TY - JOUR
T1 - Subcellular localization charts: a new visual methodology for the semi-automatic localization of protein-related data sets
AU - Sommer, Bjorn
AU - Kormeier, Benjamin
AU - Demenkov, Pavel Sergeevich
AU - Arrigo, Patrizio
AU - Hippe, Klaus
AU - Ates, Ozgur
AU - Kochetov, Alexey Vladimirovich
AU - Ivanisenko, Vladimir Aleksandrovich
AU - Kolchanov, Nikolay Aleksandrovich
AU - Hofestadt, Ralf
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://goo.gl/SzoJGt
U2 - 10.1142/S0219720013400052
DO - 10.1142/S0219720013400052
M3 - Article
SN - 0219-7200
VL - 11
SP - 1
EP - 18
JO - Journal of Bioinformatics and Computational Biology
JF - Journal of Bioinformatics and Computational Biology
IS - 1
ER -