Computational genomics

Metagenomics, the study of the genomic diversity of microbes, is increasingly used in food safety, environmental studies, human and animal health. Recent advances in high-throughput sequencing technologies have enabled the characterization and comparison of microbial communities in very diverse environments. One of the major research challenges is gaining insight into the function, structure and organisation of microbial communities. For example, characterising the composition and activity of metagenomes across different individuals (healthy and diseased subjects) is important to understand the role of microbiota in disease development. Can we predict from a gut or skin microbiome whether someone has a disease, a predisposition, or even how progressed the disease is?

To unlock this opportunity, we are creating an AI analytical framework linking microbiome composition to health in order to better comprehend the directions in which the microbiome should be manipulated. The approach is based on the use of data-driven machine learning  to build predictive models from high-dimensional and sparse matrices summarising relative abundances of microbial taxa in samples.  By analysing microbiome data across human, mouse, and environmental samples and applying RoDEO (Robust Differential Gene Expression), combined with machine learning methods, we have been able to predict phenotypes or traits of host organisms accurately. Our approach has the potential to facilitate disease diagnosis and improve the future personalisation of medicine.

Whole-metagenome analysis

Current taxonomic classification methods focus on sequencing specific marker genes, such as 16S rRNA, and rely on existing microbial reference databases, which are often incomplete. A more informative method is whole-metagenome shotgun sequencing, which generates huge collections of short reads. The need to analyse, assemble or align metagenomics reads makes whole-metagenome analysis both data and computation-intensive. We propose a new method for rapidly creating a compact representation of microbiome sequencing data using similarity-preserving sketches of streaming k-mer spectra. These sketches allow for dissimilarity estimation, rapid microbiome catalogue searching and classification of microbiome samples in near real time.

We are also working on applying artificial intelligence to build predictive models that can provide insights for specific cutting-edge applications such as for guiding diagnostics and developing personalised treatments. Current machine-learning workflows that predict traits of host organisms from their commensal microbiome do not take into account the entire genetic material constituting the microbiome, instead basing the analysis on specific marker genes. We are developing a machine-learning workflow that efficiently performs host phenotype prediction from entire shotgun metagenomes by computing similarity-preserving compact representations of genetic material using histoskeching. Our workflow enables prediction tasks, such as classification and regression, from terabytes of raw sequencing data that do not necessitate pre-processing through expensive bioinformatics pipelines.

Plant genomics

Building core regulatory gene networks in hexaploid bread wheat

Wheat is the most prevalently cultivated grain for human consumption. To keep pace with the increase in human population and environmental changes, it is vital to increase and sustain the yield of the crop. There have been sustained efforts for the past couple of years to sequence the wheat genome in order to better understand the molecular mechanisms responsible for key agricultural traits. Although the availability of the recently completed bread wheat genome gives us an insight into its structure and compositional nature in terms of genes and transcripts, the biological functions arising from the expression of transcripts and the interaction of transcriptome networks remain largely unknown. Transcriptional networks drive the biology of an organism, so understanding the architecture of these networks is key to genetically design crops for the future. The wheat genome is about five times the size of the human genome and has more than 100,000 genes. In order to understand wheat as a system, one needs to understand the interplay between genes through a combination of experimental data, prior knowledge, as well as sophisticated data management and computational techniques.

In this project, we aim to create a computational framework around the large-scale transcriptomics datasets to understand key regulatory mechanisms in the wheat genome. In particular, we are focused on the biological process of circadian regulation that has been found to underpin many key agronomic traits, including flowering time, dormancy, water use efficiency, pathogen interaction, nitrogen metabolism and carbon partitioning. Given the complexity of the wheat genome and the lack of community-curated information, the methods developed and scientific insights gained from this project can lead to a better understanding of important biomarkers and molecular traits that can be leveraged by commercial plant breeders to develop improved plant varieties.

Hierarchical clustering derived from a temporal gene expression dataset

Hierarchical clustering derived from a temporal gene expression dataset