Subcellular localization charts: a new visual methodology for the semi-automatic localization of protein-related data sets

Sommer B, Kormeier B, Demenkov PS, Arrigo P, Hippe K, Ates Ö, Kochetov AV, Ivanisenko VA, Kolchanov NA, Hofestädt R (2013)
Journal of Bioinformatics and Computational Biology 11(01).

Journal Article | Published | English

No fulltext has been uploaded

Author
; ; ; ; ; ; ; ; ;
Abstract
The CELLmicrocosmos PathwayIntegration (CmPI) was developed to support and visualize the subcellular localization prediction of protein-related data such as protein-interaction networks. From the start it was possible to manually analyze the localizations by using an interactive table. It was, however, quite complicated to compare and analyze the different localization results derived from data integration as well as text-mining-based databases. The current software release provides a new interactive visual workflow, the Subcellular Localization Charts. As an application case, a MUPP1-related protein-protein interaction network is localized and semi-automatically analyzed. It will be shown that the workflow was dramatically improved and simplified. In addition, it is now possible to use custom protein-related data by using the SBML format and get a view of predicted protein localizations mapped onto a virtual cell model.
Publishing Year
ISSN
eISSN
PUB-ID

Cite this

Sommer B, Kormeier B, Demenkov PS, et al. Subcellular localization charts: a new visual methodology for the semi-automatic localization of protein-related data sets. Journal of Bioinformatics and Computational Biology. 2013;11(01).
Sommer, B., Kormeier, B., Demenkov, P. S., Arrigo, P., Hippe, K., Ates, Ö., Kochetov, A. V., et al. (2013). Subcellular localization charts: a new visual methodology for the semi-automatic localization of protein-related data sets. Journal of Bioinformatics and Computational Biology, 11(01).
Sommer, B., Kormeier, B., Demenkov, P. S., Arrigo, P., Hippe, K., Ates, Ö., Kochetov, A. V., Ivanisenko, V. A., Kolchanov, N. A., and Hofestädt, R. (2013). Subcellular localization charts: a new visual methodology for the semi-automatic localization of protein-related data sets. Journal of Bioinformatics and Computational Biology 11.
Sommer, B., et al., 2013. Subcellular localization charts: a new visual methodology for the semi-automatic localization of protein-related data sets. Journal of Bioinformatics and Computational Biology, 11(01).
B. Sommer, et al., “Subcellular localization charts: a new visual methodology for the semi-automatic localization of protein-related data sets”, Journal of Bioinformatics and Computational Biology, vol. 11, 2013.
Sommer, B., Kormeier, B., Demenkov, P.S., Arrigo, P., Hippe, K., Ates, Ö., Kochetov, A.V., Ivanisenko, V.A., Kolchanov, N.A., Hofestädt, R.: Subcellular localization charts: a new visual methodology for the semi-automatic localization of protein-related data sets. Journal of Bioinformatics and Computational Biology. 11, (2013).
Sommer, Björn, Kormeier, Benjamin, Demenkov, Pavel S., Arrigo, Patrizio, Hippe, Klaus, Ates, Özgür, Kochetov, Alexey V., Ivanisenko, Vladimir A., Kolchanov, Nikolay A., and Hofestädt, Ralf. “Subcellular localization charts: a new visual methodology for the semi-automatic localization of protein-related data sets”. Journal of Bioinformatics and Computational Biology 11.01 (2013).
This data publication is cited in the following publications:
This publication cites the following data publications:

12 References

Data provided by Europe PubMed Central.


Heazlewood, Nucleic Acids Res. 35(), 2007

Croft, Nucleic Acids Res. 39(), 2011
The Interactorium: visualising proteins, complexes and interaction networks in a virtual 3-D cell.
Widjaja YY, Pang CN, Li SS, Wilkins MR, Lambert TD., Proteomics 9(23), 2009
PMID: 19798670
Visualization and analysis of a cardio vascular disease- and MUPP1-related biological network combining text mining and data warehouse approaches.
Sommer B, Tiys ES, Kormeier B, Hippe K, Janowski SJ, Ivanisenko TV, Bragin AO, Arrigo P, Demenkov PS, Kochetov AV, Ivanisenko VA, Kolchanov NA, Hofestadt R., J Integr Bioinform 7(1), 2010
PMID: 21068463
The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models.
Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, Arkin AP, Bornstein BJ, Bray D, Cornish-Bowden A, Cuellar AA, Dronov S, Gilles ED, Ginkel M, Gor V, Goryanin II, Hedley WJ, Hodgman TC, Hofmeyr JH, Hunter PJ, Juty NS, Kasberger JL, Kremling A, Kummer U, Le Novere N, Loew LM, Lucio D, Mendes P, Minch E, Mjolsness ED, Nakayama Y, Nelson MR, Nielsen PF, Sakurada T, Schaff JC, Shapiro BE, Shimizu TS, Spence HD, Stelling J, Takahashi K, Tomita M, Wagner J, Wang J; SBML Forum., Bioinformatics 19(4), 2003
PMID: 12611808

Kormeier, Journal of Integrative Bioinformatics 7(2), 2010

Hippe, GI Jahrestagung 2010(), 2010

Scheer, Nucleic Acids Res. 39(), 2011

AUTHOR UNKNOWN, 0

Janowski, Silico Biology 10(1), 2010
Human Protein Reference Database and Human Proteinpedia as discovery tools for systems biology.
Prasad TS, Kandasamy K, Pandey A., Methods Mol. Biol. 577(), 2009
PMID: 19718509

Demenkov, Computational Technologies 13(2), 2008

Export

0 Marked Publications

Open Data PUB

Web of Science

View record in Web of Science®

Sources

PMID: 23427987
PubMed | Europe PMC

Search this title in

Google Scholar