Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

8.2 Workflows Results
and optimise the analysis to make it as stringent or as loose as necessary. The
target predictions in GenemiR’s database can be queried through a variety of
selectable filters, allowing the user to include or exclude any combination of
algorithms, group multiple microRNAs into the same query, and retrieve vari-
able subsets of targeted genes (Fig 8.1). Finally, GenemiR allows the matching
of prediction data to any gene expression dataset of interest, and provides a fa-
cility for loading external datasets determined by the user. Graphical plotting
functions display target expression levels relative to the conditions of the ex-
periment, such as tissue-specific transcriptional profiling or time course series.
The software itself does not contain internal experimental data, but simply
aggregates the output of prediction algorithms in a manner suitable to explo-
ration and hypothesis testing, reducing the complexity of this approach by
integrating microRNA and gene annotations, genome-wide expression data,
and regulatory target predictions in a common analysis framework (Fig 8.1).
8.2 Workflows
The GenemiR software allows for the discovery of systemic or tissue-specific
patterns that may be hidden in the microRNA targeting prediction data from
eight leading algorithms. In order to do this, two flows of information are estab-
lished that address reciprocal biological questions: "which genes are targeted
by one or more specific microRNAs?" and "which microRNAs are predicted
to target a given set of genes?". These issues are intimately related within
the context of the same biological system, but are organised as two opposite
flows of information in terms of program operation and layout (Fig 8.2). De-
pending on the type of query executed against the internal databases, a list
of genes predicted to be targeted by a number of microRNAs according to a
customer-defined selection of prediction algorithms, is generated (Workflow 1,
figure 8.3). In the opposite workflow, a list of microRNAs is generated start-
ing from a list of gene symbols or Genbank, RefSeq, EMBL, ENSG or ENST
identifiers, according to the customer-defined selection of prediction algorithms
(Workflow 2, figure 8.3). Conversion from the identifiers contained originally
in the prediction files is constantly active in both workflows to always translate
the queries to a human intelligible list of gene symbols.
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