Computational
Genomics

"We live in an era of unprecedented
amounts of biological and genetic data.
Using computational methods we can
now derive insight from these datasets
to improve medical decision-making,"

- Dr Eleni Giannoulatou 

dr Eleni Giannoulatou 

head, computational genomics laboratory

Research Overview

Key Research Areas

  • Bioinformatics
  • Statistical Genetics
  • Analysis of high-throughput genomic datasets
  • Congenital heart disease

Research Overview

High-throughput genome sequencing technologies have revolutionised our understanding of human genetic diseases. What we need now is the new computational approaches to catch up to recent advances in this sequencing technology. This will enable researchers to explore massive datasets more easily and translate the insights contained within to help realise a future where personalized medicine based on individual genomes is the norm.

Dr Giannoulatou and her team analyse large amounts of genomic data to identify genetic causes of disease and understand fundamental mechanisms in biology. The main focus of her team is to identify the genetic causes of congenital heart disease, the most common birth defect which affects more than 2,000 babies in Australia every year.

research projects

There are 3 key projects underway in the Computational Genomics Laboratory, led by Dr Eleni Giannoulatou;

1. Identification of genetic causes of Congenital Heart Disease

 Congenital heart disease (CHD) defines a large set of structural and functional deficits that arise during cardiac embryogenesis affecting 8 out of 1,000 live births. The cause of 80% of the CHD cases remains unknown. We develop quantitative approaches to analyse large sequencing datasets aiming to understand the genetic mechanisms underlying CHD. This project is part of a large collaborative study with Prof Sally Dunwoodie.

 2. Understanding the emergence of de novo mutations

Mutations causing birth defects can be inherited from affected parents or can arise randomly as the embryo develops within the womb. Depending on the time of the occurrence of these de novo mutations (DNMs), the risk of disease occurring in offspring may be higher than expected.

DNMs can occur in either the parental germline (i.e. sperm or egg) or post-zygotically during embryogenesis. Using computational methods, we aim to identify to understand when DNMs occur and how they emerge.

3. Development of novel bioinformatics methodology to analyse and integrate genomic datasets

We develop state-of-the-art computational methods to analyse and integrate genomic data.  These include novel statistical machine learning methodology as well as robust computational approaches. Applications include in-house medical genomics projects such the Congenital Heart Disease whole genome sequencing project (in collaboration with Prof Sally Dunwoodie) as well as other large genomic studies. 

laboratory members & collaborators

Laboratory Members

Eddie Ip, PhD Student

Jacob Munro, Research Assistant

Michael Troup, Research Assistant

Collaborators

Prof David Winlaw, University of Sydney & The Children’s Hospital at Westmead

A/Prof Anne Goriely, University of Oxford

Prof Andrew Silver, University of London

Prof George Fountzilas, Aristotle University of Thessaloniki

publication highlights

1. Giannoulatou E, Maher GJ, Ding Z, Gillis AJM, Dorssers LCJ, Hoischen A, Rajpert-De Meyts E, WGS500 Consortium , McVean G, Wilkie AOM, Looijenga LHJ, Goriely A, “Whole-genome sequencing of spermatocytic tumors provides insights into the mutational processes operating in the male germline”, PLOS ONE, 2017, [Epub ahead of print]

2. Lau E, Giannoulatou E, Celermajer D, Humbert M, "Epidemiology and treatment of pulmonary arterial hypertension", Nature Reviews Cardiology, 2017, accepted, 19/5/2017

3. Munro JE, Dunwoodie SL, Giannoulatou E, “SVPV: a structural variant prediction viewer for paired-end sequencing datasets”, Bioinformatics, 2017, [Epub ahead of print]

4. Blue GM, Kirk EP, Giannoulatou E, Sholler GF, Dunwoodie SL, Harvey RP, Winlaw DS, “Advances in the Genetics of Congenital Heart Disease: A Clinician's Guide”, Journal of the American College of Cardiology, 2017, 69(7):859-870

5. Fountzilas G, Giannoulatou E, Alexopoulou Z, Zagouri F, Timotheadou E, Papadopoulou K, Lakis S, Bobos M, Poulios S, Sotiropoulou M, Lyberopoulou A, Gogas H, Pentheroudakis G, Pectasides D, Koutras A, Christodoulou C, Papandreou C, Samantas E, Papakostas P, Kosmidis P, Bafaloukos D, Karanikiotis C, Dimopoulos MA, Kotoula V, “TP53 mutations and protein immunopositivity may predict for poor outcome but also for trastuzumab benefit in patients with early breast cancer treated in the adjuvant setting”, Oncotarget, 2016, in press, Epub ahead of print.

6. The Kenyan Bacteraemia study group and Wellcome Trust Case Control Consortium 2 including Giannoulatou E, “Polymorphism in a lincRNA associates with a doubled risk of pneumococcal bacteremia in Kenyan children”, American Journal of Human Genetics, 2016, in press. Epub ahead of print.

7. Maher GJ, McGowan SJ, Giannoulatou E, Verrill C, Goriely A, Wilkie AOM, “Visualizing the origins of selfish de novo mutations in individual seminiferous tubules of human testes”, Proceedings of the National Academy of Sciences of the United States of America, 2016, 113(9):2454-2459.

8. Blue GM, Kirk EP, Giannoulatou E, Dunwoodie S, Ho JWK, Hilton DCK, White SM, Sholler GF, Harvey RP, Winlaw DS, “Targeted Next-generation Sequencing Identifies Pathogenic Variants in Familial Congenital Heart Disease”, Journal of the American College of Cardiology, 2014, 64(23):2498-2506. 

9. Giannoulatou E, Park SH, Humphreys DT, Ho JWK, “Verification and validation of bioinformatics software without a gold standard: A case study of BWA and Bowtie”, BMC Bioinformatics, 2014, 15 (Suppl 16), S15.

10. Armitage AE*, Stacey AR*, Giannoulatou E*, Marshall E, Sturges P, Chatha K, Pasricha SR , Prentice AM, Webster C, Pellegrino P, Williams I, Norris PJ, Drakesmith H, Borrow P, “Distinct patterns of hepcidin and iron regulation during HIV- 1, HBV and HCV infections”, Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(33):12187-12192. *joint first authors

11. Morris DW, Pearson RD, Cormican P, Kenny EM, O'Dushlaine CT, Lemieux Perreault LP, Giannoulatou E, Tropea D, Maher BS, Wormley B, Kelleher E, Fahey C, Molinos I, Bellini S, Pirinen M, Strange A, Freeman C, Thiselton DL, Elves RL, Regan R, Ennis S, Dinan TG, McDonald C, Murphy KC, O'Callaghan E, Waddington JL, Walsh D, O'Donovan M, Grozeva D, Craddock N, Stone J, Scolnick E, Purcell S, Sklar P, Coe B, Eichler EE, Ophoff R, Buizer J, Szatkiewicz J, Hultman C, Sullivan P, Gurling H, McQuillin A, St Clair D, Rees E, Kirov G, Walters J, Blackwood D, Johnstone M, Donohoe G; International Schizophrenia Consortium; SGENE+ Consortium, O'Neill FA; Wellcome Trust Case Control Consortium 2, Kendler KS, Gill M, Riley BP, Spencer CC, Corvin A, “An inherited duplication at the gene p21 Protein-Activated Kinase 7 (PAK7) is a risk factor for psychosis”, Human Molecular Genetics, 2014, 23(12):3316-26.

12. Hughes JR, Roberts N, McGowan S, Hay D, Giannoulatou E, Lynch M, De Gobbi M, Taylor S, Gibbons R, Higgs DR, “Analysis of hundreds of cis-regulatory landscapes at high resolution in a single, high-throughput experiment”, Nature Genetics, 2014, 46(2):205-12.

13. Swiers G, Baumann C, O'Rourke J, Giannoulatou E, Taylor S, Joshi A, Moignard V, Pina C, Bee T, Kokkaliaris KD, Yoshimoto M, Yoder MC, Frampton J, Schroeder T, Enver T, Göttgens B, de Bruijn MF, “Early dynamic fate changes in haemogenic endothelium characterized at the single-cell level”, Nature Communications, 2013, 4:2924.

14. Giannoulatou E, McVean G, Taylor IB, McGowan SJ, Maher GJ, Iqbal Z, Pfeifer SP, Turner I, Burkitt Wright EM, Shorto J, Itani A, Turner K, Gregory L, Buck D, Rajpert-De Meyts E, Looijenga LH, Kerr B, Wilkie AO, Goriely A, “Contributions of intrinsic mutation rate and selfish selection to levels of de novo HRAS mutations in the paternal germline”, Proceedings of the National Academy of Sciences of the United States of America, 2013, 4:2924.

15. Zhang YH, Zhao Y, Li N, Peng YC, Giannoulatou E, Jin RH, Yan HP, Wu H, Liu JH, Liu N, Wang DY, Shu YL, Ho LP, Kellam P, McMichael A, Dong T, “Interferon-induced transmembrane protein-3 genetic variant rs12252-C is associated with severe influenza in Chinese individuals”, Nature Communications, 2013, 4:1418.

16. Twigg SR, Babbs C, van den Elzen ME, Goriely A, Taylor S, McGowan SJ, Giannoulatou E, Lonie L, Ragoussis J, Sadighi Akha E, Knight SJ, Zechi-Ceide RM, Hoogeboom JA, Pober BR, Toriello HV, Wall SA, Rita Passos-Bueno M, Brunner HG, Mathijssen IM, Wilkie AO, “Cellular interference in craniofrontonasal syndrome: males mosaic for mutations in the X-linked EFNB1 gene are more severely affected than true hemizygotes”, Human Molecular Genetics, 2013, 22(8):1654-62.

17. Golubchik T, Brueggemann AB, Street T, Gertz RE Jr, Spencer CC, Ho T, Giannoulatou E, Link-Gelles R, Harding RM, Beall B, Peto TE, Moore MR, Donnelly P, Crook DW, Bowden R, “Pneumococcal genome sequencing tracks a vaccine escape variant formed through a multi-fragment recombination event”, Nature Genetics, 2012, 44(3):352-5.

18. International Multiple Sclerosis Genetics Consortium; Wellcome Trust Case Control Consortium 2 including Giannoulatou E, “Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis”, Nature, 2011, 476(7359):214-9.

19. Wellcome Trust Case Control Consortium including Giannoulatou E, “Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls”, Nature, 2010, 464(7289):713-20.  

20. Giannoulatou E, Yau C, Colella S, Ragoussis J, Holmes CC, “GenoSNP: a variational Bayes within-sample SNP genotyping algorithm that does not require a reference population”, Bioinformatics, 2008, 24(19):2209-14.

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