Retrieving mutation-specific information for human proteins in UniProt/Swiss-Prot Knowledgebase.

The UniProt/Swiss-Prot Knowledgebase records about 30,500 variants in 5,664 proteins (Release 52.2). Most of these variants are manually curated single amino acid polymorphisms (SAPs) with references to the literature. In order to keep the list of published documents related to SAPs up to date, an automatic information retrieval method is developed to recover texts mentioning SAPs. The method is based on the use of regular expressions (patterns) and rules for the detection and validation of mutations. When evaluated using a corpus of 9,820 PubMed references, the precision of the retrieval was determined to be 89.5% over all variants. It was also found that the use of nonstandard mutation nomenclature and sequence positional correction is necessary to retrieve a significant number of relevant articles. The method was applied to the 5,664 proteins with variants. This was performed by first submitting a PubMed query to retrieve articles using gene or protein names and a list of mutation-related keywords; the SAP detection procedure was then used to recover relevant documents. The method was found to be efficient in retrieving new references on known polymorphisms. New references on known SAPs will be rendered accessible to the public via the Swiss-Prot variant pages.

Bridging the gap between medical and bioinformatics: an ontological case study in colon carcinoma.

Ontological principles are needed in order to bridge the gap between medical and biological information in a robust and computable fashion. This is essential in order to draw inferences across the levels of granularity which span medicine and biology, an example of which include the understanding of the roles of tumor markers in the development and progress of carcinoma. Such information integration is also important for the integration of genomics information with the information contained in the electronic patient records in such a way that real time conclusions can be drawn. In this paper, we describe a large multi-granular datasource built by using ontological principles and focusing on the case of colon carcinoma.

An ontology for carcinoma classification for clinical bioinformatics.

There are a plenty of existing classifications and staging schemes for carcinomas, one of the most frequently used being the TNM classification. Such classifications involve entities which exist at various anatomical levels of granularity and in order to apply such classifications to the Electronic Health Care Records, one needs to build ontologies which are not only based on the formal principles but also take into consideration the diversity of the domains which are involved in clinical bioinformatics. Here we outline a formal theory for addressing these issues in a way that inferences drawn upon the ontologies would be helpful in interpreting and inferring on the entities which exist at different anatomical levels of granularity. Our case study is on the colon carcinoma, one of the commonest carcinomas prevalent within the European population.