Genome Insights

Natalie Twine

Natalie Twine
Team Leader
Genome Insights

Generate insights into genome-trait relations by analyzing population-scale ‘omics (genomics, transcriptomics, methylomics) and integrating with observational data.

 

Our Science

Genomic information is increasingly being used for medical research, giving rise to the need for efficient analysis methodologies able to cope with thousands of individuals and millions of genomic variants. We have developed a suite of tools utilizing machine learning, BigData technology and serverless cloud-computing to enable real-time genomic data analysis. Our innovative tool portfolio aids human health research in a number of ways. Researchers and clinicians need rapid discovery of novel disease variants: We have thus developed VariantSpark which can identify a novel disease variant in thousands of patient samples in just 30 minutes. Following this, a clinician/researcher can conduct a worldwide search of newly discovered variants in other published datasets using our Serverless-Beacon tool. Fast and accurate identification of distant relatives in sample cohorts assists in accurate disease gene discovery, which can be achieved by our tool, TRIBES. Moving into the clinic, our GenPhenInsights tool can rapidly integrate large cohort genetic and phenotypic data in a secure way, thereby assisting clinicians to make an accurate interpretation of genetic information.

Our Solutions

  • VariantSpark: Machine learning library for biomarker detection on large genome-wide cohorts
  • TRIBES: detecting distant relatedness in genomic cohorts
  • Serverless Beacon: exchanging genotype information in a scalable and confidential manner
  • GenPhen Insights: clinical decision support tool bringing genomic and phenotypic data together with Machine Learning

Case Studies

March 20, 2018

Using cloud-based distributed computing to uncover the molecular origins of dementia and ALS

Case study, Genome Insights Case Study, , , , , , , ,

Advanced bioinformatics tools help sift through millions of genomic mutations to discover the origins of dementia and related neurodegenerative diseases as part of a network of national and international experts. The Challenge:...

Team Members:

Natalie Twine

Arash Bayat

Yatish Jain

Mischa Lundberg

 

 

 

 

 

 

Natalie Twine
Team Leader and Research Scientist – developing genomic technologies with a focus on neurodegenerative disease, ALS.
CSIRO Profile

Arash Bayat
Post-doctoral Scientist researching machine learning approaches to genome-wide association studies (VariantSpark).
CSIRO Profile

Yatish Jain
Solutions Engineer building platforms for multiple projects, including GenPhenInsight and Drop.

Micha Lundberg
PhD Student with QIMR, extending functionality for VariantSpark and GenPhenInsight.

Publications

  1. O’brien, Aidan; Saunders, Neil; Guo, Yi; Buske, Fabian; Scott, Rodney; Bauer, Denis. VariantSpark: Population Scale Clustering of Genotype Information. BMC Genomics. 2015; 16(1052 1):1. https://doi.org/10.1186/s12864-015-2269-7
  2. Project MinE Consortium; Bauer, Denis; Twine, Natalie. Genome-wide Analyses Identify KIF5A as a Novel ALS Gene. Neuron. 2018; 97(6 e.6):1268-1283. https://doi.org/10.1016/j.neuron.2018.02.027
  3. Lai, Kaitao; Twine, Natalie; O’Brien, Aidan; Guo, Yi; Bauer, Denis. Artificial Intelligence and Machine Learning in Bioinformatics. In: Michael Gribskov, editor/s. Encyclopedia of Bioinformatics and Computational Biology. Elsevier; 2019. 272-286. https://doi.org/10.1016/B978-0-12-809633-8.20325-7
  4. Bauer, Denis; Gaff, Clara; Dinger, Marcel; Caramins, Melody; Buske, Fabian; Fenech, Michael; Hansen, David; Cobiac, Lynne. Genomics and personalised whole-of-life healthcare. Trends in Molecular Medicine. 2014; 20(9):479-486. https://doi.org/10.1016/j.molmed.2014.04.001