VisGenome and Ensembl: Usability of Integrated Genome Maps

  • Authors:

  • Joanna Jakubowska;Ela Hunt;John Mcclure;Matthew Chalmers;Martin Mcbride;Anna F. Dominiczak

  • Affiliations:

  • Department of Computing Science, University of Glasgow, UK;Department of Computer Science, ETH Zurich, Switzerland;BHF Glasgow Cardiovascular Research Centre, University of Glasgow, UK;Department of Computing Science, University of Glasgow, UK;BHF Glasgow Cardiovascular Research Centre, University of Glasgow, UK;BHF Glasgow Cardiovascular Research Centre, University of Glasgow, UK

  • Venue:
  • DILS '08 Proceedings of the 5th international workshop on Data Integration in the Life Sciences
  • Year:
  • 2008

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Abstract

It is not always clear how best to represent integrated data sets, and which application and database features allow a scientist to take best advantage of data coming from various information sources. To improve the use of integrated data visualisation in candidate gene finding, we carried out a user study comparing an existing general-purpose genetics visualisation and query system, Ensembl, to our new application, VisGenome. We report on experiments verifying the correctness of visual querying in VisGenome, and take advantage of software assessment techniques which are still uncommon in bioinformatics, including asking the users to perform a set of tasks, fill in a questionnaire and participate in an interview. As VisGenome offers smooth zooming and panning driven by mouse actions and a small number of search and view adjustment menus, and Ensembl offers a large amount of data in query interfaces and clickable images, we hypothesised that a simplified interface supported by smooth zooming will help the user in their work. The user study confirmed our expectations, as more users correctly completed data finding tasks in VisGenome than in Ensembl. This shows that improved interactivity and a novel comparative genome representation showing data at various levels of detail support correct data analysis in the context of cross-species QTL and candidate gene finding. Further, we found that a user study gave us new insights and showed new challenges in producing tools that support complex data analysis scenarios in the life sciences.