Compareads: comparing huge metagenomic experiments.
Compareads: comparing huge metagenomic experiments.
复制标题
DOI：
10.1186/1471-2105-13-s19-s10
复制
发表时间：
2012
影响因子：
3
通讯作者：
Peterlongo P
中科院分区：
文献类型：
--
作者：
Maillet N;Lemaitre C;Chikhi R;Lavenier D;Peterlongo P
目前，宏基因组样本分析主要是通过与存储在数据库中的先验知识进行比较来实现的。虽然强大，但这些方法不允许利用未知和/或“不可培养”的物种，例如估计细菌的99%。这项工作介绍了Compareads，一种从头比较宏基因组方法，它返回由高重复测序仪生成的两个可能的宏基因组数据集之间相似的读段。这项工作的一个独创性在于它处理庞大数据集的能力。本文提出的第二个主要贡献是设计了一个基于Bloom过滤器的概率数据结构，能够以有限的内存占用和受控的错误率索引数百万次读取。我们表明，Compareads能够检索生物信息，同时能够扩展到巨大的数据集。它的时间和内存特性使得Compareads可以在读取集上使用，每个读取集由超过1亿个Illumina读取组成，在几个小时内消耗4 GB内存，因此可以在今天的个人计算机上使用。Compareads使用一种新的数据结构，是一种用于从头比较巨大宏基因组样本的实用解决方案。Compareads是在CeCILL许可下发布的，可以从http://alcovna.genouest.org/compareads/免费下载。
Nowadays, metagenomic sample analyses are mainly achieved by comparing them with a priori knowledge stored in data banks. While powerful, such approaches do not allow to exploit unknown and/or "unculturable" species, for instance estimated at 99% for Bacteria. This work introduces Compareads, a de novo comparative metagenomic approach that returns the reads that are similar between two possibly metagenomic datasets generated by High Throughput Sequencers. One originality of this work consists in its ability to deal with huge datasets. The second main contribution presented in this paper is the design of a probabilistic data structure based on Bloom filters enabling to index millions of reads with a limited memory footprint and a controlled error rate. We show that Compareads enables to retrieve biological information while being able to scale to huge datasets. Its time and memory features make Compareads usable on read sets each composed of more than 100 million Illumina reads in a few hours and consuming 4 GB of memory, and thus usable on today's personal computers. Using a new data structure, Compareads is a practical solution for comparing de novo huge metagenomic samples. Compareads is released under the CeCILL license and can be freely downloaded from http://alcovna.genouest.org/compareads/.