Introduction

Poster at BioNLP 2010: Ontology-Based Extraction and Summarization of Protein Mutation Impact InformationNLP methods for extracting mutation information from the bibliome have become an important new research area within bio-NLP, as manually curated databases, like the Protein Mutant Database (PMD) (Kawabata et al., 1999), cannot keep up with the rapid pace of mutation research. However, while significant progress has been made with respect to mutation detection, the automated extraction of the impacts of these mutations has so far not been targeted. In this paper, we describe the first work to automatically summarize impact information from protein mutations. Our approach is based on populating an OWL-DL ontology with impact information, which can then be queried to provide structured information, including a summary.

Introduction

The FungalWeb Ontology is a knowledge representation vehicle designed to integrate information relevant to industrial applications of enzymes. The ontology integrates information from established sources and supports complex queries to the instantiated FungalWeb knowledge base. The ontology represents prototype Semantic Web technology customized to the domain of industrial enzymes with a focus on enzyme discovery, commercial enzyme products and vendors, and the industrial applications and benefits of industrial enzymes. Using a series of application scenarios we demonstrate the utility of this 'Semantic Web' infrastructure to the enzyme biotechnologist.

Abstract

Text Mining in biology and biomedicine requires a large amount of domain-specific knowledge. Publicly accessible resources hold much of the information needed, yet their practical integration into natural language processing (NLP) systems is fraught with manifold hurdles, especially the problem of semantic disconnectedness throughout the various resources and components. Ontologies can provide the necessary framework for a consistent semantic integration, while additionally delivering formal reasoning capabilities to NLP.

In this chapter, we address four important aspects relating to the integration of ontology and NLP: (i) An analysis of the different integration alternatives and their respective vantages; (ii) The design requirements for an ontology supporting NLP tasks; (iii) Creation and initialization of an ontology using publicly available tools and databases; and (iv) The connection of common NLP tasks with an ontology, including technical aspects of ontology deployment in a text mining framework. A concrete application example—text mining of enzyme mutations—is provided to motivate and illustrate these points.

Keywords: Text Mining, NLP, Ontology Design, Ontology Population, Ontological NLP

Abstract

The biomedical literature is growing at an ever-increasing rate, which pronounces the need to support scientists with advanced, automated means of accessing knowledge. We investigate a novel approach employing description logics (DL)-based queries made to formal ontologies that have been created using the results of text mining full-text research papers. In this paradigm, an OWL-DL ontology becomes populated with instances detected through natural language processing (NLP). The generated ontology can be queried by biologists using DL reasoners or integrated into bioinformatics workflows for further automated analyses. We demonstrate the feasibility of this approach with a system targeting the protein mutation literature.

Keywords: text mining; semantic web; ontological NLP; protein mutations; automated reasoning in bioinformatics; querying OWL-DL ontologies; description logics.

Abstract

The development of text analysis systems targeting the extraction of information about mutations from research publications is an emergent topic in biomedical research. Current systems differ in both scope and approaches, which prevents a meaningful comparison of their performance and therefore possible synergies. To overcome this "evaluation bottleneck," we developed a comprehensive framework for the systematic analysis of mutation extraction systems, precisely defining tasks and corresponding evaluation metrics that will allow a comparison of existing and future applications.

Keywords: mutation extraction systems; mutation evaluation tasks; mutation evaluation metrics

Abstract

Protein structure visualization tools render images that allow the user to explore structural features of a protein. Context specific information relating to a particular protein or protein family is not easily integrated and must be uploaded from databases or provided through manual curation of input files. We describe a mixed natural language processing and sequence analysis based approach for the retrieval of mutation specific annotations from full text articles for rendering with protein structures.

Keywords

Text Mining, Protein Structure Annotation, Protein Function, ProSAT, Xylanase

Abstract

A large amount of biological knowledge today is only available from full-text research papers. Since neither manual database curators nor users can keep up with the rapidly expanding volume of scientific literature, natural language processing approaches are becoming increasingly important for bioinformatic projects.

In this paper, we go beyond simply extracting information from full-text articles by describing an architecture that supports targeted access to information from biological databases using the results derived from text mining of research papers, thereby integrating information from both sources within a biological application.

The described architecture is currently being used to extract information about protein mutations from full-text research papers. Text mining results drive the retrieval of sequence information from protein databases and the employment of algorithmic sequence analysis tools, which facilitate further data access from protein structure databases. Complex mapping of NLP derived text annotations to protein structures allows the rendering, with 3D structure visualization, of information not available in databases of mutation annotations.

Abstract

The FungalWeb Ontology aims to support the data integration needs of enzyme biotechnology from inception to product roll out. Serving as a knowledge base for decision support, the conceptualization seeks to link fungal species with enzymes, enzyme substrates, enzyme classifications, enzyme modifications, enzyme retail and applications. We demonstrate how the FungalWeb Ontology supports this remit by presenting application scenarios, conceptualizations of the ontological frame able to support these scenarios and semantic queries typical of a Biotech Manager. Queries to the knowledge base are answered with description logic (DL) and automated reasoning tools.

Abstract

Protein structure visualization tools render images that allow the user to explore structural features of a protein. Context specific information relating to a particular protein or protein family is, however, not easily integrated and must be uploaded from databases or provided through manual curation of input files. Protein Engineers spend considerable time iteratively reviewing both literature and protein structure visualizations manually annotated with mutated residues. Meanwhile, text mining tools are increasingly used to extract specific units of raw text from scientific literature and have demonstrated the potential to support the activities of Protein Engineers.

The transfer of mutation specific raw-text annotations to protein structures requires integrated data processing pipelines that can co-ordinate information retrieval, information extraction, protein sequence retrieval, sequence alignment and mutant residue mapping. We describe the Mutation Miner pipeline designed for this purpose and present case study evaluations of the key steps in the process. Starting with literature about mutations made to protein families; haloalkane dehalogenase, bi-phenyl dioxygenase, and xylanase we enumerate relevant documents available for text mining analysis, the available electronic formats, and the number of mutations made to a given protein family. We review the efficiency of NLP driven protein sequence retrieval from databases and report on the effectiveness of Mutation Miner in mapping annotations to protein structure visualizations. We highlight the feasibility and practicability of the approach.

Keywords

Text mining - Protein structure annotation - Protein mutation - Data mining - Haloalkane dehalogenase - Biphenyl dioxygenase - Xylanase

Abstract

Protein structure visualization tools render images that allow the user to explore structural features of a protein. Context specific information relating to a particular protein or protein family is not easily integrated and must be uploaded from databases or provided through manual curation of input files. We describe a mixed natural language processing and protein sequence analysis approach for the retrieval of mutation specific annotations from full text articles for rendering with protein structures.