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Metagenomic assembly and characterisation of viral signatures with MetaCortex

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If you have a question about this talk, please contact Mustapha Amrani.

Mathematical, Statistical and Computational Aspects of the New Science of Metagenomics

Co-authors: Ricardo Ramirez Gonzalez (The Genome Analysis Centre (TGAC)), Kate Baker (Wellcome Trust Sanger Institute), Pablo Murcia (MRC-University of Glasgow Centre for Virus Research), Mario Caccamo (The Genome Analysis Centre (TGAC))

Most standard tools for assembly of next generation sequencing reads are not designed for metagenomic datasets, rather the aim is to assemble a single genome into a relatively small number of large contigs or scaffolds. In part this is achieved by implementation of algorithms designed to remove sequencing errors and to smooth small variations that would otherwise break contiguity. However, this approach has limits when used to analyse environmental samples, in which fragments of DNA from multiple organisms may be present and where the aim is not to assemble a single genome, but to understand the wider biological composition of the sample.

The last 2 years has seen the emergence of a number of specialised metagenomic assemblers aimed at addressing this tools gap. One example of this is our own work, MetaCortex, which is capable of assembling sequencing reads and outputting contigs indicative of the entire set of organisms present in a sample. MetaCortex works by identifying sub-graphs within the wider de Bruijn graph assembly and building contigs, which represent the consensus path through these components. In doing so, variation information is preserved and it is possible to detect organisms present in the sample with low read coverage. We applied this method to the analysis of DNA samples from Eidolon helvum, a species of bat implicated in the transmission of zoonotic disease to humans and were able to identify hundreds of different viral DNA signatures within samples.

This talk is part of the Isaac Newton Institute Seminar Series series.

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