7. Phosphorylation
Data Analysis
Chia-Lang Hsu∗,‡and Wei-Hsuan Wang†
∗Department of Life Sciences,
National Taiwan University, Taipei, Taiwan
†Genome and Systems Biology Degree Program,
National Taiwan University, Taipei, Taiwan
‡[email protected]- Introduction
Advances in mass spectrometry (MS) and enrichment methods allow
large-scale measuring changes in site-specific protein phosphorylations at a
high temporal resolution. A single MS-based phosphoproteomics experiment
can generate datasets consisting of more than thousands of identified
and quantified phosphorylation sites. The aim of performing such high-
throughput phosphoproteomics investigation is to extract meaningful bio-
logical information that can provide mechanistic insights or hypotheses
for further studies. Although this remains the bottleneck in the field
of phosphoproteomics, the continuous developments and improvement of
bioinformatics tools provide useful strategies to dissect large datasets and
extract the biologically significant information. These tools can be used
to determine, which kinase are more active under the experimental condi-
tions,1–3which pathways or biological processes are significantly enriched in
the data,4,5or to generate and visualize the data in the context of biological
networks.6–8
To gain insight into the regulation of biological processes, it is required
to monitor protein and phosphorylation dynamics by time-course or
multiple-condition experiments. To unveil the dynamics behind the time-
course or multiple-condition phosphoproteomics data, several bioinformatics
tools have been developed to analyze such phosphoproteomics datasets.
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