Classification of metagenomics data at lower taxonomic level using a robust supervised classifier.
Classification of metagenomics data at lower taxonomic level using a robust supervised classifier.
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DOI：
10.4137/ebo.s20523
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Wang K
中科院分区：
文献类型：
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作者：
Hou T;Liu F;Liu Y;Zou QY;Zhang X;Wang K
随着越来越多的完整测序的基因组变得可用，宏基因组数据的分类学分类将极大地受益于监督分类器，其可以响应于新的基因组而即时更新。目前，一些有监督的分类器已经被开发出来评估宏基因组序列的生物体。我们发现，现有的监督分类器通常不能区分来自不同类别的训练数据时，数据包含一些离群值准确。然而，训练基因组数据（细菌和古细菌基因组）通常包含一部分离群值，这些离群值来自测序错误、噬菌体入侵和一些高表达基因等。离群值被视为噪声，阻碍了具有更好预测精度的分类器的发展。为了解决这个问题，我们提出了一种鲁棒的监督分类器-加权支持向量域描述（WSVDD），它可以消除训练基因组数据的一些离群值的干扰，然后为每个分类类别生成更准确的数据域描述。实验结果表明，WSVDD是更强大的比其他分类器的模拟桑格和454读取不同的离群率。此外，在模拟宏基因组和真实的肠道宏基因组上进行的实验中，WSVDD也取得了比其他分类器更好的预测精度。
As more and more completely sequenced genomes become available, the taxonomic classification of metagenomic data will benefit greatly from supervised classifiers that can be updated instantaneously in response to new genomes. Currently, some supervised classifiers have been developed to assess the organism of metagenomic sequences. We have found that the existing supervised classifiers usually cannot discriminate the training data from different classes accurately when the data contain some outliers. However, the training genomic data (bacterial and archaeal genomes) usually contain a portion of outliers, which come from sequencing errors, phage invasions, and some highly expressed genes, etc. The outliers, treated as noises, prohibit the development of classifiers with better prediction accuracy. To solve the problem, we present a robust supervised classifier, weighted support vector domain description (WSVDD), which can eliminate the interference from some outliers for training genomic data and then generate more accurate data domain descriptions for each taxonomic class. The experimental results demonstrate WSVDD is more robust than other classifiers for simulated Sanger and 454 reads with different outlier rates. In addition, in experiments performed on simulated metagenomes and real gut metagenomes, WSVDD also achieved better prediction accuracy than other classifiers.