Journal Article

Better bioinformatics through usability analysis

Davide Bolchini, Anthony Finkelstein, Vito Perrone and Sylvia Nagl

in Bioinformatics

Volume 25, issue 3, pages 406-412
Published in print February 2009 | ISSN: 1367-4803
Published online December 2008 | e-ISSN: 1460-2059 | DOI: https://dx.doi.org/10.1093/bioinformatics/btn633
Better bioinformatics through usability analysis

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Motivation: Improving the usability of bioinformatics resources enables researchers to find, interact with, share, compare and manipulate important information more effectively and efficiently. It thus enables researchers to gain improved insights into biological processes with the potential, ultimately, of yielding new scientific results. Usability ‘barriers’ can pose significant obstacles to a satisfactory user experience and force researchers to spend unnecessary time and effort to complete their tasks. The number of online biological databases available is growing and there is an expanding community of diverse users. In this context there is an increasing need to ensure the highest standards of usability.

Results: Using ‘state-of-the-art’ usability evaluation methods, we have identified and characterized a sample of usability issues potentially relevant to web bioinformatics resources, in general. These specifically concern the design of the navigation and search mechanisms available to the user. The usability issues we have discovered in our substantial case studies are undermining the ability of users to find the information they need in their daily research activities. In addition to characterizing these issues, specific recommendations for improvements are proposed leveraging proven practices from web and usability engineering. The methods and approach we exemplify can be readily adopted by the developers of bioinformatics resources.

Contact: dbolchin@iupui.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  6043 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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