bbc 2015

BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015
Abstract ID: P
Poster
10th Benelux Bioinformatics Conference bbc 2015
P12. PHENETIC: MULTI-OMICS DATA INTERPRETATION USING
INTERACTION NETWORKS
Dries De Maeyer 1,2,3* , Bram Weytjens 1,2,3 , Luc De Raedt 4 & Kathleen Marchal 2,3 .
Centre for Microbial and Plant Genetics, KULeuven 1 ; Department for Information Sciences (INTEC, IMinds), UGent 2 ;
Department for Plant Biotechnology and Bioinformatics, UGent 3 ; Department of Computer Science, KULeuven 4 .
* dries.demaeyer@biw.kuleuven.be
The omics revolution has introduced new challenges when studying interesting phenotypes. High throughput omics
technologies such as next-generation sequencing and microarray technologies generate large amounts of data.
Interpreting the resulting data from these experiments is not trivial due to the data’s size and the inherent noise of the
underlying technologies. In addition to this, the “omics” technologies have led to an ever expanding biological
knowledge which has to be taken into account when interpreting new experimental results. Interaction network in
combination with subnetwork inference methods provide a solution to this problem by mining the current public
interactomics knowledge using experimental omics data to better understand the molecular mechanisms driving the
interesting phenotypes under study.
INTRODUCTION
Computational methods are becoming essential for
analyzing large scale omics datasets in the light of current
knowledge. By representing publicly available
interactomics knowledge as interaction networks
subnetwork inference methods can extract the actual
molecular mechanisms that drive an interesting phenotype.
The PheNetic framework is such a method that allows for
mining interaction networks with multi-omics datasets.
Using this framework different types of biological
applications have been analyzed in the past such as KOtranscriptomics
interpretation (De Maeyer, 2013),
expression analysis (De Maeyer, 2015) and distinguishing
driver from passenger mutation from eQTL experiments
(De Maeyer).
METHODS
Interaction networks provide a flexible representation of
public biological interactomics knowledge. These
networks represent the physical interactions between
genes and their corresponding gene products in the
interactome of the organism under research (Cloots, 2011).
The interaction network integrates different layers of
homogeneous interactomics data, e.g. signalling, proteinprotein,
(post)transcriptional and metabolic interactomics
data, into a single heterogeneous network representation.
The PheNetic framework uses interaction networks to find
biologically valid paths which connect (in)activated genes
selected from multi-omics data sets. These paths provide a
biological explanation of how the genes from these data
sets can trigger each other. Finding the best explanations
or paths in the interaction network corresponds to finding
that subnetwork that best explains the observed results and
provides an insight into the molecular mechanisms that
drive the interesting phenotype. Depending on the type of
biological application and provided data different types of
paths can be used to infer the subnetwork such as KOtranscriptomics
interpretation (De Maeyer, 2013),
expression analysis (De Maeyer, 2015) and interpreting
eQTL experiments (De Maeyer).
RESULTS & DISCUSSION
In a first setup PheNetic was used to study the pathways
and processes involved in acid resistance in Escherichia
coli (De Maeyer, 2013). Using our framework we were
able to determine the different molecular pathways that
drive acid resistance and identify the regulators that
underlie this phenotype. It was shown that subnetwork
inference methods outperform naïve gene rankings in
identifying the biological pathways associated with the
phenotype under research based.
In a second setup PheNetic was used to interpret
expression data (De Maeyer, 2015) to extract from the
interaction network those parts of the interaction network
that show differences in expression. This method was
provided as a web server that can be accessed at
http://bioinformatics.intec.ugent.be/
phenetic and that allows for an intuitive and visual
interpretation of the inferred subnetworks.
In a third setup PheNetic was used to select driver
mutations from passenger mutations in coupled genetictranscriptomics
data sets from evolution experiments (De
Maeyer). Evolved strains with the same phenotype are
expected to have consistent changes in the same pathways.
Therefore, finding the subnetwork that best connects the
mutations to the differentially expressed genes over all
strains is expected to identify the driver mutations over
passenger mutations in combination with identifying the
molecular mechanisms that induce the observed change in
phenotype. This approach provides a systemic insight in
both the biological processes and genetic background that
induces phenotype.
Based on the different approaches it can be concluded that
PheNetic is a flexible framework for subnetwork selection
that allows for solving a large variety of biological
applications using multi-omics data sets.
REFERENCES
Cloots, L., & Marchal, K. (2011). Curr Opin Microbiol, 14(5), 599-607.
De Maeyer, D., Renkens, J., Cloots, L., De Raedt, L., & Marchal, K.
(2013). Mol Biosyst, 9(7), 1594-1603.
De Maeyer, D., Weytjens, B., Renkens, J., De Raedt, L., & Marchal, K.
(2015). Nucleic Acids Res, 43(W1), W244-250.
De Maeyer, D., Weytjens, B., De Raedt, L., & Marchal, K. Molecular
biology and evolution. Submitted
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