The Perseus computational platform for comprehensive analysis of (prote)omics data
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The Perseus computational platform for comprehensive analysis of (prote)omics data. / Tyanova, Stefka; Temu, Tikira; Sinitcyn, Pavel; Carlson, Arthur; Hein, Marco Y; Geiger, Tamar; Mann, Matthias; Cox, Jürgen.
In: Nature Methods, Vol. 13, No. 9, 09.2016, p. 731-40.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - The Perseus computational platform for comprehensive analysis of (prote)omics data
AU - Tyanova, Stefka
AU - Temu, Tikira
AU - Sinitcyn, Pavel
AU - Carlson, Arthur
AU - Hein, Marco Y
AU - Geiger, Tamar
AU - Mann, Matthias
AU - Cox, Jürgen
PY - 2016/9
Y1 - 2016/9
N2 - A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.
AB - A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.
KW - Computational Biology
KW - Computer Graphics
KW - Databases, Protein
KW - Machine Learning
KW - Mass Spectrometry
KW - Protein Processing, Post-Translational
KW - Proteins
KW - Proteomics
KW - Software
KW - Workflow
KW - Journal Article
U2 - 10.1038/nmeth.3901
DO - 10.1038/nmeth.3901
M3 - Journal article
C2 - 27348712
VL - 13
SP - 731
EP - 740
JO - Nature Methods
JF - Nature Methods
SN - 1548-7091
IS - 9
ER -
ID: 186876519