IKAP: A heuristic framework for inference of kinase activities from Phosphoproteomics data
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IKAP : A heuristic framework for inference of kinase activities from Phosphoproteomics data. / Mischnik, Marcel; Sacco, Francesca; Cox, Jürgen; Schneider, Hans-Christoph; Schäfer, Matthias; Hendlich, Manfred; Crowther, Daniel; Mann, Matthias; Klabunde, Thomas.
In: Bioinformatics (Online), Vol. 32, No. 3, 01.02.2016, p. 424-31.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - IKAP
T2 - A heuristic framework for inference of kinase activities from Phosphoproteomics data
AU - Mischnik, Marcel
AU - Sacco, Francesca
AU - Cox, Jürgen
AU - Schneider, Hans-Christoph
AU - Schäfer, Matthias
AU - Hendlich, Manfred
AU - Crowther, Daniel
AU - Mann, Matthias
AU - Klabunde, Thomas
N1 - © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
PY - 2016/2/1
Y1 - 2016/2/1
N2 - MOTIVATION: Phosphoproteomics measurements are widely applied in cellular biology to detect changes in signalling dynamics. However, due to the inherent complexity of phosphorylation patterns and the lack of knowledge on how phosphorylations are related to functions, it is often not possible to directly deduce protein activities from those measurements. Here, we present a heuristic machine learning algorithm that infers the activities of kinases from Phosphoproteomics data using kinase-target information from the PhosphoSitePlus database. By comparing the estimated kinase activity profiles to the measured phosphosite profiles, it is furthermore possible to derive the kinases that are most likely to phosphorylate the respective phosphosite.RESULTS: We apply our approach to published datasets of the human cell cycle generated from HeLaS3 cells, and insulin signalling dynamics in mouse hepatocytes. In the first case, we estimate the activities of 118 at six cell cycle stages and derive 94 new kinase-phosphosite links that can be validated through either database or motif information. In the second case, the activities of 143 kinases at eight time points are estimated and 49 new kinase-target links are derived.AVAILABILITY AND IMPLEMENTATION: The algorithm is implemented in Matlab and be downloaded from github. It makes use of the Optimization and Statistics toolboxes. https://github.com/marcel-mischnik/IKAP.git.CONTACT: marcel.mischnik@gmail.comSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
AB - MOTIVATION: Phosphoproteomics measurements are widely applied in cellular biology to detect changes in signalling dynamics. However, due to the inherent complexity of phosphorylation patterns and the lack of knowledge on how phosphorylations are related to functions, it is often not possible to directly deduce protein activities from those measurements. Here, we present a heuristic machine learning algorithm that infers the activities of kinases from Phosphoproteomics data using kinase-target information from the PhosphoSitePlus database. By comparing the estimated kinase activity profiles to the measured phosphosite profiles, it is furthermore possible to derive the kinases that are most likely to phosphorylate the respective phosphosite.RESULTS: We apply our approach to published datasets of the human cell cycle generated from HeLaS3 cells, and insulin signalling dynamics in mouse hepatocytes. In the first case, we estimate the activities of 118 at six cell cycle stages and derive 94 new kinase-phosphosite links that can be validated through either database or motif information. In the second case, the activities of 143 kinases at eight time points are estimated and 49 new kinase-target links are derived.AVAILABILITY AND IMPLEMENTATION: The algorithm is implemented in Matlab and be downloaded from github. It makes use of the Optimization and Statistics toolboxes. https://github.com/marcel-mischnik/IKAP.git.CONTACT: marcel.mischnik@gmail.comSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
KW - Algorithms
KW - Animals
KW - Cell Cycle
KW - Cell Cycle Proteins
KW - Cells, Cultured
KW - Databases, Factual
KW - HeLa Cells
KW - Hepatocytes
KW - Heuristics
KW - Humans
KW - Insulin
KW - Mice
KW - Phosphoproteins
KW - Phosphorylation
KW - Protein Kinases
KW - Proteomics
KW - Software
KW - Journal Article
KW - Research Support, Non-U.S. Gov't
U2 - 10.1093/bioinformatics/btv699
DO - 10.1093/bioinformatics/btv699
M3 - Journal article
C2 - 26628587
VL - 32
SP - 424
EP - 431
JO - Bioinformatics (Online)
JF - Bioinformatics (Online)
SN - 1367-4811
IS - 3
ER -
ID: 186877788