Transcriptomics and Bioinformatics
Our vision is to data-mine our genetics information to unravel the developmental gene regulatory networks that are required to form healthy babies, and that cause congenital malformations if altered.
The Transcriptomics & Bioinformatics group are a multidisciplinary team of computational and molecular biologists who specialise in mining our genomic information, in order to uncover the underlying genetic causes of congenital diseases, such as congenital heart disease (CHD).
The group is particularly focused on decrypting the role of the non-coding genome in development and disease, via developing novel software, data mining pipelines and conducting bioinformatics research in collaboration with other laboratories at MCRI and beyond.
Challenges facing children and adolescents
The successful formation of healthy babies relies on a network of genes that must be activated at exact times and in specific territories during embryonic development. Failure to activate this network at the right time and right place will result in congenital malformations.
Therefore, it is imperative that we:
- Identify the genetic components of this gene regulatory network, and
- Understand where and when these genes are recruited, in order to fully understand how healthy babies develop and how genetic malformations may arise.
Our current research
To address this challenge, the research team applies systems biology approaches (the study of biological components, be it molecules, cells, organisms or entire species), to reconstruct the developmental gene regulatory networks that are required to form healthy babies, and that cause congenital malformations if altered.
The group develops novel software for -omics data mining (genomics end epigenomics, single cell and spatial transcriptomics, networks) and 3D data visualisation platforms, making complex high-throughput data intuitive and interpretable by expert researchers, clinicians and lay audiences alike.
Most importantly, the group collaborates with life scientists all around the world to exploit the power of Bioinformatics to enable breakthrough discoveries in our understanding of embryonic development and congenital disease.
Future impact on children and adolescents
Our bioinformatics research delivers enabling bioinformatics resources to collaborating laboratories, who employ these resources to drive better insights into genes and their contributions to health and diseases in babies.
Our data mining research also helps derive gene panels with proven predictive power which are essential for producing reliable diagnostics tests for parents and babies.
Group Leaders
Group Members
Our projects
Investigating the role of non-coding cis-regulatory elements in congenital heart disease
Congenital heart diseases (CHD) are the major cause of death in newborns, but the genetic aetiology of this developmental disorder is not fully known. This project develops an efficient pipeline of genome-wide gene discovery based on the identification of a cardiac-specific cis-regulatory element signature that points to candidate genes involved in heart development.
Our pipeline has enabled the discovery of novel genes with roles in heart development. This workflow, which relies on screening for non-coding cis-regulatory signatures, is amenable for identifying developmental genes for an organ without constraining to genes that are expressed exclusively in the organ of interest.
Modelling Gene Regulatory Networks Underlying Early Kidney Development and Kidney Organoids
Kidney organoids are influenced by complex genetic circuitries, and systems-level investigation of the underlying gene regulatory network could provide a key to explaining the heterogeneity observed during the organoid formation process.
By employing recent advances in literature mining and network modelling techniques, we could now perform a systematic investigation of the complex gene regulatory network involved in kidney organoid formation. The novel model provides an opportunity to study and engineer the networks required to generate kidney organoids in a robust and reproducible manner.
Spatially resolved transcriptomics in immersive environments
Spatially resolved transcriptomics is an emerging class of high-throughput technologies that enable biologists to systematically investigate the expression of genes along with spatial information. Upon data acquisition, one major hurdle is the subsequent interpretation and visualization of the datasets acquired.
To address this challenge, we develop a novel data visualization system with interactive functionalities designed to help biologists interpret spatially resolved transcriptomic datasets.
Using our system, biologists can interact with the data in novel ways not previously possible, such as visually exploring the gene expression patterns of an organ, and comparing genes based on their 3D expression profiles.
Funding
- National Health and Medical Research Council (NHMRC)
- Australia Research Council (ARC)
- Novo Nordisk Foundation Center for Stem Cell Medicine, reNEW (Grant Number NNF21CC0073729)
- CSL Limited
- Australian Genome Research Facility
- Rotary International
- Sun Foundation
Collaborations
- Murdoch Children’s Research Institute, Melbourne Australia
- Australian Regenerative Medicine Institute, Melbourne Australia
- Monash University, Melbourne Australia
- The University of Melbourne, Melbourne Australia
- University of Tasmania, TAS Australia
- University of Queensland, UQ Australia
- Victor Chang Cardiac Research Institute, NSW Australia
- Garvan Institute, NSW Australia
- Children’s Medical Research Institute, NSW Australia
- Jackson Laboratories, Maine USA
- Harvard Medical School, Boston USA
- European Molecular Biology Laboratory, Heidelberg Germany
- Hubrecht Institute, The Netherlands
- University of Heidelberg, Germany
- University of Konstanz, Germany
- University of Trento, Italy
- University of Campinas, Brazil
- University of Chile, Chile
- University of California Santa Cruz, USA
- Columbia University, NY USA
Featured publications
Nim HT, Dang LT, Thiyagarajah H, Bakopoulos D, See M, Charitakis N, Sibbritt T, Eichenlaub MP, Archer SK, Fossat N, Burke RE, Tam PPL, Warr CG, Johnson TK#, Mirana Ramialison#. A cis-regulatory-directed pipeline for the identification of genes involved in cardiac development and disease. Genome Biology 2021; 22:335.
Xin Z, Cai Y, Dang LT, Burke HMS, Revote J, Nim HT#, Li YF# and Ramialison M#. MonaGO: a novel Gene Ontology enrichment analysis visualisation system. BMC Bioinformatics 2022; 23:69.
Mohenska M, Tan NM, Tokolyi A, Furtado MB, Costa MW, Perry AJ, Hatwell-Humble J, van Duijvenboden K, Nim HT, Ji YMM, Charitakis N, Bienroth D, Bolk F, Vivien C, Knaupp AS, Powell DR, Elliott DA, Porrello ER, Nilsson SK, del Monte-Nieto G, Rosenthal NA, Rossello FJ, Polo JM# and Ramialison M#. 3D-Cardiomics: A spatial transcriptional atlas of the mammalian heart. JMCC 2022; 163:20-32.
Dang LT, Tondl M, Chiu MHH, Revote J, Paten B, Besse F, Quaife-Ryan G, Tano V, Cumming H, Drvodelic MJ, Eichenlaub MP, Hallab JC, Nim HT, Stolper JS, Tokolyi A, Bogoyevitch MA, Jans DA, Porrello ER, Hudson JE, Ramialison M. TrawlerWeb: an online de novo motif discovery tool for next-generation sequencing datasets. BMC Genomics 2018;19(1):238.
Abbas A#, Ramialison M#^, Currie P#^. Integrated Value of Influence: an integrative method for the identification of network most influential nodes. Patterns 2020; 1(5).