Understanding biology requires capture, analysis and visualisation of data at many levels of organisation from the molecular through to whole populations and eco-systems. At the tissue, organ and whole organisms level spatial relationships and inter-linking of function are critical and atlases of many forms have been developed provide an underlying spatio-temporal reference framework for such spatially-organised data. From earliest times this is typified as anatomy but more recently includes function, phenotype and geneomic-level data such as patterns of gene-expression. The challenges of visualisation range from depiction of spatial relationships such as anatomy through to visual analytics of massive data-collections (big-data). Visualisation in this theme as all about biological events and biological structures in space and time.
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The visualization of genomes can be achieved through tools, ranging from techniques that allow genome browsing to the exploration of new sequences. The study of genomes is now largely digitalised through sequencing technology. Automated visualisation tools allow researchers to analyse and manipulate large-scale data sets, providing a powerful complement to human interpretation.
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At the molecular level, we need to display static and dynamic representations of the structures of over 100,000 biological macromolecules and many other associated small molecules including drugs. Macromolecular structures typically have over 1,000 atoms and often far more/ Molecular graphics programs enable users to generate images that are both informative and visually fascinating. In addition, we have sequences totalling trillion nucleotides and 25 billion amino acids and methods to visualise these data facilitating comparison are available. As more of biology is mapped at the sequence and structural levels, the user base of molecular display is expanding rapidly. Developers face the challenge of providing increased functionality combined with ease of use.
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With data and information on biological systems growing rapidly, ordering this wealth of knowledge into pathway models has become of paramount importance. This is because the sheer complexity of components, cells and stimuli that can control the activity of systems is a hindrance to advancing our understanding of the processes that regulate biological function in health and disease. Here we aim to highlight tools and resources that support the graphical and computational modelling of biological pathways.
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This theme focuses on approaches to visualizing phylogenies - evolutionary trees showing lineages of descent of individuals, populations or species from their hypothesized common ancestors. The only figure in 'On the Origin of Species' (Darwin, 1859) is a phylogenetic tree visualized in a manner we would consider to be 'modern' - attempting to represent a model of the evolutionary process. This is in contrast to the very literal tree visualizations by Haeckel at the same time showing a gnarled trunk leading to humans at the apex and numerous side-branches leading to other, presumably lesser, species. Modern phylogenetic trees try to represent numerous data dimensions (relatedness, time, genetic divergence, trait evolution, geographical distribution and statistical support) often in a single figure with varying degrees of success. The aim of this theme is to explore how best to represent complex multi-dimensional evolutionary information on a phylogenetic tree in order to convey clear and simple interpretations.
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