使用多组k-mer计数的多重比较宏基因组学
Multiple Comparative Metagenomics using Multiset k-mer Counting
摘要：
背景大规模的宏基因组项目旨在提取不同环境条件下的生物多样性知识。目前用于比较微生物群落的方法面临着重要的局限性。那些基于分类学或功能分配的方法依赖于可以与已知生物相关联的序列的一小部分。另一方面，比较整个序列集的从头方法要么不能在雄心勃勃的宏基因组项目上扩大规模，要么不能提供精确和详尽的结果。方法.这些限制促使开发了一种新的从头宏基因组比较方法，称为Simka。该方法通过将物种计数替换为k-mer计数来计算大量标准生态距离。Simka通过在多个数据集上采用新的并行k-mer计数策略，扩大了当今的宏基因组项目。结果在公共人类微生物组计划数据集上的实验表明，Simka捕获了基本的生物结构。Simka能够在几个小时内计算出数百个宏基因组样本（690个样本，320亿次读取）的定性和定量生态距离。我们还表明，在k-mer水平上分析宏基因组与非常精确的从头比较技术高度相关，该技术依赖于所有对所有序列比对策略或基于分类学分析。
英文摘要：
Background. Large scale metagenomic projects aim to extract biodiversity knowledge between different environmental conditions. Current methods for comparing microbial communities face important limitations. Those based on taxonomical or functional assignation rely on a small subset of the sequences that can be associated to known organisms. On the other hand, de novo methods, that compare the whole sets of sequences, either do not scale up on ambitious metagenomic projects or do not provide precise and exhaustive results. Methods. These limitations motivated the development of a new de novo metagenomic comparative method, called Simka. This method computes a large collection of standard ecological distances by replacing species counts by k-mer counts. Simka scales-up today's metagenomic projects thanks to a new parallel k-mer counting strategy on multiple datasets. Results. Experiments on public Human Microbiome Project datasets demonstrate that Simka captures the essential underlying biological structure. Simka was able to compute in a few hours both qualitative and quantitative ecological distances on hundreds of metagenomic samples (690 samples, 32 billions of reads). We also demonstrate that analyzing metagenomes at the k-mer level is highly correlated with extremely precise de novo comparison techniques which rely on all-versus-all sequences alignment strategy or which are based on taxonomic profiling.
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