Allergen databases and allergen semantics

Approaches to assess IgE mediated allergy risks (sensitization and cross-reactivity) from new or modified dietary proteins

The development and introduction of new dietary protein sources has the potential to improve food supply sustainability. Understanding the potential allergenicity of these new or modified proteins is crucial to ensure protection of public health. Exposure to new proteins may result in de novo sensitization, with or without clinical allergy, or clinical reactions through cross-reactivity. In this paper we review the potential of current methodologies (in silico, in vitro degradation, in vitro IgE binding, animal models and clinical studies) to address these outcomes for risk assessment purposes for new proteins, and especially to identify and characterise the risk of sensitization for IgE mediated allergy from oral exposure. Existing tools and tests are capable of assessing potential crossreactivity. However, there are few possibilities to assess the hazard due to de novo sensitization. The only methods available are in vivo models, but many limitations exist to use them for assessing risk. We conclude that there is a need to understand which criteria adequately define allergenicity for risk assessment purposes, and from these criteria develop a more suitable battery of tests to distinguish between proteins of high and low allergenicity, which can then be applied to assess new proteins with unknown risks.

AllerML: markup language for allergens

Many concerns have been raised about the potential allergenicity of novel, recombinant proteins into food crops. Guidelines, proposed by WHO/FAO and EFSA, include the use of bioinformatics screening to assess the risk of potential allergenicity or cross-reactivities of all proteins introduced, for example, to improve nutritional value or promote crop resistance. However, there are no universally accepted standards that can be used to encode data on the biology of allergens to facilitate using data from multiple databases in this screening. Therefore, we developed AllerML a markup language for allergens to assist in the automated exchange of information between databases and in the integration of the bioinformatics tools that are used to investigate allergenicity and cross-reactivity. As proof of concept, AllerML was implemented using the Structural Database of Allergenic Proteins (SDAP; http://fermi.utmb.edu/SDAP/) database. General implementation of AllerML will promote automatic flow of validated data that will aid in allergy research and regulatory analysis.

Bioinformatics and the allergy assessment of agricultural biotechnology products: industry practices and recommendations

Bioinformatic tools are being increasingly utilized to evaluate the degree of similarity between a novel protein and known allergens within the context of a larger allergy safety assessment process. Importantly, bioinformatics is not a predictive analysis that can determine if a novel protein will ''become" an allergen, but rather a tool to assess whether the protein is a known allergen or is potentially cross-reactive with an existing allergen. Bioinformatic tools are key components of the 2009 CodexAlimentarius Commission's weight-of-evidence approach, which encompasses a variety of experimental approaches for an overall assessment of the allergenic potential of a novel protein. Bioinformatic search comparisons between novel protein sequences, as well as potential novel fusion sequences derived from the genome and transgene, and known allergens are required by all regulatory agencies that assess the safety of genetically modified (GM) products. The objective of this paper is to identify opportunities for consensus in the methods of applying bioinformatics and to outline differences that impact a consistent and reliable allergy safety assessment. The bioinformatic comparison process has some critical features, which are outlined in this paper. One of them is a curated, publicly available and well-managed database with known allergenic sequences. In this paper, the best practices, scientific value, and food safety implications of bioinformatic analyses, as they are applied to GM food crops are discussed. Recommendations for conducting bioinformatic analysis on novel food proteins for potential cross-reactivity to known allergens are also put forth.

Clinical data mining and research in the allergy office

PURPOSE OF REVIEW

More data are anticipated from the expected increase in use of electronic health records (EHRs). Upcoming initiatives require reporting of quality measures, meaningful use of clinical decision support, alert systems, and pharmacovigilance - knowledge resulting through use of EHRs. Data mining is a new tool that will help us manage information and derive knowledge from these data, and is a part of evolving new disciplines of informatics and knowledge management.

RECENT FINDINGS

Studies are reported from smaller clinic data marts to larger repositories and warehouses in various health systems, biomedical registries, and the medical literature on the Internet. Data mining technologies show promise and challenges. Outcome measures as structured data and narrative text can be mined with human assistance and newer automated natural language processing software. Despite advances, the growing diversity of clinic EHRs lack integration and interoperability with Internet-based biomedical databases.

SUMMARY

Allergists have the capability to mine clinic EHRs to discover new information, which may be hidden in charts. A central allergy computer can serve not just as a registry but also allows functionalities to enable EHRs' meaningful use. Harmonization of technological and organizational standards will allow seamless use of new natural language processing (NLP) tools and ontologies through a semantic web.