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WebAllergen: a web-based database for protein allergenicity prediction

Authors:

So Youn Won,

National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA), KR
About So Youn
Genomics Division
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Jeong-Ho Baek,

National Institute of Agricultural Sciences (NAS), KR
About Jeong-Ho
Gene Engineering Division
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Jae-Hyeon Oh,

National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA), KR
About Jae-Hyeon
Genomics Division
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Gang-Seob Lee,

National Institute of Agricultural Sciences (NAS), KR
About Gang-Seob
Biosafety Division
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Yong-Hwan Kim,

Dankook Universit, KR
About Yong-Hwan
Department of Crop Science and Biotechnology
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Chang-Kug Kim

National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA), KR
About Chang-Kug
Genomics Division
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Abstract

Allergies are an important health problem. In the present study, a web-based allergen platform was developed with an allergen database and allergenicity prediction functions. Drawing from the literature and public databases, 2,939 allergens were identified and categorised according to their origin and known information. This platform provides a function to search allergenic proteins through formats such as keywords, FASTA, BLAST, and provides sequence-based, motif-based and epitope-based methods for allergenicity prediction. Using specific sequence or allergen predictions, the user can find summarised allergen information to link the UniProtKB and the PDB databases.

How to Cite: Won, S.Y., Baek, J.-H., Oh, J.-H., Lee, G.-S., Kim, Y.-H. and Kim, C.-K., 2018. WebAllergen: a web-based database for protein allergenicity prediction. Journal of the National Science Foundation of Sri Lanka, 46(2), pp.233–236. DOI: http://doi.org/10.4038/jnsfsr.v46i2.8424
Published on 30 Jun 2018.
Peer Reviewed

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