背景:宏基因组读数的结合是一个活跃的研究领域,许多基于无监督机器学习的技术已被用于宏基因组读取的分类学独立分级。目的:找到簇的最佳数量以及开发有效的管道来破译微生物基因组的复杂性非常重要。方法:应用无监督聚类技术进行分箱需要事先找到最佳的聚类数量,并且被认为是一项艰巨的任务。本文描述了一种新颖的方法,MetaConClust,使用覆盖信息对重叠群进行分组,并使用基于共识的聚类方法自动找到宏基因组学数据分箱的最佳聚类数量。已观察到宏基因组学样品中重叠群的覆盖率与样品中物种的丰度成正比,并由MetaConClust用于第一阶段的数据分组。在第二阶段中,使用围绕Medoid(PAM)方法进行聚类,以生成具有通过基于共识的方法自动确定的初始聚类数量的bin。结果:最后,使用轮廓指数测试获得的垃圾箱的质量,兰特指数,召回,精度,和准确性。MetaConClust的性能与使用基准低复杂度模拟和真实宏基因组数据集的最新方法和工具进行比较,对于无监督和混合方法具有可比性。结论:这表明基于共识的聚类方法是一种有前途的方法,可以自动找到宏基因组学数据的bin数量。
Background: Binning of metagenomic reads is an active area of research, and many unsupervised machine learning-based techniques have been used for taxonomic independent binning of metagenomic reads. Objective: It is important to find the optimum number of the cluster as well as develop an efficient pipeline for deciphering the complexity of the microbial genome. Methods: Applying unsupervised clustering techniques for binning requires finding the optimal number of clusters beforehand and is observed to be a difficult task. This paper describes a novel method, MetaConClust, using coverage information for grouping of contigs and automatically finding the optimal number of clusters for binning of metagenomics data using a
consensus-based clustering approach. The coverage of contigs in a metagenomics sample has been observed to be directly proportional to the abundance of species in the sample and is used for grouping of data in the first phase by MetaConClust. The Partitioning Around Medoid (PAM) method is used for clustering in the second phase for generating bins with the initial number of clusters determined automatically through a
consensus-based method. Results: Finally, the quality of the obtained bins is tested using silhouette index, rand Index, recall, precision, and accuracy. Performance of MetaConClust is compared with recent methods and tools using benchmarked low complexity simulated and real metagenomic datasets and is found better for unsupervised and comparable for hybrid methods. Conclusion: This is suggestive of the proposition that the
consensus-based clustering approach is a promising method for automatically finding the number of bins for metagenomics data.