A collection of scripts to predict putative miR-mRNA interactions where miR activity may be associated with melanoma phenotype switching. These scripts can be split by the programming language used:
The MATLAB script find_LMMEL_active_miRs.m (and associated functions wtihin MATLAB_functions), integrates miR target prediction databases (TargetScan 7.1, DIANA-microT CDS) to enrich a statistical analysis (Pearson's correlation, mutual information) of miR and mRNA data from the Ludwig Melbourne melanoma (LM-MEL) cell line panel data. The output from this script includes a number of putative miR:mRNA interactions.
The R scripts included here examine the TCGA SKCM raw data files and create text files which are used as input for the python scripts.
The python script create_TCGA_plots.py is a standalone script which searches for specified miR:mRNA interactions within the TCGA SKCM data and plots figure panels which are matched to specific results identified from the cell line analaysis.
These scripts accompanies the manuscript:
MC Andrews/J Cursons, DG Hurley, M Anaka, JS Cebon, A Behren, EJ Crampin (2016). Systems analysis identifies miR-29b regulation of invasiveness in melanoma. BMC Molecular Cancer, (Accepted Nov 2016).
- doi: to-be-assigned
For further information, please contact:
- Department of Genomic Medicine and Department of Surgical Oncology, MD Anderson Cancer Center, USA
- ex: Cancer Immunobiology Laboratory, Olivia Newton-John Cancer Research Institute, Australia
- mcandrews (at) mdanderson.org
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Australia
- ex: Systems Biology Laboratory, University of Melbourne, Australia
- cursons.j (at) wehi.edu.au
- Systems Biology Laboratory, University of Melbourne, Australia
- daniel.hurley (at) unimelb.edu.au
- Cancer Immunobiology Laboratory, Olivia Newton-John Cancer Research Institute, Australia
- andreas.behren (at) onjcri.org.au
- Cancer Immunobiology Laboratory, Olivia Newton-John Cancer Research Institute, Australia
- jonathan.cebon (at) onjcri.org.au
- Systems Biology Laboratory, University of Melbourne, Australia
- edmund.crampin (at) unimelb.edu.au
For users unfamiliar with python, a Virtual Reference Environment will be available for this
project, containing all scripts, data and documentation in an easily-deployed format.
For further information on Virtual Reference Environments, please refer to the Online Documentation
These scripts use a number of data sources, including:
- Behren et al., Pig. Cell Mel. Res., (2013)
- mRNA abundance data (ArrayExpress)
- miR abundance data (GEO)
- Project Link
- Data Link
- Related Manuscript (Analytical)
- Users may be interested in downloading TCGA SKCM data which are already merged across patients from:
- UCSC Cancer Browser
- Broad Institute's FireBrowse
- NB: the scripts provided are not designed for this input; however the AnalyseTCGA functions can be replaced with a single call to pd.read_table() for the genomicMatrix data
- Project Website
- Data Link
- Agarwal et al., eLife, (2015).
- Chiang et al., Genes Dev., (2010).
- Friedman et al., Genome Res., (2009).
- Fromm et al., Annu. Rev. Genet., (2015).
- Nam et al., Mol. Cell, (2014).
- Garcia et al., Nat. Struct. Mol. Biol., (2011).
- Shin et al., Mol. Cell, (2010).
- Grimson et al., Mol. Cell, (2007).
- Lewis et al., Cell, (2005).
- Project Website
- Data Link NB: a free account with DIANA Tools is required for download
- DIANA Tools account creation
- Paraskevopoulou et al., Nucleic Acids Res. (2013).
- Alexiou et al., Bioinformatics, (2009)
- Maragkakis et al., BMC Bioinformatics, (2009).
- Maragkakis et al., Nucleic Acids Res., (2009).