bbc 2015

BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015
Abstract ID: P
Poster
10th Benelux Bioinformatics Conference bbc 2015
P30. GALAHAD: A WEB SERVER FOR THE ANALYSIS OF DRUG EFFECTS
FROM GENE EXPRESSION DATA
Griet Laenen 1,2,* , Amin Ardeshirdavani 1,2 , Yves Moreau 1,2 & Lieven Thorrez 1,3 .
Dept. of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics,
KU Leuven 1 ; iMinds Medical IT Dept., KU Leuven 2 ; Dept. of Development and Regeneration @ Kulak, KU Leuven 3 .
* griet.laenen@esat.kuleuven.be
Galahad (https://galahad.esat.kuleuven.be) is a web-based application for the analysis of gene expression data from drug
treatment versus control experiments, aimed at predicting a drug’s molecular targets and biological effects. Galahad
provides data quality assessment and exploratory analysis, as well as computation of differential expression. Based on
the obtained differential expression values, drug target prioritization and both pathway and disease enrichment can be
calculated and visualized. Drug target prioritization is based on the integration of the gene expression data with a
functional protein association network.
INTRODUCTION
Gene expression analysis is frequently employed to study
the effects of drug compounds on cells. The observed
transcriptional patterns can provide valuable information
for identifying compound–protein inter-actions as well as
resulting biological effects. To facilitate the analysis of
this particular data type and enable an in-depth exploration
of a drug’s mode of effect, we have developed Galahad 1 .
INPUT
The main input for Galahad are raw Affymetrix human,
mouse or rat DNA microarray data derived from both
untreated control samples and samples treated with a drug
of interest. In addition, Galahad provides the possibility to
start from differential expression data derived with other
platforms to perform drug target prioritization and
enrichment analysis.
METHODS
The different analyses are depicted in Figure 1 and
include:
preprocessing of the raw data with RMA or
MAS5.0, as indicated by the user;
quality assessment and exploratory analysis to
ascertain data quality, uncover experimental
issues, and help in deciding whether certain
arrays need to be considered as outlying;
differential expression analysis to determine the
significance of gene up- and downregulation
following drug treatment;
genome-wide drug target prioritization by
means of an in-house developed algorithm for
network neighborhood analysis integrating the
expression data with functional protein
association infor-mation 2 ;
prediction of molecular pathways involved in the
drug’s mode of effect;
identification of associated disease phenotypes
enabling side effect prediction and drug
repositioning.
OUTPUT
The output is displayed in a series of tabs corresponding to
the different analyses selected by the user:
in the Quality Control and Data Exploration
tabs, several diagnostic plots are displayed along
with a short explanation;
the Differential Expression tab contains a sorted
table listing all genes together with their log 2
ratios and P-values for differential expression, as
well as links to the corresponding GeneCards
sections;
in the Drug Target Prioritization tab, a ranked
list of genes as potential targets of the drug can be
found, together with the network diffusion-based
scores and P-values for prioritization, and links to
the corresponding GeneCards section; in addition,
a network-based visualization is available for
each gene, showing the 10 interaction partners
contrib-uting most to the gene’s ranking;
the tabs summarizing the results for Pathway
and Disease Enrichment contain a sorted table
with pathway or disease ontology IDs, names,
and database links, together with the number of
differentially expressed genes in the
corresponding gene sets and the accompanying P-
values; in addition, network graphs are available,
consisting of the top 10 most significant
pathways or disease phenotypes, along with their
associated genes colored according to fold change.
FIGURE 1. Overview of the Galahad analysis steps.
REFERENCES
1. Laenen G. et al. Nucl Acids Res 43, W208-W212 (2015).
2. Laenen G. et al. Mol BioSyst 9, 1676-1685 (2013).
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