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About UsPrevious and current researchThe Functional Genomics team is working in four main directions:
Our group was among the first to use microarray data to study transcription regulation mechanisms on a genomic scale (Brazma et al., 1998). In 1999 we realised the importance of standards in microarray data reporting (Brazma et al., 2000, Brazma et al., 2001) and began work to establish the ArrayExpress database. As of February 2009, the ArrayExpress Archive holds data from approximately 200,000 microarrays. The ArrayExpress Atlas of Gene Expression allows the users to query for expression profiles of particular genes, tissues or disease states across multiple experiments. Our PhD students and postdocs focus mostly on integrative data analysis and on building models for systems biology (e.g., Rustici et al., 2004, Schlitt & Brazma, 2006). Future projects and goalsA biological system, such as a cell, tissue, organ or organism, can be in many different states, such as developmental stages, disease states, or physiological states. Different cell types can be considered as different biological states evolving from the progenitor cell state. This poses many questions; how many different biological states are there, what are the relationships between them, which tissue or cell types aremore similar to each other and which are different, how is the biological state affected by a disease, how much does gene expression depends on environment, and how much on genotype? Finding answers to these questions is one of the most important goals of our group’s research. Towards this goal we are building a comprehensive gene expression atlas for human and model organisms. The Gene Expression Atlas integrates data from tens of thousands of transcriptomics assays available in ArrayExpress. We will also continue large collaborative projects, such as integration of transcriptomics, proteomics and human genome variation data to understand the molecular mechanisms of disease, as well as building biomedical data analysis infrastructure to help us in answering these questions. 
Visualisation of relationship transcriptomes of ~5,300 human samples categorised in 15 biological classes using Neighbor Retrieval Visualizer (NeRV; Venna & Kaski, 2007) developed by our collaborators in Helsinki University of Technology. Selected references
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