Transcriptomics and Bioinformatics
Decoding the gene networks that drive healthy human development and understanding how their disruption leads to conditions such as congenital heart disorders and childhood cancers using innovative bioinformatics approaches.
The Transcriptomics & Bioinformatics group is a multidisciplinary team of computational and molecular biologists who specialise in mining genomic information to uncover the genetic causes of childhood diseases such as congenital heart disease (CHD) and cancer.
The group focuses on understanding the role of the non-coding genome in development and disease by developing novel software, data-mining pipelines, and conducting bioinformatics research in collaboration with laboratories at MCRI and beyond.
Challenges facing children and adolescents
The formation of healthy babies relies on a network of genes activated at precise times and locations during embryonic development. Disruption of this network can lead to congenital malformations.
Therefore, it is essential to:
- Identify the genetic components of this regulatory network
- Understand where and when these genes are activated to better understand healthy development and the origins of genetic malformations.
Our current research
The team applies systems biology approaches to reconstruct developmental gene regulatory networks required for healthy development and to understand how alterations lead to congenital malformations.
The group develops new software for -omics data mining (genomics, epigenomics, single-cell and spatial transcriptomics, networks) and 3D data visualisation platforms, making complex data intuitive for researchers, clinicians, and broader audiences.
The group collaborates with life scientists and clinicians worldwide to harness bioinformatics for breakthrough discoveries in embryonic development and congenital disease, driving the next generation of translational medicine.
Future impact on children and adolescents
Our bioinformatics research provides essential resources to collaborating laboratories, enabling deeper insights into genes and their roles in health and disease.
Our data-mining work also supports the development of gene panels with proven predictive power, which are vital for reliable diagnostic tests for parents and babies.
reNEW Bioinformatics Hub
The Transcriptomics and Bioinformatics group hosts the reNEW Bioinformatics Hub, co-directed by Prof Mirana Ramialison and A/Prof Fernando Rossello, as part of the global Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW). Through this collaboration, our bioinformaticians provide integrated analytical support across three nodes:
- Murdoch Children’s Research Institute (MCRI), Australia
- Leiden University Medical Center (LUMC), Netherlands
Contact us
Mirana Ramialison, Group Leader
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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)
- Novo Nordisk Foundation Center for Stem Cell Medicine, reNEW (Grant Number NNF21CC0073729)
- Heart Foundation
- Human Frontiers Science Programme
Collaborations
- Murdoch Children’s Research Institute, Melbourne Australia
- Australian Regenerative Medicine Institute, Melbourne Australia
- Monash University, Melbourne Australia
- The University of Melbourne, Melbourne Australia
- Peter MacCallum Cancer Centre
- 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
- University of Copenhagen, Copenhagen Denmark
- 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
Bienroth D, Charitakis N, Wong D, Zhang YC, Jaeger-Honz S, Ding J, Watt KI, Stolper J, Chambers-Smith H, MacGregor D, Christiansen B, Vivien C, Piers AT, Waylen LN, Hoffmann LB, Tang J, La HM, Du MRM, Mohenska M, Polo JM, Grimmond S, Scott E, Rossello FJ, Porrello ER, Klein K, Nim HT, Elliott DA, Schreiber F, Ramialison M. Automated integration of multi-slice spatial transcriptomics data in 2D and 3D using VR-Omics. Genome Biol. 2025 Jul 2;26(1):182.
Kwon J, He GZ, Ramialison M*, Nim HT*. A Web-Based Workflow for Selecting Gene- and Tissue-Specific Enhancers. Journal of Visualized Experiments (221), e66840, doi:10.3791/66840 (2025)
Chahal G, Eichenlaub MP, Tondl M, Pawlak M, Mohenska M, Grimm L, Bottrell L, Drvodelic M, Alaei S, Hallab J, Waylen LN, Polo JM, Blanpain C, Palpant N, Rossello FJ, Änkö ML, Currie PD, Hogan BM, Winata C, Salimova E, Nim HT, Ramialison M. Epigenomics and transcriptomics profiles of developing zebrafish heart cells. Sci Data. 2025 Oct 7;12(1):1620. doi: 10.1038/s41597-025-05895-9. PMID: 41057362; PMCID: PMC12504427.
Nunes Santos L, Sousa Costa ÂM, Nikolov M, Carvalho JE, Coelho Sampaio A, Stockdale FE, Wang GF, Andrade Castillo H, Bortoletto Grizante M, Dudczig S, Vasconcelos M, Rosenthal N, Jusuf PR, Nim HT, de Oliveira P, Guimarães de Freitas Matos T, Nikovits W Jr, Tambones IL, Figueira ACM, Schubert M, Ramialison M, Xavier-Neto J. Unraveling the evolutionary origin of the complex Nuclear Receptor Element (cNRE), a cis-regulatory module required for preferential expression in the atrial chamber. Commun Biol. 2024 Apr 4;7(1):371. doi: 10.1038/s42003-024-05972-6. PMID: 38575811; PMCID: PMC10995137.
Charitakis N, Salim A, Piers AT, Watt KI, Porrello ER, Elliott DA, Ramialison M. Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods. Genome Biol. 2023 Sep 18;24(1):209. doi: 10.1186/s13059-023-03045-1. PMID: 37723583; PMCID: PMC10506280.
Xin Z, Cai Y, Dang LT, Burke HMS, Revote J, Charitakis N, Bienroth D, Nim HT, Li YF, Ramialison M. MonaGO: a novel gene ontology enrichment analysis visualisation system. BMC Bioinformatics. 2022 Feb 14;23(1):69. doi: 10.1186/s12859-022-04594-1. PMID: 35164667; PMCID: PMC8845231.
Ramialison M. Human specificity encoded in the dark matter of the genome. Nat Cardiovasc Res. 2022 Sep;1(9):794-795. doi: 10.1038/s44161-022-00129-2. PMID: 39196081.
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#, Ramialison M#. 3D-Cardiomics: A spatial transcriptional atlas of the mammalian heart. JMCC 2022; 163:20-32.
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.