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The PanHunter Platform
bolic pathways or protein interaction networks that
are significantly regulated within the tested data.
Such networks can reveal the underlying molecular
mechanisms for a disease or drug treatment.
Whenever performing such statistical methods,
PanHunter applies well-selected default parameters,
while being always transparent about them
and providing the option to customise. This allows
us to serve all scientists from a broad experience
spectrum: new users to the field of omics data
analysis will be guided and can rely on the default
settings while experienced users can customise
almost any small details of an analysis.
While the user gets empowered to perform
a plethora of different analysis via the GUI of
PanHunter that requires no coding or scripting
skills, all other aspects, like managing the data and
carrying out the analysis processes, are handled in
the background. The omics data are stored in a fast
yet secure data layer. In addition to the experimental
and meta data provided by the user, this data
layer handles and provides access to all additional
information already mentioned above.
The processing, analysis, and interpretation of
omics data involves a series of sequential steps, all
of which are covered by PanHunter. As soon as new
omics data are generated, e.g., via mRNA library
sequencing using next-generation sequencing
(NGS), the data need to be pre-processed to generate
a so-called feature abundance table. In case of
RNA-Seq, this table contains for all samples the
CLINICAL DATA
vital_status
Alive
Dead
pr_status/er _status
Negative
Positive
Indeterminate
Unknown
EXPRESSION DATA
Intensity of gene expression
5
-5
her2_status
Equivocal
Negative
Positive
Indeterminate
Unknown
age
40 60 80
Figure 4: Global view and correlation of clinical vs. transcriptomic data: tumour tissue samples from the public breast cancer
cohort of The Cancer Genome Atlas (TCGA) are shown in PanHunter and allow to associate gene expression with clinical
parameters. Hereby the user is able to identify potential connections in a global view on gene expression.
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evotec_Nov23_PanOmicsDriven

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