DGEMap

Characterising gene expression patterns is a crucial part of understanding the molecular determinants of embryonic development and the role of genes in disease. However, gene expression studies in human embryos need to overcome a number of difficulties. These include the sourcing and maintenance of collections of human material suitable for gene expression studies, bridging the expertise in both biological and informatics areas, and amassing and making accessible data from multiple laboratories and studies.

The Edinburgh Mouse Atlas aims to capture in-situ gene expression patterns in a common spatial framework. In this study, we construct a grammar to define spatial regions by combinations of these patterns. Combinations are formed by applying operators to curated gene expression patterns from the atlas, thereby resembling gene interactions in a spatial context. The space of combinations is searched using an evolutionary algorithm with the objective of finding the best match to a given target pattern.

Efficiently executing large-scale, data-intensive workflows such as Montage must take into account the volume and pattern of communication. When orchestrating data-centric workflows, centralised servers common to standard workflow systems can become a bottleneck to performance. However, standards-based workflow systems that rely on centralisation, e.g., Web service based frameworks, have many other benefits such as a wide user base and sustained support.