Nico Adams

The Chemical Information Ontology: Provenance and Disambiguation for Chemical Data on the Biological Semantic Web

Cheminformatics is the application of informatics techniques to solve chemical problems in silico. There are many areas in biology where cheminformatics plays an important role in computational research, including metabolism, proteomics, and systems biology. One critical aspect in the application of cheminformatics in these fields is the accurate exchange of data, which is increasingly accomplished through the use of ontologies. Ontologies are formal representations of objects and their properties using a logic-based ontology language. Many such ontologies are currently being developed to represent objects across all the domains of science. Ontologies enable the definition, classification, and support for querying objects in a particular domain, enabling intelligent computer applications to be built which support the work of scientists both within the domain of interest and across interrelated neighbouring domains. Modern chemical research relies on computational techniques to filter and organise data to maximise research productivity. The objects which are manipulated in these algorithms and procedures, as well as the algorithms and procedures themselves, enjoy a kind of virtual life within computers. We will call these information entities. Here, we describe our work in developing an ontology of chemical information entities, with a primary focus on data-driven research and the integration of calculated properties (descriptors) of chemical entities within a semantic web context. Our ontology distinguishes algorithmic, or procedural information from declarative, or factual information, and renders of particular importance the annotation of provenance to calculated data. The Chemical Information Ontology is being developed as an open collaborative project. More details, together with a downloadable OWL file, are available at http://code.google.com/p/semanticchemistry/ (license: CC-BY-SA).

ChemicalTagger: A tool for semantic text-mining in chemistry

BackgroundThe primary method for scientific communication is in the form of published scientific articles and theses which use natural language combined with domain-specific terminology. As such, they contain free owing unstructured text. Given the usefulness of data extraction from unstructured literature, we aim to show how this can be achieved for the discipline of chemistry. The highly formulaic style of writing most chemists adopt make their contributions well suited to high-throughput Natural Language Processing (NLP) approaches.ResultsWe have developed the ChemicalTagger parser as a medium-depth, phrase-based semantic NLP tool for the language of chemical experiments. Tagging is based on a modular architecture and uses a combination of OSCAR, domain-specific regex and English taggers to identify parts-of-speech. The ANTLR grammar is used to structure this into tree-based phrases. Using a metric that allows for overlapping annotations, we achieved machine-annotator agreements of 88.9% for phrase recognition and 91.9% for phrase-type identification (Action names).ConclusionsIt is possible parse to chemical experimental text using rule-based techniques in conjunction with a formal grammar parser. ChemicalTagger has been deployed for over 10,000 patents and has identified solvents from their linguistic context with >99.5% precision.

A versatile platform for comprehensive chip‐based explorative cytometry

Analysis of the immense complexity of the immune system is increasingly hampered by technical limitations of current methodologies, especially for multiparameter‐ and functional analysis of samples containing small numbers of cells. We here present a method, which is based on the stepwise functional manipulation and analysis of living immune cells that are self‐immobilized within microfluidic chips using automated epifluorescence microscopy overcoming current limitations for comprehensive immunophenotyping. Crossvalidation with flow cytometry revealed a 10‐fold increased sensitivity and a comparable specificity. By using small sample volumes and cell numbers (2–10 μl, down to 20,000 cells), we were able to analyze a virtually unlimited number of intracellular and surface markers even on living immune cells. We exemplify the scientific and diagnostic potential of this method by (1) identification and phenotyping of rare cells, (2) comprehensive analysis of very limited sample volume, and (3) deep immunophenotyping of human B‐cells after in vitro differentiation. Finally, we propose an informatic model for annotation and comparison of cytometric data by using an ontology‐based approach. The chip‐based cytometry introduced here turned out to be a very useful tool to enable a stepwise exploration of precious, small cell‐containing samples with an virtually unlimited number of surface‐ and intracellular markers.

Dovetailing biology and chemistry: integrating the Gene Ontology with the ChEBI chemical ontology

BackgroundThe Gene Ontology (GO) facilitates the description of the action of gene products in a biological context. Many GO terms refer to chemical entities that participate in biological processes. To facilitate accurate and consistent systems-wide biological representation, it is necessary to integrate the chemical view of these entities with the biological view of GO functions and processes. We describe a collaborative effort between the GO and the Chemical Entities of Biological Interest (ChEBI) ontology developers to ensure that the representation of chemicals in the GO is both internally consistent and in alignment with the chemical expertise captured in ChEBI.ResultsWe have examined and integrated the ChEBI structural hierarchy into the GO resource through computationally-assisted manual curation of both GO and ChEBI. Our work has resulted in the creation of computable definitions of GO terms that contain fully defined semantic relationships to corresponding chemical terms in ChEBI.ConclusionsThe set of logical definitions using both the GO and ChEBI has already been used to automate aspects of GO development and has the potential to allow the integration of data across the domains of biology and chemistry. These logical definitions are available as an extended version of the ontology from http://purl.obolibrary.org/obo/go/extensions/go-plus.owl.

A Database for Chemical Proteomics: ChEBI

Chemical proteomics is concerned with the identification of protein targets interacting with small molecules. Hence, the availability of a high quality and free resource storing small molecules is essential for the future development of the field. The Chemical Entities of Biological Interest (ChEBI) database is one such database. The scope of ChEBI includes any constitutionally or isotopically distinct atom, molecule, ion, ion pair, radical, radical ion, complex, conformer, etc., identifiable as a separately distinguishable entity. These entities in question are either products of nature or synthetic products used to intervene in the processes of living organisms. In addition, ChEBI contains a chemical ontology which relates the small molecules with each other thereby making it easier for users to discover data. The ontology also describes the biological roles that the small molecules are active in. The ChEBI database also provides a central reference point in which to access a variety of bioinformatics data points such as pathways and their biochemical reactions; expression data; protein sequence and structures.

Chemical Markup, XML and the World-Wide Web. 8. Polymer Markup Language.

Polymers are among the most important classes of materials but are only inadequately supported by modern informatics. The paper discusses the reasons why polymer informatics is considerably more challenging than small molecule informatics and develops a vision for the computer-aided design of polymers, based on modern semantic web technologies. The paper then discusses the development of Polymer Markup Language (PML). PML is an extensible language, designed to support the (structural) representation of polymers and polymer-related information. PML closely interoperates with Chemical Markup Language (CML) and overcomes a number of the previously identified challenges.

PIDO: The Primary Immunodeficiency Disease Ontology

MOTIVATION Primary immunodeficiency diseases (PIDs) are Mendelian conditions of high phenotypic complexity and low incidence. They usually manifest in toddlers and infants, although they can also occur much later in life. Information about PIDs is often widely scattered throughout the clinical as well as the research literature and hard to find for both generalists as well as experienced clinicians. Semantic Web technologies coupled to clinical information systems can go some way toward addressing this problem. Ontologies are a central component of such a system, containing and centralizing knowledge about primary immunodeficiencies in both a human- and computer-comprehensible form. The development of an ontology of PIDs is therefore a central step toward developing informatics tools, which can support the clinician in the diagnosis and treatment of these diseases. RESULTS We present PIDO, the primary immunodeficiency disease ontology. PIDO characterizes PIDs in terms of the phenotypes commonly observed by clinicians during a diagnosis process. Phenotype terms in PIDO are formally defined using complex definitions based on qualities, functions, processes and structures. We provide mappings to biomedical reference ontologies to ensure interoperability with ontologies in other domains. Based on PIDO, we developed the PIDFinder, an ontology-driven software prototype that can facilitate clinical decision support. PIDO connects immunological knowledge across resources within a common framework and thereby enables translational research and the development of medical applications for the domain of immunology and primary immunodeficiency diseases.

Predicting thermochemical parameters of oxygen-containing heterocycles using simple QSPR models

Quantitative structure–property relationships for the prediction of standard enthalpies and entropies of formation as well as standard molar heat capacities for small oxygen heterocyclic compounds were developed, using 1D, 2D and 3D descriptors and experimental or computed thermochemical data. To develop the models, the data set was split into test and training sets using D-optimal experimental design to generate a diverse training set. Internal (R 2 cross-validated = 0.898 − 0.998) and external (R 2 cross-validated = 0.847 − 0.996) validation showed the models to be both stable and highly predictive. Enthalpies of formation were best described by electrotopological, atomic composition and molecular refractivity descriptors, while Kier and Hall χ and κ descriptors as well as the number of rotatable bonds appear frequently in models describing the entropy of formation of these compounds. Heat capacity models often feature the molecular area descriptor as well as the Kier and Hall 0χ descriptor and the number of methyl groups present in the molecule.

Chemical ontologies: what are they, what are they for and what are the challenges

Ontologies encode human knowledge in computationally accessible forms. They are designed to narrow the gap between the knowledge of human experts and the functionality available in computer systems, by expressing expert knowledge in a manner computers can manipulate and reason over. With the ever-growing deluge of data in modern scientific domains, researchers need intelligent tools able to filter out irrelevant and automatically organise relevant information into meaningful categories. The Chemoinformatics and Metabolism team at the EBI is developing chemical ontologies for structure-based chemical classification, role or bioactivity-based chemical classification, and chemical information entities such as descriptors and algorithms. Our ontologies provide collections of names and synonyms which are useful for text mining, stable identifiers which are essential to semantic integration of data, and a semantically rich encoding of many aspects of the chemical domain. But for such ontologies to be maximally useful for diverse users and interoperable with other ontologies in the scientific domain, similar-sounding things have to be disentangled in our language and our ontology. Recent work addresses the distinguishing of structures from chemicals [1], and of bioactivity from drug uses [2]. Ontologies are backed by logical formalisms such as the Web Ontology Language, OWL. One of the challenges of chemical ontology is representing complex chemical structures in the underlying formalism. Cyclic structures prove particularly challenging for logicbased representation. Recent research in our group investigated the inclusion of chemical graphs in OWL [3]. Integrating chemoinformatics tools with chemical ontology is the subject of ongoing research.

Towards Lensfield - Data Management, Processing and Semantic Publication for Vernacular e-Science

Lensfield is a desktop and filesystem-based tool designed as a “personal data management assistant” for the scientist. It combines distributed version control (DVCS), software transaction memory (STM) and linked open data (LOD) publishing to create a novel data management, processing and publication tool. The application “just looks after” these technologies for the scientist, providing simple interfaces for typical uses. It is built with Clojure and includes macros which define steps in a common workflow. Functions and Java libraries provide facilities for automatic processing of data which is ultimately published as RDF in a web application. The progress of data processing is tracked by a fine-grained data structure that can be serialized to disk, with the potential to include manual steps and programmatic interrupts in largely automated processes through seamless resumption. Flexibility in operation and minimizing barriers to adoption are major design features.