Bioinformatics

Bioinformatics at the NICB

Bioinformatics at the NICB

The NICB Bioinformatics & Biostatistics Core Facility provides a centralized resource supporting NICB staff and students in the design of experiments to the analysis of data. We also actively collaborate with external researchers both nationally and internationally.

Multiple software packages are available to researchers ranging from windows based software (e.g. Affymetrix genespring) to custom algorithms developed for R/Bioconductor.

The NICB bioinformatics group utilise a wide range of techniques to extract knowledge from complex biological datasets. The group specialise in the application of multivariate statistics and machine learning techniques for the analyses of expression profiling data e.g. mRNA, proteomics and miRNA.

  • Principal Components Analysis
  • Co-interia Analysis
  • Support Vector Machines
  • Partial Least Squares
  • Artificial Neural Networks

An area of particular interest with the groups is large scale transcriptomic data mining to identify transcriptomics associated with patient prognosis in cancer. We have recently developed two websites to allow user friendly analysis of single gene association with prognosis and coexpression analysis.

The Bioinformatics Core Facility is also involved in the Analysis of CHO omics data.

Contact

Padraig Doolan (padraig.doolan@dcu.ie)

 

 

Recent Publications

  • Clarke, C., et al. (2013) Correlating transcriptional networks to breast cancer survival: a large-scale coexpression analysis. Carcinogenesis.

 

  • Madden, S.F., et al. (2013) BreastMark: an integrated approach to mining publicly available transcriptomic datasets relating to breast cancer outcome. Breast Cancer Res, 15, R52.

 

  • Clarke, C., et al. (2012) CGCDB: a web-based resource for the investigation of gene coexpression in CHO cell culture. Biotechnol Bioeng, 109, 1368-70.

 

  • Clarke, C., et al. (2012) Integrated miRNA, mRNA and protein expression analysis reveals the role of post-transcriptional regulation in controlling CHO cell growth rate. BMC Genomics, 13, 656.

 

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