CompostBin：一种基于DNA组成的环境鸟枪读数分箱算法
CompostBin: A DNA composition-based algorithm for binning environmental shotgun reads
摘要：
微生物多样性研究的一个主要障碍是，绝大多数微生物不能在实验室中培养，因此不适合传统的表征方法。环境鸟枪测序（ESS）通过对微生物群落中存在的生物体的DNA进行测序来克服这一障碍。通过将每个序列读数与其来源生物体相关联，可以极大地促进对该宏基因组数据的解释。我们报告了CompostBin的发展，这是一种基于DNA组成的算法，用于分析宏基因组序列读数并将其分配到特定分类单元中。与先前试图分箱组装的重叠群并且通常需要在已知参考基因组上进行训练的方法不同，CompostBin具有准确分箱原始序列读数而无需组装或训练的能力。它应用主成分分析将数据投影到一个信息丰富的低维空间中，然后在这个过滤后的数据集上使用归一化切割聚类算法将序列分类到特定分类单元中。我们证明了算法的准确性，在各种模拟数据集和一个宏基因组数据集与已知的物种分配。CompostBin是一项正在进行的工作，计划在未来对算法进行几次改进。
英文摘要：
A major hindrance to studies of microbial diversity has been that the vast majority of microbes cannot be cultured in the laboratory and thus are not amenable to traditional methods of characterization. Environmental shotgun sequencing (ESS) overcomes this hurdle by sequencing the DNA from the organisms present in a microbial community. The interpretation of this metagenomic data can be greatly facilitated by associating every sequence read with its source organism. We report the development of CompostBin, a DNA composition-based algorithm for analyzing metagenomic sequence reads and distributing them into taxon-specific bins. Unlike previous methods that seek to bin assembled contigs and often require training on known reference genomes, CompostBin has the ability to accurately bin raw sequence reads without need for assembly or training. It applies principal component analysis to project the data into an informative lower-dimensional space, and then uses the normalized cut clustering algorithm on this filtered data set to classify sequences into taxon-specific bins. We demonstrate the algorithm's accuracy on a variety of simulated data sets and on one metagenomic data set with known species assignments. CompostBin is a work in progress, with several refinements of the algorithm planned for the future.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
