Systems biology offers an interdisciplinary approach to scientific research that typically involves the collaboration of teams of experimentalists and mathematical modellers. While the importance of data standards has been recognised in facilitating exchange of data between the parties, challenges still remain regarding the practicalities of disseminating experimental data.&#xd;&#xa;&#xd;&#xa;The introduction of novel web-based tools aimed at promoting collaborative work has provided a platform upon which scientific applications can be built. The recently released Google Wave protocol provides a facility for real-time collaboration between teams of researchers.&#xd;&#xa;&#xd;&#xa;This work introduces a customized Robot that automatically scans text in Google Waves for experimental data identifiers, extracts corresponding experimental data from remote resources associated with such identifiers, and appends charts showing this experimental data to the Wave

Neil Swainston

English

Nature Precedings

waveomics: bringing experimental data to online collaboration
Neil Swainston
Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester 
M1 7DN, UK
Abstract
Summary
Systems biology offers an interdisciplinary approach to scientific research that typically 
involves the collaboration of teams of experimentalists and mathematical modellers. While the 
importance of data standards has been recognised in facilitating exchange of data between 
the parties, challenges still remain regarding the practicalities of disseminating experimental 
data.
The introduction of novel web-based tools aimed at promoting collaborative work has 
provided a platform upon which scientific applications can be built. The recently released 
Google Wave protocol provides a facility for real-time collaboration between teams of 
researchers.
This work introduces a customized Robot that automatically scans text in Google Waves for 
experimental data identifiers, extracts corresponding experimental data from remote 
resources associated with such identifiers, and appends charts showing this experimental 
data to the Wave.
Availability and Implementation
The waveomics Robot is available at waveomics@appspot.com and has the project page 
http://waveomics.appspot.com/. Source code and associated build files are available under 
the open source Academic Free Licence v3.0 from http://mcisb.sf.net/. Waveomics is written 
in Java 1.6 using the Google Wave Robots API, version 2.
Contact
neil.swainston@manchester.ac.uk
Introduction
Systems Biology applies a holistic approach to bioscience, in which an understanding of the 
system is elucidated from an integrated, cyclical process of observation, modelling and 
hypothesis generation. Due to its very nature, it is an interdisciplinary science, drawing upon 
heterogeneous experimental datasets from a range of sources. The ability to disseminate this 
experimental data in such a way that it can be accessed in a simple and intuitive manner is of 
particular importance in such an interdisciplinary environment. Systems biology projects 
commonly operate a distributed approach to collaborative work, where experimentalists, 
modellers and coordinators may be housed in different departments, and often even different 
countries.
Google Wave (http://wave.google.com/) has been described as “what email would look like if 
it were invented today”. A web-based application, it has been developed specifically to 
facilitate on-line collaboration, and shares much of its functionality with that of e-mail, instant 
messaging and wikis. Data are organized as “Waves”, which are partly discussions, partly 
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editable documents. Multiple participants can contribute to and edit a Wave, in this sense 
providing functionality similar to that of a wiki page. Additionally, there exists the facility to 
both develop and utilise customized plugins, in the form of “Gadgets” and “Robots”. Gadgets 
are extensible applications that can be shared and used interactively by all participants in a 
Wave. Examples of these include embeddable calendar and map applications. Robots 
behave as automated participants in the Wave, and can respond to update events to perform 
such tasks as automatically checking input for spelling errors, or in a more specialised 
scientific example, scanning text for chemical formulae.
As such, there has been great interest in the bioinformatics community in the use of Google 
Wave1, and while the original excitement has subsided a little, tools such as this (or those that 
supercede them) provide great promise in facilitating collaborative work. The speed at which 
the community will adopt such tools is likely to be dependent upon the development of useful 
and usable tools that will persuade a mass of users to move away from more familiar media 
such as e-mail.
A Robot is introduced here, waveomics, which provides the first example of allowing 
experimental data from multiple sources to be queried and shared in the Google Wave 
environment. While this represents a first step, the work gives an indication of what can be 
achieved in such a system, and provides a concrete use case as to how research laboratories 
can benefit by making their data available in standard formats that can be exploited and 
shared online.
Methods
Robots are automated participants in a Wave. They can perform customised tasks, such as 
modifying the contents of the Wave, interacting with other participants, or interfacing with 
external resources, such as other web resources or databases.
The waveomics Robot was written using the Google Wave Robots API. The Robot itself is a 
Java servlet that extends the AbstractRobot base class. Event handler methods are 
overwritten and these are triggered when a given operation occurs in a Wave to which the 
Robot has been added. Examples of these events are onWaveletSelfAdded, which is fired 
when the Robot is added to a Wave, and onWaveletParticipantsChanged, fired when a 
new participant enters the discussion.
The waveomics Robot implements the onBlipSubmitted event. In Google Wave terms, a 
“Blip” is described as a single unit of information, which commonly is a single update or 
message from a participant. Upon submission of such a Blip, the event handler in the Robot 
scans the text within the Blip, applying one or more regular expressions, to determine 
identifiers of interest. These may take the form of gene names, protein identifiers, or chemical 
species.
Robots, like human participants, can also contribute to Waves by adding or updating Blips 
themselves. Upon detection of an identifier, the Robot updates the Blip by appending a 
customised Gadget (a plug-in application) that attempts to search for and display any 
experimental data appropriate to this identifier.
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Figure 1. Architecture of the waveomics Robot, associated Gadget and data servlet.
Gadgets are specified in XML files containing HTML markup that can be embedded within a 
Wave. As such, a Gadget can perform much the functionality of any other web page. The 
Robot passes the waveomics Gadget the matched database identifier as a query string. The 
Gadget passes this identifier to an external data retrieval servlet, whose responsibility it is to 
query an appropriate database for experimental data matching the identifier. Upon extracting 
data, the data retrieval servlet utilizes Google Charts to generate a chart image, returning a 
URL specifying the chart image to the Gadget, which then embeds the image within its HTML 
markup. (See figure 1).
Results
Waveomics supports the querying and visualisation of two heterogeneous experimental data 
types: mass spectrometry acquired proteomics data, and spectrophotometric enzyme kinetic 
assays data. It is important to note, however, that the architecture described here may be 
extended to query for and embed other experimental data types, such as metabolomics data, 
or even respond to updated Blips in an entirely different manner.
Proteomics
Upon submission of a Blip, the waveomics Robot applies a regular expression matcher to the 
text within the Blip, attempting to find UniProt identifiers2. Upon locating one or more 
identifiers, a new Gadget is appended to the Blip that submits each to a proteomics data 
retrieval servlet. This servlet queries a proteomics data resource holding PRIDE formatted 
XML documents3,4. If mass spectra matching the UniProt identifier are extracted, these are 
converted by Google Charts into image URLs, which are then embedded with the Gadget 
HTML markup, effectively appending the chart image to the end of the Blip. The Gadget also 
provides the facility to navigate through multiple spectra if appropriate (see fig. 2).
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Figure 2. Screen-capture showing a mass spectrum Gadget, automatically appended to a Blip 
by the waveomics Robot upon detection of the UniProt protein identifier TPIS_YEAST.
Enzyme kinetics
A similar process is followed for enzyme kinetics data. Enzyme Classification (EC) numbers5 
are extracted from Blips, with an enzyme kinetics data retrieval servlet querying the MeMo-
RK database6 to returning enzyme kinetics assay data sets that are converted into image 
URLs as above.
Acknowledgements
The author thanks friendfeed’s Life Scientists for fostering an interest in Google Wave within 
the bioinformatics community.
Funding: This work was supported by the Biotechnology and Biological Sciences Research 
Council and the Engineering and Physical Sciences Research Council [BB/C008219/1].
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1
 Neylon C. Head in the clouds: Re-imagining the experimental laboratory record for the web-based networked 
world. Autom Exp. 2009, 1:3.
2
 The UniProt Consortium. The Universal Protein Resource (UniProt). Nucleic Acids Res. 2008, 36, D190-D195.
3
 Jones P, et al. (2006) PRIDE: a public repository of protein and peptide identifications for the proteomics 
community. Nucleic Acids Res. 2006, 34, D659-D663.
4
 Swainston N, et al. A QconCAT informatics pipeline for the analysis, visualization and sharing of absolute 
quantitative proteomics data. Proteomics. (Submitted).
5
 Webb EC. Enzyme nomenclature 1992. Academic Press, San Diego, California.
6
 Swainston N, et al. Enzyme kinetics informatics: from instrument to browser. FEBS J. 2010 Aug 3 [Epub ahead 
of print] DOI: 10.1111/j.1742-4658.2010.07778.x
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Waveomics: bringing experimental data to online collaboration

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Waveomics: bringing experimental data to online collaboration

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