Pattern recognition algorithms for the analysis of metagenomes
Recent studies of microorganisms and microbial communities (groups of various microbial populations) have revealed links to human physiology and health; to novel enzymes for biotechnology processes; and to the remediation of pollutants and waste products. However, current knowledge of microorganisms is limited to less than 1% of extant microbial diversity, due to the inability to culture the majority of microbes under laboratory conditions. With recent advances in DNA sequencing, it has become increasingly feasible to sequence the collective DNA of an entire community of microbial genomes. Metagenomics allows questions to be asked about the overall functionality of a microbial community, and in particular, allows access to novel microoraganisms that cannot be obtained in pure culture.
This project investigates novel computational methods for the analysis of sequenced metagenomic DNA, with specific focus on binning short-read metagenomic data. This binning problem arises due to a large number of microbial genomes that are randomly fragmented and mixed together during library preparation. Since knowledge of the original genome sequences is not available in a metagenome, the primary challenge is to group these fragments into bins, which correspond to inferred populations or clades within a sample. This allows functional roles to be directly assigned to constituent populations and further allows interactions within a microbial community to be analysed.
The project will explore a broad range of applications, but will focus primarily on uncultured microbial and viral communities associated with human physiology and health.
Leader: Saman Halgamuge
Staff: Isaam Saeed, Suhinthan Maheswararajah
Collaborators: Sen-Lin Tang (Academia Sinica Taiwan), Bill Chang (Your Gene Bioinformatics), Arthur Hsu (Walter Eliza Hall Institute), Rudolf Kruese (University of Magdeburg)
Sponsors: Australian Research Council
Biomedical Engineering,Mechanical Engineering
Convergence of engineering and IT with the life sciences
bioinformatics; data mining; genomics; knowledge discovery; Metagenomics