Andy Brass

A systematic approach to modeling, capturing, and disseminating proteomics experimental data

Both the generation and the analysis of proteome data are becoming increasingly widespread, and the field of proteomics is moving incrementally toward high-throughput approaches. Techniques are also increasing in complexity as the relevant technologies evolve. A standard representation of both the methods used and the data generated in proteomics experiments, analogous to that of the MIAME (minimum information about a microarray experiment) guidelines for transcriptomics, and the associated MAGE (microarray gene expression) object model and XML (extensible markup language) implementation, has yet to emerge. This hinders the handling, exchange, and dissemination of proteomics data. Here, we present a UML (unified modeling language) approach to proteomics experimental data, describe XML and SQL (structured query language) implementations of that model, and discuss capture, storage, and dissemination strategies. These make explicit what data might be most usefully captured about proteomics experiments and provide complementary routes toward the implementation of a proteome repository.

An ontology for bioinformatics applications

MOTIVATION An ontology of biological terminology provides a model of biological concepts that can be used to form a semantic framework for many data storage, retrieval and analysis tasks. Such a semantic framework could be used to underpin a range of important bioinformatics tasks, such as the querying of heterogeneous bioinformatics sources or the systematic annotation of experimental results. RESULTS This paper provides an overview of an ontology [the Transparent Access to Multiple Biological Information Sources (TAMBIS) ontology or TaO] that describes a wide range of bioinformatics concepts. The present paper describes the mechanisms used for delivering the ontology and discusses the ontologys design and organization, which are crucial for maintaining the coherence of a large collection of concepts and their relationships. AVAILABILITY The TAMBIS system, which uses a subset of the TaO described here, is accessible over the Web via http://img.cs.man.ac.uk/tambis (although in the first instance, we will use a password mechanism to limit the load on our server). The complete model is also available on the Web at the above URL.

Semantic similarity measures as tools for exploring the gene ontology.

Many bioinformatics resources hold data in the form of sequences. Often this sequence data is associated with a large amount of annotation. In many cases this data has been hard to model, and has been represented as scientific natural language, which is not readily computationally amenable. The development of the Gene Ontology provides us with a more accessible representation of some of this data. However it is not clear how this data can best be searched, or queried. Recently we have adapted information content based measures for use with the Gene Ontology (GO). In this paper we present detailed investigation of the properties of these measures, and examine various properties of GO, which may have implications for its future design.

A classification of tasks in bioinformatics

MOTIVATION This paper reports on a survey of bioinformatics tasks currently undertaken by working biologists. The aim was to find the range of tasks that need to be supported and the components needed to do this in a general query system. This enabled a set of evaluation criteria to be used to assess both the biology and mechanical nature of general query systems. RESULTS A classification of the biological content of the tasks gathered offers a checklist for those tasks (and their specialisations) that should be offered in a general bioinformatics query system. This semantic analysis was contrasted with a syntactic analysis that revealed the small number of components required to describe all bioinformatics questions. Both the range of biological tasks and syntactic task components can be seen to provide a set of bioinformatics requirements for general query systems. These requirements were used to evaluate two bioinformatics query systems.

Making sense of microarray data distributions

MOTIVATION Typical analysis of microarray data has focused on spot by spot comparisons within a single organism. Less analysis has been done on the comparison of the entire distribution of spot intensities between experiments and between organisms. RESULTS Here we show that mRNA transcription data from a wide range of organisms and measured with a range of experimental platforms show close agreement with Benfords law (Benford, PROC: Am. Phil. Soc., 78, 551-572, 1938) and Zipfs law (Zipf, The Psycho-biology of Language: an Introduction to Dynamic Philology, 1936 and Human Behaviour and the Principle of Least Effort, 1949). The distribution of the bulk of microarray spot intensities is well approximated by a log-normal with the tail of the distribution being closer to power law. The variance, sigma(2), of log spot intensity shows a positive correlation with genome size (in terms of number of genes) and is therefore relatively fixed within some range for a given organism. The measured value of sigma(2) can be significantly smaller than the expected value if the mRNA is extracted from a sample of mixed cell types. Our research demonstrates that useful biological findings may result from analyzing microarray data at the level of entire intensity distributions.

Query processing in the TAMBIS bioinformatics source integration system

Conducting bioinformatic analyses involves biologists in expressing requests over a range of highly heterogeneous information sources and software tools. Such activities are laborious, and require detailed knowledge of the data structures and call interfaces of the different sources. The TAMBIS (Transparent Access to Multiple Bioinformatics Information Sources) project seeks to make the diversity in data structures, call interfaces and locations of bioinformatics sources transparent to users. In TAMBIS, queries are expressed in terms of an ontology implemented using a description logic, and queries over the ontology are rewritten to a middleware level for execution over the diverse sources. The paper describes query processing in TAMBIS, focusing in particular on the way source-independent concepts in the ontology are related to source-dependent middleware calls, and describing how the planner identifies efficient ways of evaluating user queries.

Conceptual modelling of genomic information.

MOTIVATION Genome sequencing projects are making available complete records of the genetic make-up of organisms. These core data sets are themselves complex, and present challenges to those who seek to store, analyse and present the information. However, in addition to the sequence data, high throughput experiments are making available distinctive new data sets on protein interactions, the phenotypic consequences of gene deletions, and on the transcriptome, proteome, and metabolome. The effective description and management of such data is of considerable importance to bioinformatics in the post-genomic era. The provision of clear and intuitive models of complex information is surprisingly challenging, and this paper presents conceptual models for a range of important emerging information resources in bioinformatics. It is hoped that these can be of benefit to bioinformaticians as they attempt to integrate genetic and phenotypic data with that from genomic sequences, in order to both assign gene functions and elucidate the different pathways of gene action and interaction. RESULTS This paper presents a collection of conceptual (i.e. implementation-independent) data models for genomic data. These conceptual models are amenable to (more or less direct) implementation on different computing platforms.

The fibrillar collagens, collagen VIII, collagen X and the C1q complement proteins share a similar domain in their C-terminal non-collagenous regions

A sequence comparison of the C‐termini of collagens X, VIII, the collagen‐like complement factor C1q, and the fibrillar collagens showed a conserved cluster of aromatic residues. This conserved cluster was in a domain of approximately 130 amino acids that exhibited marked similarities in hydrophilicity profiles between the different collagens, despite a low level of sequence similarity. These data suggest that the ‘collagen X‐like family’ and the fibrillar collagens contain a domain within their C‐termini that adopts a common tertiary structure, and that a conserved cluster of aromatic residues in this domain may be involved in C‐terminal trimerization.

The Ov20 Protein of the Parasitic Nematode Onchocerca volvulus A STRUCTURALLY NOVEL CLASS OF SMALL HELIX-RICH RETINOL-BINDING PROTEINS

Ov20 is a major antigen of the parasitic nematodeOnchocerca volvulus, the causative agent of river blindness in humans, and the protein is secreted into the tissue occupied by the parasite. DNA encoding Ov20 was isolated, and the protein was expressed in Escherichia coli. Fluorescence-based ligand binding assays show that the protein contains a high affinity binding site for retinol, fluorescent fatty acids (11-((5-dimethylaminonaphthalene-1-sulfonyl)amino)undecanoic acid, dansyl-dl-α-aminocaprylic acid, and parinaric acid) and, by competition, oleic and arachidonic acids, but not cholesterol. The fluorescence emission of dansylated fatty acids is significantly blue-shifted upon binding in comparison to similarly sized β-sheet-rich mammalian retinol- and fatty acid-binding proteins. Secondary structure prediction algorithms indicate that a α-helix predominates in Ov20, possibly in a coiled coil motif, with no evidence of β structures, and this was confirmed by circular dichroism. The protein is highly stable in solution, requiring temperatures in excess of 90 °C or high denaturant concentrations for unfolding. Ov20 therefore represents a novel class of small retinol-binding protein, which appears to be confined to nematodes. The retinol binding activity of Ov20 could possibly contribute to the eye defects associated with onchocerciasis and, because there is no counterpart in mammals, represents a strategic target for chemotherapy.

PEDRo: A database for storing, searching and disseminating experimental proteomics data

BackgroundProteomics is rapidly evolving into a high-throughput technology, in which substantial and systematic studies are conducted on samples from a wide range of physiological, developmental, or pathological conditions. Reference maps from 2D gels are widely circulated. However, there is, as yet, no formally accepted standard representation to support the sharing of proteomics data, and little systematic dissemination of comprehensive proteomic data sets.ResultsThis paper describes the design, implementation and use of a P roteome E xperimental D ata R epo sitory (PEDRo), which makes comprehensive proteomics data sets available for browsing, searching and downloading. It is also serves to extend the debate on the level of detail at which proteomics data should be captured, the sorts of facilities that should be provided by proteome data management systems, and the techniques by which such facilities can be made available.ConclusionsThe PEDRo database provides access to a collection of comprehensive descriptions of experimental data sets in proteomics. Not only are these data sets interesting in and of themselves, they also provide a useful early validation of the PEDRo data model, which has served as a starting point for the ongoing standardisation activity through the Proteome Standards Initiative of the Human Proteome Organisation.