WebAllergen: a web server for predicting allergenic proteins

Riaz, Tariq; Hor, Hen Ley; Krishnan, Arun V.; Tang, Fenglin; Li, Kuo-Bin

doi:10.1093/bioinformatics/bti356

Cited by 29 publications

(18 citation statements)

References 10 publications

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“…The performance PSSM based SVM model was also compared to AlgPred [4] and WebAllergen [5,6] models (see Figure 1c). We submitted an independent dataset of 97 allergens and equal number of non allergens to these models.…”

Section: Discussionmentioning

confidence: 99%

An SVM method using evolutionary information for the identification of allergenic proteins

Kumar¹,

Shelokar²

2008

Bioinformation

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Abstract:This study presents an allergenic protein prediction system that appears to be capable of producing high sensitivity and specificity. The proposed system is based on support vector machine (SVM) using evolutionary information in the form of an amino acid position specific scoring matrix (PSSM). The performance of this system is assessed by a 10-fold crossvalidation experiment using a dataset consisting of 693 allergens and 1041 non-allergens obtained from Swiss-Prot and Structural Database of Allergenic Proteins (SDAP). The PSSM method produced an accuracy of 90.1% in comparison to the methods based on SVM using amino acid, dipeptide composition, pseudo (5-tier) amino acid composition that achieved an accuracy of 86.3, 86.5 and 82.1% respectively. The results show that evolutionary information can be useful to build more effective and efficient allergen prediction systems.

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Section: Discussionmentioning

confidence: 99%

An SVM method using evolutionary information for the identification of allergenic proteins

Kumar¹,

Shelokar²

2008

Bioinformation

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“…According to the FAO/WHO method 7, AllergenPro provide the function to find the query protein for an 80 amino acids sliding window by a FASTA alignment program. Motif-based prediction method [6] provides the function to compare the two sequences with the specific profiles between the conserved motifs and the unique sequences. If specific protein is predicted to be allergenic, query result will be displayed to compare against a set of well-known allergenic motifs.…”

Section: Methodsmentioning

confidence: 99%

AllergenPro: an integrated database for allergenicity analysis and prediction

et al. 2014

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The National Agricultural Biotechnology Information Center (NABIC) reconstructed an AllergenPro database for allergenic proteins analysis and allergenicity prediction. The AllergenPro is an integrated web-based system providing information about allergen in foods, microorganisms, animals and plants. The allergen database has the three main features namely, (1) allergen list with epitopes, (2) searching of allergen using keyword, and (3) methods for allergenicity prediction. This updated AllergenPro outputs the search based allergen information through a user-friendly web interface, and users can run tools for allergenicity prediction using three different methods namely, (1) FAO/WHO, (2) motif-based and (3) epitope-based methods. AvailabilityThe database is available for free at http://nabic.rda.go.kr/allergen/

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“…This method employs MEME motifs of a length of 50 residues for the prediction of allergenicity by using pairwise sequence alignment with certain threshold. WebAllergen [72] is a web server for the prediction of allergenic proteins which is also based on specific detectable allergenic motifs in known allergens [73]. Furthermore, a study carried out by Kong et al showed that an approach based on search of multiple motifs is more specific and efficient than the conventional single motif search [74].…”

Section: Motif-based Approachesmentioning

confidence: 99%

Databases and Algorithms in Allergen Informatics

Kadam¹,

Sawant²,

Jayaraman³

et al. 2016

Bioinformatics - Updated Features and Applications

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Allergic diseases are considered as one of the major health problems worldwide due to their increasing prevalence. Advancements in genomic, proteomic, and analytical techniques have resulted in considerable progress in the field of allergology, which has led to accumulation of huge amount of data. Allergen bioinformatics comprises allergenrelated data resources and computational methods/tools, which deal with an efficient archival, management, and analysis of allergological data. Significant work has been done in the area of allergen bioinformatics that has proven pivotal for the development and progress of this field. In this chapter, we describe the current status of databases and algorithms, encompassing the field of allergen bioinformatics by examining work carried out thus far with respect to features such as allergens and allergenicity, allergen databases, algorithms/tools for allergen/allergenicity prediction, allergen epitope prediction, and allergenic cross-reactivity assessment. This chapter illustrates concepts and algorithms in allergen bioinformatics, as well as it outlines the key areas for potential development in allergology field.

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WebAllergen: a web server for predicting allergenic proteins

Abstract: http://weballergen.bii.a-star.edu.sg/

Cited by 29 publications

References 10 publications

An SVM method using evolutionary information for the identification of allergenic proteins

An SVM method using evolutionary information for the identification of allergenic proteins

AllergenPro: an integrated database for allergenicity analysis and prediction

Databases and Algorithms in Allergen Informatics

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