 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.
Allows bioinformatics assessment of protein potential allergenicity by virtue of the corresponding amino acid sequences. Each such textual output incorporates a scoring figure, a confidence numeral of the assignment and information on high- or low-scoring matches to identified allergen-related motifs, including their respective location in accordingly derived allergens.
A web server that predicts the potential allergenicity of proteins. The query protein will be compared against a set of prebuilt allergenic motifs that have been obtained from 664 known allergen proteins. The query will also be compared with known allergens that do not have detectable allergenic motifs.
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.
A web server with essential tools for the assessment of predicted as well as published cross-reactivity patterns of allergens. AllerTool includes graphical representation of allergen cross-reactivity information; a local sequence comparison tool that displays information of known cross-reactive allergens; a sequence similarity search tool for assessment of cross-reactivity in accordance to FAO/WHO Codex alimentarius guidelines; and a method based on support vector machine (SVM).
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.
Predicts allergenicity of proteins from sequence-derived structural and physicochemical properties of whole proteins. APPEL is particularly useful for novel allergen proteins that are non-homologous to any known protein.
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.
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.
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.
An allergen prediction program. PREALw allows the evaluation of the potential allergenicity of protein by integrating and combining the advantages of three kinds of widespread allergen prediction methods. It integrates PREAL, FAO/WHO criteria and motif-based method by weighted average score. A web site has been developed to allow users to predict the potential allergens online using PREALw algorithm.
A fuzzy rule based system for assessing the protein for allergenicity. It combines five different modules–Machine learning classifier (MLC), Motif search, Global similarity with allergen, FAO/WHO evaluation scheme, and Global similarity with allergen like putative non-allergen (APN)– to assess the protein allergenicity.
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