Genome to phenome: exploiting multi-omics and deep learning strategies to decipher importance of isoforms in health and behaviour

Overview

The molecular process that leads to multiple mRNA transcripts being produced from the same segment of DNA (aka gene) is known as alternative splicing (AS). This is a common form of regulation in higher eukaryotes, enabling the production of novel protein isoforms, which in turn are known to have a big impact on phenotype. Understanding the regulatory factors involved in AS, including epigenetic mechanisms such as DNA methylation, will offer key insights into important biological phenomena (health disease, behaviour, production). An interesting aspect is the epigenetic inheritance and the possibility of predicting a phenotype from methylation data.

Traditionally, AS and the detection of protein isoforms has been difficult to study due to limitations in short-read DNA sequencing technology and data-dependent proteomics workflows. Making use of recent technological advancements in the fields of Next Generation Sequencing (NGS) and Proteomics, especially long-read sequencing and data independent acquisition mass spectrometry. We have developed techniques which help to address overcome some of these issues and there is a room for improvement and their application to different animal models (mouse, sheep, daphnia and moth).

This project will utilise and improve upon the current pipeline to study the role that epigenetic processes play in the regulation of AS and protein isoform abundance using animal models of disease, behaviour and inheritance. It is anticipated that work carried out in this project will have widespread applications across many biological disciplines.

Research activities

The largest component of this project will be the utilisation of existing bioinformatics software to improve current NGS and proteomics analysis pipelines. The development of novel bioinformatics tools may also be necessary if suitable software is deemed to be lacking. In addition, a small amount of new data may need to be generated, primarily for reproducibility and validation purposes. If so, this will involve wet-lab activities such as the preparation of tissue samples for long-read genomic sequencing and mass spectrometry-based proteomics data acquisition.

Outcomes

Most of the data required to successfully carry out the project will be provided, in some cases additional data will need to be generated using technology available at QUT’s CARF. Subsequently, data will need to be analysed in an integrated (multi-omics) manner. This will likely include machine learning algorithms and development of new analytical software/bioinformatics pipelines.

In addition, to enable data reproducibility and pipeline inter-usability, use of Common Workflow Language (CWL), as well as containerisation software such as Docker, will be key components of the work. All these outcomes should lead to publications in high-ranking scientific journals.

Skills and experience

You should have a good understanding of R statistical package and quantitative data analysis.

Additional skills required depend on the aspect of the project. There are three aspects of the project:

1. Analysis of already available data, bioinformatics, data mining.
   For this aspect we are looking for a student with experience and interest in bioinformatics analysis of quantitative data.
2. Collection of additional data, experimental molecular biology.
   For this aspect we are looking for a student with experience and interest in molecular biology techniques
3. Development of new tools, technology, tool advancement.
   Background with either mass spectrometry or NGS or shiny apps.

Scholarships

You may be eligible to apply for a research scholarship.

Explore our research scholarships

Keywords

• epigenetics
• proteomics
• genomics
• bioinformatics
• alternative splicing
• multi-omics

Contact

Contact the supervisor for more information.