Development of computational strategies for identification and characterisation of viruses in metagenomic samples

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The analysis of data from next generation sequencing of metagenomic samples has emerged as an important tool in recent years. In the past, much of this analysis has involved targeted 16S ribosomal sequencing followed by taxonomic classification. However, the increase in throughput and reduction in cost of NGS, combined with the lack of resolution provided by 16S approaches, has encouraged the adoption of whole genome shotgun approaches. While read mapping is still a useful tool for analysing this data, greater insights are possible from assembly of reads. However, metagenomic assembly is a very immature field with only a handful of assemblers having emerged. One of these is our own MetaCortex, a proof-of-concept assembly tool that has shown promising results when applied to the analysis of the virome of a species of bats from West Africa. The purpose of this project is to develop the algorithms necessary to turn the proof-of-concept into an efficient and sensitive assembly tool that will benefit the metagenomics community. Though we feel the tool should have applicability to a wide range of metagenomic datasets, we are targeting the particular problem of viral detection, as this is an important and under-explored area of metagenomic analysis that has important implications for animal and human health.

In order to validate the effectiveness of the assembly algorithms, we plan to test on simulated datasets and, crucially, on new metagenomic sequence data generated for this project. This will include samples from humans, cows and insects that carry known viruses, or have been artificially infected with known viruses. Additionally, we have access to a set of rodent samples collected in Africa that are expected to contain many zoonotic viruses. These will be used as a case study to demonstrate the effectiveness of the tool in real world experiments.

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