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Leveraging Clustering Techniques to Facilitate Metagenomic Analysis

机译：利用聚类技术促进元基因组分析

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Machine learning clustering algorithms provide excellent methods for conducting metagenomic analysis with efficiency. This study uses two machine learning algorithms, the self-organizing map and the K-means algorithms, to cluster data from an environmental sample collected from a hot springs habitat and to provide a visual analysis of that data. A data processing pipeline is described that uses the clustering algorithms to identify which reference genomes should be included for further analysis in determining possible organisms that are present in a metagenomic sample. The clustering revealed probable candidates for additional analysis, including a thermophilic, anaerobic bacterium, which is likely to be found in a hot springs environment and serves to validate the functionality of these tools. The machine learning techniques discussed here can serve as a launching point for elucidating protein sequences that could serve as possible reference comparisons to a specific metagenomic sample and lead to further study.
机译：机器学习聚类算法为高效进行宏基因组分析提供了极好的方法。这项研究使用两种机器学习算法（自组织图和K-means算法）对来自温泉栖息地的环境样本中的数据进行聚类，并对这些数据进行可视化分析。描述了一种数据处理管道，该数据处理管道使用聚类算法来确定应包含哪些参考基因组，以便在确定宏基因组样本中可能存在的生物时进行进一步分析。聚类揭示了可能进行进一步分析的候选物，其中包括嗜热，厌氧细菌，该细菌很可能在温泉环境中发现并用于验证这些工具的功能。这里讨论的机器学习技术可以用作阐明蛋白质序列的起点，可以作为对特定宏基因组学样本的可能参考比较，并导致进一步的研究。

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