COMPUTATIONAL METHODS FOR MICROBIAL NEXT GENERATION RE-SEQUENCING DATA

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The overwhelming majority of life that has existed or exists is invisible to the naked eye (collectively termed the microbes, or microorganisms) and, including the viruses, forms large and complex communities. Characterising the species present, genome composition and genetic variation in these communities has been a major focus of 'metagenomics', the genomic study of mixed samples from the environment, or from animals or humans, for example, from an animal's gut or a soil microbial ecosystems. Contemporary sequencing technologies (next generation sequencing, NGS) have massively parallelized the determination of nucleotide order within genetic material resulting in our ability to rapidly sequence different microbes. This introduces the potential to explore microbial communities and genetic diversity on a scale that was previously unprecedented. Computational methods play a central role in the analysis, alignment and assembly of NGS data. However, the amount of data being generated is outstripping our ability to analyse them routinely, let alone carry out appropriate comparative analysis. This lack of software arises because most research effort is being directed at assembling single complete genomes from next generation sequence data. However, with microbes many interesting questions concern the diversity of sequences present in a community and population variation, revealed by 'ultra-deep' sequencing. Emerging approaches aim to build a de novo assembly of the NGS reads (each read is an individual sequence fragment corresponding to a region of a genome) in a similar fashion to a jigsaw puzzle where a picture is constructed by joining all the matching pieces together. In de novo assembly the genome sequence is constructed by allocating matching short reads together. The majority of the existing de novo assembly approaches for NGS data make extensive use of the de Bruijn graph method. However, building de Bruijn graphs for very large NGS data sets is very demanding because they require hefty computational resources. In this project we propose to develop novel computational methods, based on compressing the individual NGS reads by recasting them as numerical sequences (and working with this transformed/compressed data directly) that will be generically useful for all types of microbial data sets. In order to do this we will explore novel methods for representing short-read sequence data graphically and apply established mathematical approaches for efficient data mining. The particular problem we will address is the assembly of NGS data sets where the variation in the sample needs to be considered in the analysis. In metagenomics data variation between reads corresponds to both distinct microbial species and variation within individual species or viral populations. A particularly important focus is the ability to assembly a genome without a reference sequence for comparison (de novo assembly) as an appropriate reference genome is frequently not available for many microbes and, even when a reference is available, genome architecture can vary within a species.