Lab

Computational Biomedicine Lab


About the lab

Our goal is to achieve a deeper understanding of processes involved in disease emergence and progression, with a focus on cancer biology, and possible avenues for therapeutic interventions and optimised treatment schedules. Most projects involve the analysis of experimental high-throughput data (incl. (sc)RNA-seq, NanoString, DNA Methyl-Seq, ChIP-Seq, CLIP-seq, and more). Moreover, we use various mathematical formalisms to model the effects of environmental stimuli or pathogenic events on molecular interaction networks and signalling pathways. Using machine learning and computer simulation approaches, we predict disease progression and outcome as well as the effect of therapeutic agents on cellular survival and resistance mechanisms.

Featured research (7)

Alternative splicing (AS) is a crucial mechanism for regulating gene expression and isoform diversity in eukaryotes. However, the analysis and visualization of AS events from RNA sequencing data remains challenging. Most tools require a certain level of computer literacy and the available means of visualizing AS events, such as coverage and sashimi plots, have limitations and can be misleading. To address these issues, we present SpliceWiz, an R package with an interactive Shiny interface that allows easy and efficient AS analysis and visualization at scale. A novel normalization algorithm is implemented to aggregate splicing levels within sample groups, thereby allowing group differences in splicing levels to be accurately visualized. The tool also offers downstream gene ontology enrichment analysis, highlighting ASEs belonging to functional pathways of interest. SpliceWiz is optimized for speed and efficiency and introduces a new file format for coverage data storage that is more efficient than BigWig. Alignment files are processed orders of magnitude faster than other R-based AS analysis tools and on par with command-line tools. Overall, SpliceWiz streamlines AS analysis, enabling reliable identification of functionally relevant AS events for further characterization. SpliceWiz is a Bioconductor package and is also available on GitHub (https://github.com/alexchwong/SpliceWiz).
Background After many years of neglect in the field of alternative splicing, the importance of intron retention (IR) in cancer has come into focus following landmark discoveries of aberrant IR patterns in cancer. Many solid and liquid tumours are associated with drastic increases in IR, and such patterns have been pursued as both biomarkers and therapeutic targets. Paradoxically, breast cancer (BrCa) is the only tumour type in which IR is reduced compared to adjacent normal breast tissue. Methods In this study, we have conducted a pan-cancer analysis of IR with emphasis on BrCa and its subtypes. We explored mechanisms that could cause aberrant and pathological IR and clarified why normal breast tissue has unusually high IR. Results Strikingly, we found that aberrantly decreasing IR in BrCa can be largely attributed to normal breast tissue having the highest occurrence of IR events compared to other healthy tissues. Our analyses suggest that low numbers of IR events in breast tumours are associated with poor prognosis, particularly in the luminal B subtype. Interestingly, we found that IR frequencies negatively correlate with cell proliferation in BrCa cells, i.e. rapidly dividing tumour cells have the lowest number of IR events. Aberrant RNA-binding protein expression and changes in tissue composition are among the causes of aberrantly decreasing IR in BrCa. Conclusions Our results suggest that IR should be considered for therapeutic manipulation in BrCa patients with aberrantly low IR levels and that further work is needed to understand the cause and impact of high IR in other tumour types.
Extensive investigation of gene fusions in cancer has led to the discovery of novel biomarkers and therapeutic targets. To date, most studies have neglected chromosomal rearrangement-independent fusion transcripts and complex fusion structures such as double or triple-hop fusions, and fusion-circRNAs. In this review, we untangle fusion-related terminology and propose a classification system involving both gene and transcript fusions. We highlight the importance of RNA-level fusions and how long-read sequencing approaches can improve detection and characterization. Moreover, we discuss novel bioinformatic tools to identify fusions in long-read sequencing data and strategies to experimentally validate and functionally characterize fusion transcripts.
This book provides an update on the latest development in the field of microRNAs in cancer research with an emphasis on translational research. Since the early 2000s, microRNAs have been recognized as important and ubiquitous regulators of gene expression. Soon it became evident that their deregulation can cause human diseases including cancer. This book focuses on the emerging opportunities for the application of microRNA research in clinical practice. In this context, computer models are presented that can help to identify novel biomarkers, e.g. in circulating microRNAs, and tools that can help to design microRNA-based therapeutic interventions. Other chapters evaluate the role of microRNAs in immunotherapy, immune responses and drug resistance. Covering key topics on microRNAs in cancer research this book is a valuable resource for both emerging and established microRNA researchers who want to explore the potential of microRNAs as therapeutic targets or co-adjuvants in cancer therapies.
Dynamic intron retention (IR) in vertebrate cells is of widespread biological importance. Aberrant IR is associated with numerous human diseases including several cancers. Despite consistent reports demonstrating that intrinsic sequence features can help introns evade splicing, conflicting findings about cell type- or condition-specific IR regulation by trans-regulatory and epigenetic mechanisms demand an unbiased and systematic analysis of IR in a controlled experimental setting. We integrated matched mRNA sequencing (mRNA-Seq), whole-genome bisulfite sequencing (WGBS), nucleosome occupancy methylome sequencing (NOMe-Seq) and chromatin immunoprecipitation sequencing (ChIP-Seq) data from primary human myeloid and lymphoid cells. Using these multi-omics data and machine learning, we trained two complementary models to determine the role of epigenetic factors in the regulation of IR in cells of the innate immune system. We show that increased chromatin accessibility, as revealed by nucleosome-free regions, contributes substantially to the retention of introns in a cell-specific manner. We also confirm that intrinsic characteristics of introns are key for them to evade splicing. This study suggests an important role for chromatin architecture in IR regulation. With an increasing appreciation that pathogenic alterations are linked to RNA processing, our findings may provide useful insights for the development of novel therapeutic approaches that target aberrant splicing.

Lab head

Ulf Schmitz
Department
  • Molecular and Cell Biology
About Ulf Schmitz
  • I’m an Associate Professor in Bioinformatics at the James Cook University (Australia) and an NHMRC Emerging Leadership Fellow (2021-2025). I am a computational biologist with training in bioinformatics and systems biology. My research interests focus on computational RNA biology and Systems Medicine. I develop integrative workflows, databases, and software tools for the analysis of gene regulation in cancer.

Members (2)

Siyuan Wu
  • James Cook University
Chirag Parsania
  • University of Macau

Alumni (2)

Veronika Petrova
Veronika Petrova
Jaynish Shah
  • Kolling Institute of Medical Research