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This figure shows the conversion of a microarray CEL image file to a list of candidate genes, pathways, and pathway publications. The workflow makes use of a local statistical processor, services from the National Centre for Biotechnological Innovation (NCBI) and the Kyoto Ecyclopedia of Genes and Genomes (KEGG)

Sleeping sickness (or African trypanosomiasis) is an endemic disease throughout the sub-Saharan region of Africa. It is the result of infection from the trypanosome parasite, affecting a host of organisms. The inability of the agriculturally productive Boran cattle species to resist trypanosome infection is a major restriction within this region. The N’Dama species of cattle, however, has shown tolerance to infection and its subsequent disease. The low milk yields and lack of physical strength of this breed, unfortunately, limit their use in farming or meat production. A better understanding of the processes that govern the characteristics of resistance or susceptibility in different breeds of cattle will potentially lead to the development of novel therapeutic drugs or the construction of informed selective breeding programs for enhancing agricultural production.

Research conducted by the Wellcome Trust Host-Pathogen project is currently investigating the mechanisms of resistance to this parasitic infection, utilising Taverna workflows for a large-scale analysis of complex biological data (Fisher et al . 2007). The workflows in this study combine two approaches to identify candidate genes and their subsequent biological pathways: classic genetic mapping can identify chromosomal regions that contain genes involved in the expression of a trait (Quantitative Trait Loci or QTL) while transcriptomics can reveal differential gene expression levels in susceptible and resistant species.

Previous studies using the mouse as model organism identified 3 chromosomal regions statistically linked to resistance to trypanosome infection. One of these regions, the Tir1 QTL, showed the largest effect on survival. Previous investigations using this QTL identified a region shared between the mouse and cow genomes. As the scale of the data analysis task is large, researchers performing such an analysis manually would often triage their data and in this case have tended to focus on this shared region in their search for candidate genes contributing to the susceptibility to trypanosome infection. While this approach may be scientifically valid, there is a danger that candidate genes may be missed where additional biological factors may contribute to the expression of the phenotype. With a workflow, this triage of data is no longer necessary. All data can be analysed systematically, reducing the risk of missing vital information.

Researchers on the Wellcome Trust Pathogen-Host project conducted a wider analysis of the entire QTL region using a set of workflows to identify pathways whose genes lie within the chosen QTL region, and contain genes whose expression level changes. As a result of this research, a key pathway was identified whose component genes showed differential expression following infection from the trypanosome parasite. Further analysis showed that, within this pathway, the Daxx gene is located within the Tir1 QTL region and showed the strongest change in expression level. Subsequent investigations using the scientific literature highlighted the potential role of Daxx in contributing to the susceptibility to trypanosome infection. This prompted the re-sequencing of Daxx within the laboratory, leading to the identification of mutations of the gene within the susceptible mouse strains. Previous studies had failed to identify this as a candidate gene due to the premature triage of the QTL down to the syntenous region.

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Source:  OpenStax, Research in a connected world. OpenStax CNX. Nov 22, 2009 Download for free at http://cnx.org/content/col10677/1.12
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