The microbes that live in an environment can be identified from the combined genomic material, also referred to as the metagenome. Sequencing a metagenome can result in large volumes of sequencing reads. A promising approach to reduce the size of metagenomic datasets is by clustering reads into groups based on their overlaps. Clustering reads is valuable to facilitate downstream analyses, including computationally intensive strain-aware assembly. As current read clustering approaches cannot handle the large datasets arising from high-throughput metagenome sequencing, a novel read clustering approach is needed. In this paper we propose OGRE, an Overlap Graph-based Read clustEring procedure for high-throughput sequencing data, with a focus on shotgun metagenomes.

中文翻译：

---

OGRE：基于重叠图的宏基因组读取clustEring。

可以从组合的基因组材料（也称为元基因组）中识别生活在环境中的微生物。对一个基因组进行测序可能会导致大量的测序读取。减少宏基因组数据集大小的一种有前途的方法是根据读取的重叠将其聚类成组。聚类读段对于促进下游分析（包括计算密集型应变感知装配）的分析很有价值。由于当前的读取聚类方法无法处理由高通量元基因组测序产生的大型数据集，因此需要一种新颖的读取聚类方法。在本文中，我们提出了OGRE，一种用于高通量测序数据的基于重叠图的Read clustEring程序，重点是with弹枪的基因组。