Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

8.1 Principles Results
say, this is a flawed approach triggered by the absence of a unifying common
theme in the prediction algorithms that would make them more accurate in
their predictions. Rationally, if several algorithms can be developed that yield
vastly different results, then we are missing an important variable in our equa-
tion. In this perspective, the existing prediction algorithms are not capable of
simulating accurately what is happening in Nature time after time in the same
reproducible way. Having said this, we were interested in finding out if a single
prediction algorithm or a superset of several prediction algorithms was best at
predicting microRNA to mRNA annealing. During this analysis we stumbled
upon an observation that might be a hint towards the identity of the missing
variable in the prediction algorithm equation.
In order to determine which set of prediction algorithms were best at predict-
ing microRNA:mRNA annealing, we needed experimentally validated data to
help us screen the positives from the false positives, which contribute greatly
to the pool of predictions. We used exon array data and microRNA array
data that was available to us from our GNS cell lines (Appendix E and F) to
validate our findings. Since a large number of prediction algorithms are com-
monly used in research and they each predict a vast number of interactions,
we built a tool called "GenemiR" to allow molecular biologists to generally
access and manage microRNA predictions on a large scale and across differ-
ent algorithms that would also help us address our research question. The
GenemiR software exists as a command line binary executable for Unix-based
systems or as a compiled local installation for Windows. Due to its length,
the code of the binary executable could not be added to the appendix of this
thesis, but it is available for anyone to view or download at www.ebi.ac.
uk/~diva/GenemiR/genemir-code.txt. The executable for Windows can be
downloaded at www.ebi.ac.uk/~diva/GenemiR/GenemiR.zip.
In order to address the need for unified search and presentation of microRNA
to mRNA target interactions predicted across multiple algorithms, GenemiR
relates human and mouse microRNAs with their predicted target genes as
reported by eight leading predictors. At the core of the program are two
primitives that allow it to be extremely fast in extrapolating lists of gene tar-
gets given a microRNA and, performing the reciprocal analysis, extrapolating
lists of microRNAs predicted to repress one or more genes of interest (Fig 8.1).
Many other auxiliary functions and filters allow the expert user to parametrize
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