The ForeSee (4C) approach for integrative analysis in gene discovery.

The development of high-throughput experimental techniques has made measurements for virtually all kinds of cellular components possible. Effective integration and analysis of this diverged information to produce insightful knowledge is central to biological study today. In this chapter, we present a methodology for building integrative analytical workbenches using the workflow technology. We focus on the field of gene discovery through the combined study of transcriptomics, genomics and epigenomics, although the methodology is generally applicable to any omics-data analysis for biomarker discovery. We illustrate the application of the methodology by presenting our study on the identification of aberrant genomic regions, genes and/or their regulatory elements with their implications for breast cancer research. We also discuss the challenges and opportunities brought by the latest development of the next generation sequencing technology.

Solutions for complex, multi data type and multi tool analysis: principles and applications of using workflow and pipelining methods.

Analytical workflow technology, sometimes also called data pipelining, is the fundamental component that provides the scalable analytical middleware that can be used to enable the rapid building and deployment of an analytical application. Analytical workflows enable researchers, analysts and informaticians to integrate and access data and tools from structured and non-structured data sources so that analytics can bridge different silos of information; compose multiple analytical methods and data transformations without coding; rapidly develop applications and solutions by visually constructing analytical workflows that are easy to revise should the requirements change; access domain-specific extensions for specific projects or areas, for example, text extraction, visualisation, reporting, genetics, cheminformatics, bioinformatics and patient-based analytics; automatically deploy workflows directly into web portals and as web services to be part of a service-oriented architecture (SOA). By performing workflow building, using a middleware layer for data integration, it is a relatively simple exercise to visually design an analytical process for data analysis and then publish this as a service to a web browser. All this is encapsulated into what can be referred to as an 'Embedded Analytics' methodology which will be described here with examples covering different scientifically focused data analysis problems.

Addressing informatics challenges in Translational Research with workflow technology.

Interest in Translational Research has been growing rapidly in recent years. In this collision of different data, technologies and cultures lie tremendous opportunities for the advancement of science and business for organisations that are able to integrate, analyse and deliver this information effectively to users. Workflow-based integration and analysis systems are becoming recognised as a fast and flexible way to build applications that are tailored to scientific areas, yet are built on a common platform. Workflow systems are allowing organisations to meet the key informatics challenges in Translational Research and improve disease understanding and patient care.

Ab initio modelling of the N-terminal domain of the secretin receptors.

G protein coupled receptors of the secretin family are activated by peptide hormones of about 30 residues in length. There is considerable sequence homology within both the hormone and receptor families. The receptors possess in addition to the integral membrane domain a characteristic extracellular domain of about 120 residues in length, having conserved cysteine residues, which are involved in disulphide bridge formation, and tryptophanes, which have been shown to be critical for hormone binding. This extracellular domain does not have detectable homology to any known protein fold. In order to be able to propose a structure for this domain we have used ab initio prediction methods combined with constraints based on experimental results for the disulphide connectivity. The results of computational tools for predicting secondary structure and accessibility, together with ligand binding and mutational data and other structural considerations were used in the ab initio protein folding programs DRAGON and GADGET and also the simpler program RAMBLE, which was able to explore different permutations of disulphide bond connectivity, tryptophan side chain orientation and chain topology. The methods generated a limited number of plausible models but no single unique solution was found under the constraints. One of these was refined into a full atomic model that contained a possible peptide binding site comprising the most conserved residues.