Integration and visualisation of data in bioinformatics

Includes bibliographical referencesThe most recent advances in laboratory techniques aimed at observing and measuring biological processes are characterised by their ability to generate large amounts of data. The more data we gather, the greater the chance of finding clues to understand the systems of life. This, however, is only true if the methods that analyse the generated data are efficient, effective, and robust enough to overcome the challenges intrinsic to the management of big data. The computational tools designed to overcome these challenges should also take into account the requirements of current research. Science demands specialised knowledge for understanding the particularities of each study; in addition, it is seldom possible to describe a single observation without considering its relationship with other processes, entities or systems. This thesis explores two closely related fields: the integration and visualisation of biological data. We believe that these two branches of study are fundamental in the creation of scientific software tools that respond to the ever increasing needs of researchers. The distributed annotation system (DAS) is a community project that supports the integration of data from federated sources and its visualisation on web and stand-alone clients. We have extended the DAS protocol to improve its search capabilities and also to support feature annotation by the community. We have also collaborated on the implementation of MyDAS, a server to facilitate the publication of biological data following the DAS protocol, and contributed in the design of the protein DAS client called DASty. Furthermore, we have developed a tool called probeSearcher, which uses the DAS technology to facilitate the identification of microarray chips that include probes for regions on proteins of interest. Another community project in which we participated is BioJS, an open source library of visualisation components for biological data. This thesis includes a description of the project, our contributions to it and some developed components that are part of it. Finally, and most importantly, we combined several BioJS components over a modular architecture to create PINV, a web based visualiser of protein-protein interaction (PPI) networks, that takes advantage of the features of modern web technologies in order to explore PPI datasets on an almost ubiquitous platform (the web) and facilitates collaboration between scientific peers. This thesis includes a description of the design and development processes of PINV, as well as current use cases that have benefited from the tool and whose feedback has been the source of several improvements to PINV. Collectively, this thesis describes novel software tools that, by using modern web technologies, facilitates the integration, exploration and visualisation of biological data, which has the potential to contribute to our understanding of the systems of life

Exploration space of human-data interaction

Data is everywhere. Starting with the invention of writing, representation artifacts brought the data to observable state which led to natural establishment of an interaction form between human and data. In the human-data interaction (HDI) environment, data representations and analytic systems act as an intermediary role. I suggest a new de nition for HDI in which this interaction is conceptualized as a communication model over a set of media. The interaction occurs with the exchange of messages originated from both human and data. Timing and content of the messages are employed to facilitate objective evaluation of properties of analytic system in question. To systematically investigate the complex nature of HDI, my methodology postulates the phenomenon as a high-dimensional space in which data analytic systems could be positioned based on their properties. Evaluation of the properties are performed based on solid de nitions of the dimensions. I de ne ve properties for data analytic systems, namely, responsiveness, communication media level, unit task diversity, closeness factor, and progressiveness level, and demonstrate how these properties could be objectively calculated. I visually explore the HDI space in which data analytic systems reported in my thesis are plotted on a two-dimensional Cartesian system whose axes are responsiveness and communication media level. Visually identi able patterns in this plot, which I call realms, are characterized by quantitative and qualitative analysis of objective, behavioral, and subjective data collected during the user interaction with the corresponding analytic system