COMPUTATIONAL METHODS FOR MICROBIAL NEXT GENERATION RE-SEQUENCING DATA

绝大多数已经存在或存在的生命是肉眼不可见的（统称为微生物或微生物），包括病毒在内，形成了庞大而复杂的群落。表征这些群落中存在的物种、基因组组成和遗传变异一直是“宏基因组学”的主要焦点，宏基因组学是对来自环境或来自动物或人类的混合样品（例如，来自动物肠道或土壤微生物生态系统）的基因组研究。当代测序技术（下一代测序，NGS）已经大规模并行化了遗传物质内核苷酸顺序的测定，从而使我们能够快速测序不同的微生物。这为以前所未有的规模探索微生物群落和遗传多样性带来了潜力。计算方法在NGS数据的分析、比对和汇编中发挥着核心作用。然而，所产生的数据量超出了我们例行分析的能力，更不用说进行适当的比较分析了。这种软件的缺乏是因为大多数研究工作都是针对从下一代序列数据组装单个完整基因组。然而，对于微生物来说，许多有趣的问题涉及到“超深度”测序揭示的群落和种群变异中存在的序列多样性。新兴的方法旨在以类似于拼图游戏的方式构建NGS读段的从头组装（每个读段是对应于基因组区域的单独序列片段），其中通过将所有匹配的片段连接在一起来构建图片。在从头组装中，通过将匹配的短读段分配在一起来构建基因组序列。大多数现有的从头组装NGS数据的方法广泛使用的de Bruijn图方法。然而，为非常大的NGS数据集构建de Bruijn图是非常苛刻的，因为它们需要大量的计算资源。在这个项目中，我们建议开发新的计算方法，基于通过将单个NGS读数重新转换为数字序列（并直接使用这种转换/压缩的数据）来压缩它们，这将对所有类型的微生物数据集通用。为了做到这一点，我们将探索新的方法，以图形方式表示短读序列数据，并适用于有效的数据挖掘建立数学方法。我们将解决的具体问题是NGS数据集的组装，其中需要在分析中考虑样本的变化。在宏基因组学数据中，读数之间的差异既对应于不同的微生物物种，也对应于单个物种或病毒种群内的差异。一个特别重要的焦点是在没有用于比较的参考序列的情况下组装基因组（从头组装）的能力，因为对于许多微生物来说，适当的参考基因组通常是不可用的，并且即使当参考可用时，基因组结构也可以在物种内变化。

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.