DNA & Blood Profiling (Omics)

Ettan Workstation

Omics Facility

Baker IDI has considerable research expertise in the areas of lipidomics, metabolomics, genomics and epigenomics. The DNA & Blood Profiling Facility (commonly referred to as the "omics") integrates these research disciplines so health problems can be examined from a multifaceted viewpoint.

The central idea behind the new facility is so health problems are understood and resolved in a way that takes into account individual responses to risk factors. This systems biology approach combines the expertise of the scientists involved and the latest in technological advances in the fields enabling a more precise look at complex biological questions. In addition, our Bioinformatics team is developing new computational tools to analyse large data sets in a more lucid and concise manner.

The facility hosts state-of-the-art instruments including the Illumina Genome Analyzer II (next generation sequencing) with particular interest in ChIP Sequencing and RNA Sequencing and the llumina iSCAN (gene expression arrays), as well as a number of specialised mass spectrometers for genotyping/peptide mass fingerprinting, bio-molecule separation and protein profiling.

Researchers are encouraged to take full advantage of the many platforms available at the Omics Facility. Our services are also offered to external academics and commercial organisations, at very competitive rates. For more information, see our full list of platforms and make an enquiry:

Dr Farhad Shafiei
Facility Manager
 Ph: (03) 8532 1423
Email: farhad.shafiei@bakeridi.edu.au

- Illumina GAII
Illumina GAII

Omics Biological Information Flow & Platform Applications

The scientific community is increasingly recognising that multiple data sources (DNA, RNA, protein and metabolites, etc) and sophisticated computational approaches that integrate diverse data sets are required to uncover the hierarchy of molecular, cellular, and tissue-based networks that define complex physiological and disease states. Utilisation of a number of leading technology platforms and applications enables a rapid and precise interrogation of these data sources as illustrated in the figure below. 

 - OmicsBiologicalInformationFlowPlatformApplications

Bioinformatics

Bioinformatics is the study of informatic processes in biotic systems. It is closely linked to systems biology and provides an enabling framework for the generation of useful knowledge out of large complex datasets. Molecular data is deposited in a large central database together with a list of biological characteristics such as phenotypic traits in the case of humans, and experimental manipulation parameters in the case of tissue culture and animal models. Statistical techniques are then used to identify relationships between the phenotype traits/characteristics to provide the investigator with a broad understanding of the disease state in the system under examination (human, animal, cell based).

Step Requirement Approach/procedure Softwares
1 ExperimentSet Up
  • Data formatting and upload to analysis software
  • Normalisation of signal (Quantile). Filter out low intensity non-significant signals against background (P-Value cut-off)
  • Experiment grouping based on study design, reference assignment for interpretation (normalization considerations, such as patient baseline control, etc).
 Illumina Pipeline (for NGS), Genome Studio®, Partek®, GeneSpring GX10R®, Array tools, and R/Bioconductor
2 Quality Control
  • Generate Box Whisker Plot (confirm normalisation method)
  • Principal Component Analysis (test whether sample groups separate on basis of the results)
  • Plot genes along sample groups (visualize the basis of variation found in the test), etc.
 Partek®, GeneSpring GX10R®, Array tools, and R/Bioconductor
3 Gene Selection
  • Analysis of dataset for significant differences between samples/groups. Significance Analysis of Microarrays (SAM), ANOVA, paired t-test, Mann Whitney, Volcano plots (Bayesian)
  • Compare signals of selected genes between and within groups/replicates to confirm clear differences over background variation (noise)
  • Cluster samples via hierarchical clustering on cleaned set of selected genes. Options also include Self Organising Maps, k-means clustering (if you know how many groups to expect), etc.
 Partek®, GeneSpring GX10R®, Array tools and R/Bioconductor
4 Class Prediction
  • Undertake tests to determine ability of selected genes to delineate classes (i.e. biomarker prediction of state) using the following techniques:
  • Support vector machine learning on a validation sample set or by bootstrapping-leave-one-out methodology
  • Recursive feature elimination, training and test sets
  • Kaplan Meier and Cox proportional testing for patient clinical features (classically survival or recurrence)
 NICTA proprietary algorithms, Partek®, GeneSpring GX10R®, Array tools and R/Bioconductor
5 Results Interpretation
  • Place set of observed changes into context of molecular networks and pathways using;
  • Gene Ontology data
  • Pathways (Ingenuity Pathway Analysis®)
  • Gene Set Analysis
  • Cross compare sets of changes with other recorded datasets undertaken in the facility (connectivity MAP)
  • Integrate and evaluate cross platform datasets using multivariate analysis to determine relative contribution of selected gene/lipid/protein sets to delineation of a phenotype (mutually exclusive, correlated, Fisher exact, etc.)
 GeneSpring GX10R®, Array tools, Ingenuity Pathway Analysis
 		      

Bioinformatics Packages

Analysis Packages  Details Cost
Standard  Approaches selected from
steps 1-3		 Depends on selections & softwares used
Advanced  Approaches selected from
steps 1-5 		 Depends on selections & softwares used