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Visualising biological data: a semantic approach to tool and database integration.

Pettifer S, Thorne D, McDermott P, Marsh J, Vill├ęger A, Kell DB, Attwood TK - BMC Bioinformatics (2009)

Bottom Line: For many day-to-day bioinformatics tasks, the sizes of current databases, and the scale of the analyses necessary, now demand increasing levels of automation; nevertheless, the unique experience and intuition of human researchers is still required to interpret the end results in any meaningful biological way.Numerous tools do exist for this purpose, but many do not have optimal interfaces, most are effectively isolated from other tools and databases owing to incompatible data formats, and many have limited real-time performance when applied to realistically large data-sets: much of the user's cognitive capacity is therefore focused on controlling the software and manipulating esoteric file formats rather than on performing the research.Key to the system's usability is its direct exploitation of semantics, which, crucially, gives individual components knowledge of their own functionality and allows them to interoperate seamlessly, removing many of the existing barriers and bottlenecks from standard bioinformatics tasks.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Computer Science, University of Manchester, Manchester, M13 9PL, UK. steve.pettifer@manchester.ac.uk

ABSTRACT

Motivation: In the biological sciences, the need to analyse vast amounts of information has become commonplace. Such large-scale analyses often involve drawing together data from a variety of different databases, held remotely on the internet or locally on in-house servers. Supporting these tasks are ad hoc collections of data-manipulation tools, scripting languages and visualisation software, which are often combined in arcane ways to create cumbersome systems that have been customized for a particular purpose, and are consequently not readily adaptable to other uses. For many day-to-day bioinformatics tasks, the sizes of current databases, and the scale of the analyses necessary, now demand increasing levels of automation; nevertheless, the unique experience and intuition of human researchers is still required to interpret the end results in any meaningful biological way. Putting humans in the loop requires tools to support real-time interaction with these vast and complex data-sets. Numerous tools do exist for this purpose, but many do not have optimal interfaces, most are effectively isolated from other tools and databases owing to incompatible data formats, and many have limited real-time performance when applied to realistically large data-sets: much of the user's cognitive capacity is therefore focused on controlling the software and manipulating esoteric file formats rather than on performing the research.

Methods: To confront these issues, harnessing expertise in human-computer interaction (HCI), high-performance rendering and distributed systems, and guided by bioinformaticians and end-user biologists, we are building reusable software components that, together, create a toolkit that is both architecturally sound from a computing point of view, and addresses both user and developer requirements. Key to the system's usability is its direct exploitation of semantics, which, crucially, gives individual components knowledge of their own functionality and allows them to interoperate seamlessly, removing many of the existing barriers and bottlenecks from standard bioinformatics tasks.

Results: The toolkit, named Utopia, is freely available from http://utopia.cs.man.ac.uk/.

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Forthcoming tools. Screenshots of Utopia Documents (left) and Networks (right). In Documents, the different coloured highlights represent areas of interest, determined algorithmically or by manual annotation (for example, here the comment bubble shows a reader adding a commentary to this paper, which is then automatically shared with all other readers). In Networks, nodes are coloured according to whether they represent metabolites or their co-factors.
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Related In: Results  -  Collection

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Figure 4: Forthcoming tools. Screenshots of Utopia Documents (left) and Networks (right). In Documents, the different coloured highlights represent areas of interest, determined algorithmically or by manual annotation (for example, here the comment bubble shows a reader adding a commentary to this paper, which is then automatically shared with all other readers). In Networks, nodes are coloured according to whether they represent metabolites or their co-factors.

Mentions: The released version of the Utopia suite currently has three front-end applications: Sequences, Structures and Library; with two further tools in development: Networks and Documents. These are illustrated in figures 3 and 4.


Visualising biological data: a semantic approach to tool and database integration.

Pettifer S, Thorne D, McDermott P, Marsh J, Vill├ęger A, Kell DB, Attwood TK - BMC Bioinformatics (2009)

Forthcoming tools. Screenshots of Utopia Documents (left) and Networks (right). In Documents, the different coloured highlights represent areas of interest, determined algorithmically or by manual annotation (for example, here the comment bubble shows a reader adding a commentary to this paper, which is then automatically shared with all other readers). In Networks, nodes are coloured according to whether they represent metabolites or their co-factors.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2697642&req=5

Figure 4: Forthcoming tools. Screenshots of Utopia Documents (left) and Networks (right). In Documents, the different coloured highlights represent areas of interest, determined algorithmically or by manual annotation (for example, here the comment bubble shows a reader adding a commentary to this paper, which is then automatically shared with all other readers). In Networks, nodes are coloured according to whether they represent metabolites or their co-factors.
Mentions: The released version of the Utopia suite currently has three front-end applications: Sequences, Structures and Library; with two further tools in development: Networks and Documents. These are illustrated in figures 3 and 4.

Bottom Line: For many day-to-day bioinformatics tasks, the sizes of current databases, and the scale of the analyses necessary, now demand increasing levels of automation; nevertheless, the unique experience and intuition of human researchers is still required to interpret the end results in any meaningful biological way.Numerous tools do exist for this purpose, but many do not have optimal interfaces, most are effectively isolated from other tools and databases owing to incompatible data formats, and many have limited real-time performance when applied to realistically large data-sets: much of the user's cognitive capacity is therefore focused on controlling the software and manipulating esoteric file formats rather than on performing the research.Key to the system's usability is its direct exploitation of semantics, which, crucially, gives individual components knowledge of their own functionality and allows them to interoperate seamlessly, removing many of the existing barriers and bottlenecks from standard bioinformatics tasks.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Computer Science, University of Manchester, Manchester, M13 9PL, UK. steve.pettifer@manchester.ac.uk

ABSTRACT

Motivation: In the biological sciences, the need to analyse vast amounts of information has become commonplace. Such large-scale analyses often involve drawing together data from a variety of different databases, held remotely on the internet or locally on in-house servers. Supporting these tasks are ad hoc collections of data-manipulation tools, scripting languages and visualisation software, which are often combined in arcane ways to create cumbersome systems that have been customized for a particular purpose, and are consequently not readily adaptable to other uses. For many day-to-day bioinformatics tasks, the sizes of current databases, and the scale of the analyses necessary, now demand increasing levels of automation; nevertheless, the unique experience and intuition of human researchers is still required to interpret the end results in any meaningful biological way. Putting humans in the loop requires tools to support real-time interaction with these vast and complex data-sets. Numerous tools do exist for this purpose, but many do not have optimal interfaces, most are effectively isolated from other tools and databases owing to incompatible data formats, and many have limited real-time performance when applied to realistically large data-sets: much of the user's cognitive capacity is therefore focused on controlling the software and manipulating esoteric file formats rather than on performing the research.

Methods: To confront these issues, harnessing expertise in human-computer interaction (HCI), high-performance rendering and distributed systems, and guided by bioinformaticians and end-user biologists, we are building reusable software components that, together, create a toolkit that is both architecturally sound from a computing point of view, and addresses both user and developer requirements. Key to the system's usability is its direct exploitation of semantics, which, crucially, gives individual components knowledge of their own functionality and allows them to interoperate seamlessly, removing many of the existing barriers and bottlenecks from standard bioinformatics tasks.

Results: The toolkit, named Utopia, is freely available from http://utopia.cs.man.ac.uk/.

Show MeSH