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The PanHunter Platform
Molecular patient
databases
Patient (iPSC) - derived
disease models
Re-defining health and
disease with molecular
disease profiles
Focus on disease
relevance throughout
the entire process
Extensive substance testing
Diagnostics
and biomarkers
Precision diagnostics
Precise tracking of
disease progress
Genomics - Transcriptomics - Proteomics - Metabolomics
Industrialised data generation
PanOmics
data generation
Data science - Machine learning /
Artificial intelligence - Bioinformatics
AI/ML driven data analytics
Figure 1: Evotec's omics-driven and patient-based research platforms
However, generating deep and high-quality omics
data is just the starting point. Alongside advancing
our data generation and interpretation capabilities,
we have been working towards democratising access
to data exploration for all scientists and give all
researchers the bio -infor matics skill set to handle
and analyse high-dimensional omics data.
To empower our scientists to cope with the
overwhelmingly increasing amount of data and to
reduce the complexity of data analysis and interpretation,
we built PanHunter, our versatile and
interactive multi-omics data analysis platform. It
provides a plethora of tools, visualisations, and
reference information readily available. Via its easily
accessible web interface, it allows our scientists
to immerse with the data, analyse them faster and
more focused, visualise them easier, put them into
context, and derive deep and meaningful interpretations
from them to make well-informed decisions.
PanHunter can be used through its graphical user
interface in any modern web browser. This interactive
interface provides immediate feedback to
user interaction and reduces waiting times between
setting up and interpreting analyses, especially
compared to the classical pipelining approach.
This allows our users to focus on the results
rather than the process of handling the data. Additionally,
the modular structure of PanHunter allows
quick and easy adaption to new tasks and necessity
arising from our internal research projects or
external partner requests.
The main motivation when designing PanHunter
was to reduce existing obstacles for data access as
much as possible and diminish the overhead and
repetitive work usually associated with omics data
analysis. The result is that both, our bioinformaticians
as well as our lab scientists can focus more
on data interpretation and less on data processing.
However, using one general platform for omics data
analysis comes with additional advantages: standardisation,
reproducibility, and collaboration.
A standardised data processing workflow, which
is an integral part of PanHunter, allows for easy
re-purposing of already existing datasets in other
contexts, which eventually increases the value of
each individual experiment. It serves therefore as
an exchange and access platform for the increasPanHunter
data
analytics
30
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