Bioinformatics Advance Access originally published online on September 7, 2004
Bioinformatics 2005 21(3):415-417; doi:10.1093/bioinformatics/bti005
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Bioinformatics vol. 21 issue 3 © Oxford University Press 2005; all rights reserved.
Capturing biological information with class–responsibility–collaboration cards
1 Department of Biostatistics, Bioinformatics and Epidemiology 135 Cannon Street, P.O. Box 250835, Charleston, SC 29425, USA
2 Bioinformatics Core Facility Medical University of South Carolina 135 Cannon Street, P.O. Box 250835, Charleston, SC 29425, USA
*To whom correspondence should be addressed.
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Abstract |
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Summary: Class–responsibility–collaboration (CRC) cards have been used extensively in the software industry for defining complex object-oriented software requirements. We have adapted this tool to capture information about biological components, collaborators and responsibilities within these collaborations, which is not captured by current annotation tools. CRC cards should provide a common ground that will facilitate communication between biologist and computer scientists.
Availability: A CRC card template, XML representation and XML schema are freely available at http://people.musc.edu/~zhengw/CRCCard/CRC_Card_Index.html
Contact: zhengw{at}musc.edu
Supplementary information: Supplemental Figures 1–4.
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Introduction |
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The current growth of biological data necessitates tools to organize, and annotate this information in a format that is easy to understand. Current annotation tools, such as Entrez (http://www.ncbi.nlm.nih.gov/Database/index.html), GeneCards (Rebhan et al., 1998) Ensembl (http://www.ensembl.org) and SWISS-MODEL (http://www.expasy.org/swissmod/SWISS-MODEL.html) provide comprehensive information describing protein properties using gene ontologies, domain prediction, sequence presentation and homology modeling. Although these are excellent protein annotation tools, they fail to capture protein collaborators or the functional consequences of their collaborations. The emphasis on individual protein analysis by these tools is undergoing a paradigm shift, which stresses capturing biological entities (bioentities) responsibilities and their interactions with other cellular components (defined as collaborators). Since software engineering and biology face similar problems, dealing with large amounts of complex information, one may look to computer science for possible solutions to capturing this missing information. In the software industry, class, responsibility and collaboration cards (CRC cards) have been used extensively to define the requirements for developing complex object-oriented software systems (Wilkinson, 1998). Similarly, CRC cards may be used to capture information invaluable in the reconstruction of cellular networks, while maintaining a format easily understood by computer scientists. CRC cards capture bioentities characteristics, their interactions with other bioentities (i.e. RNA/DNA, proteins, etc.), and describe the bioentity of interests' responsibility (function) within these collaborations. Overall, CRC cards provide a simple format that is comprehendible to biologist and computer scientist alike.
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Application and Discussion |
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Table 1 provides a scaled-down version of a CRC card for Raf-1, a protein implicated in numerous cellular processes including transformation, differentiation and proliferation. Owing to space limitations, only a few of the 55 collaborators are shown (a complete version of the Raf-1 CRC card may be found at http://people.musc.edu/~zhengw/CRCCard/Raf_CRC_Card.doc). Each card represents a cellular component (class), with the name and the attributes of this component presented at the top of the card. Below the class section, the attributes section allows the user to add information that may not be captured by other areas of the card such as synonyms, common bioentity locations, accession numbers and structural information. This section is flexible and has no size limit, thus allowing users to capture as much detail as they like. The bottom of the card is a two-column table. In the left column, each cell describes the responsibilities of this bioentity in the system. That is, this column describes the role of the cellular component during or after the interaction with its collaborator. Similar information can be found in the increasingly prolific interactome maps (Li et al., 2004) which have been developed to illustrate protein interaction networks. However, unlike interactome maps, which ‘display purely binary data—either it exists or it does not—without regards to its timing, location, strength, direction or consequence’ (Perkel, 2004) the CRC card supplies the consequences of these interactions. The right column lists the names of other proteins or cellular components that will collaborate with this bioentity to fulfill the responsibility. These collaborators may be other bioentities such as DNA, RNA and lipids or, as in our example, proteins that may be phosphorylated, bound to or interact with the bioentity of interest. We have also included collaborators that act on the current class.
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Additional information may be obtained through the references placed next to each collaborator or links to other databases that provide more detailed protein attribute information. Although CRC cards are commonly created with index cards, they may also be implemented as web-based systems with collaborators hyperlinked to their own CRC cards. This approach allows the classes to be grouped into subsystems for easy browsing. These cards may also be integrated into a larger model, providing additional information about model components. In addition, to standardize the CRC cards, and facilitate data extraction we have developed an Extensible Markup Language (XML) representation and schema (http://www.w3.org/XML/) for the CRC cards that allows them to be computer readable (http://people.musc.edu/~zhengw/CRCCard/CRC_Card_Index.html). Together, a detailed network with easily extractable data is created that goes beyond purely showing protein interactions. For practical purposes, we have not created CRC cards for each collaborator, but rather hyperlinked each collaborator to its NCBI entry.
As with any tool that relies on data, which may be incomplete or incorrect, it is possible that information about protein responsibilities may be lacking. Further complicating the assembly of the CRC card is the fact that there are no algorithms sophisticated enough to extract protein responsibilities from articles. To address this issue it is proposed that CRC cards should be easily updated through web-based submissions. In the future, utilizing additional standards such as Gene Ontology (http://www.geneontology.org/) to define biological components, processes and functions, and the adoption of the Systems Biology Markup Language (http://sbml.org/index.psp) will help to provide consistent annotation, since these standards can be used for data validation. In addition, to validate information and provide quality assurance, experts on individual proteins could monitor the CRC cards. Ultimately, however, to aid the spread of these tools it will be necessary to develop a standard practice in which researchers are required to provide relevant information during paper submission, thus minimizing the burden of annotating the cellular proteome.
CRC cards' main strengths lie in its simplicity and ability to reflect the collaborators and responsibilities of cellular components. Used in conjunction with current protein annotation tools this approach will facilitate computer scientists' understanding of biological systems thus, helping them to build better models. In conclusion, CRC cards provide a scaffold for the development of more detailed systems models and can provide a first step in the analysis of signaling networks.
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Acknowledgments |
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D.S. is supported by NLM training grant 5-T15-LM007438-02. W.J.Z. is partly supported by a grant (DE-FG02-01ER63121) from the Department of Energy.
Received on July 1, 2004; revised on August 6, 2004; accepted on August 23, 2004
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REFERENCES |
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TOPAbstractIntroductionApplication and DiscussionREFERENCES |
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Chen, J., Fujii, K., Zhang, L., Roberts, T., Fu, H. (2001) Raf-1 promotes cell survival by antagonizing apoptosis signal-regulating kinase 1 through a MEK-ERK independent mechanism. Proc. Natl Acad. Sci. USA, 98, 7783–7788
Chong, H., Vikis, H.G., Guan, K.L. (2003) Mechanisms of regulating the Raf kinase family. Cell Signal, 15, 463–469[CrossRef][ISI][Medline].
Colombini, M., Blachly-Dyson, E., Forte, M. (1996) VDAC, a channel in the outer mitochondrial membrane. Ion Channels, 4, 169–202[Medline].
Galaktionov, K., Jessus, C., Beach, D. (1995) Raf1 interaction with Cdc25 phosphatase ties mitogenic signal transduction to cell cycle activation. Genes Dev., 9, 1046–1058
Jamal, S. and Ziff, E.B. (1995) Raf phosphorylates p53 in vitro and potentiates p53-dependent transcriptional transactivation in vivo . Oncogene, 10, 2095–2101[ISI][Medline].
Li, S., Armstrong, C.M., Bertin, N., Ge, H., Milstein, S., Boxem, M., Vidalain, P.O., Han, J.D., Chesneau, A., Hao, T., et al. (2004) A map of the interactome network of the metazoan C.elegans . Science, 303, 540–543
Lin, J.H., Makris, A., McMahon, C., Bear, S.E., Patriotis, C., Prasad, V.R., Brent, R., Golemis, E.A., Tsichlis, P.N. (1999) The ankyrin repeat-containing adaptor protein Tvl-1 is a novel substrate and regulator of Raf-1. J. Biol. Chem., 274, 14706–14715
Perkel, J. (2004) Validating the interactome. Scientist, 18, 18–22.
Rebhan, M., Chalifa-Caspi, V., Prilusky, J., Lancet, D. (1998) GeneCards: a novel functional genomics compendium with automated data mining and query reformulation support. Bioinformatics, 14, 656–664
Wang, S., Ghosh, R.N., Chellappan, S.P. (1998) Raf-1 physically interacts with Rb and regulates its function: a link between mitogenic signaling and cell cycle regulation. Mol. Cell. Biol., 18, 7487–7498
Wilkinson, N.M. Using CRC Cards: An Informal Approach to Object-Oriented Development, (1998) , Cambridge Cambridge University Press, pp. 252.
Wu, X., Noh, S.J., Zhou, G., Dixon, J.E., Guan, K.L. (1996) Selective activation of MEK1 but not MEK2 by A-Raf from epidermal growth factor-stimulated Hela cells. J. Biol. Chem., 271, 3265–3271
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