The NSW Systems Biology Initiative

The New South Wales Systems Biology Initiative was established in mid-2008 with foundation funding from the New South Wales Office for Science and Medical Research (OSMR) and the University of New South Wales.

Our mission is to build capabilities and expertise in bioinformatics for genomics and proteomics, and to disseminate this expertise through collaboration.

We are funded to provide collaborative bioinformatics expertise and services to users of NCRIS-funded genomic and proteomic facilities, specifically the Ramaciotti Centre for Gene Function Analysis, the Bioanalytical Mass Spectrometry Facility (BMSF), the Australian Proteome Analysis Facility (APAF) and Southern Cross Plant Science at Southern Cross University.

We also provide collaborative bioinformatics support to groups at UNSW and elsewhere. For researchers in non-profit organisations in the state of New South Wales, these services are provided free of charge.

Services

We can provide data analysis expertise in the following -omics areas:

Systems Biology & Integration

Contextualisation of data into ontological classes, biochemical and metabolic pathways and networks
Construction of genetic and protein-protein interaction networks
High dimensionality visualisation using custom and off the shelf tools
Interpretation of transcriptomic and proteomic data in a clinical context (epidemiological or individual patients)

Proteomics

LC-MS-MS experimental design
Data collation and summation from large LC-MS-MS experiments
Analysis of data from comparative studies using isotopic labels
Univariate and multivariate statistical analysis of protein expression data from LC-MS-MS analysis and 2-D gels
Interpretation and abstraction of protein identification data
Extraction and normalization of data from 2-D gels to assay inter-experiment reproducibility

Genomics & Transcriptomics

Microarray experimental design – essential to ensure the analysis of adequate sample numbers and replicates to generate valid data
Data extraction and normalization – to ensure inter-experiment reproducibility
Univariate and multivariate statistical analysis of gene expression data – to find genes or groups of genes that are similarly or differentially expressed
Genomic and EST sequence assembly, annotation and analysis
Data interpretation in genetic context, including the Human Haplotype
Data analysis, storage support and sequence analysis for new generation sequencing technology