Phymm and PhymmBL: metagenomic phylogenetic classification with interpolated Markov models.
Phymm and PhymmBL: metagenomic phylogenetic classification with interpolated Markov models.
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DOI：
10.1038/nmeth.1358
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发表时间：
2009-09
期刊：
影响因子：
48
通讯作者：
Salzberg, Steven L.
中科院分区：
文献类型：
--
作者：
Brady, Arthur;Salzberg, Steven L.
元基因组学项目从未描述的环境中收集DNA，每个样本可能包含数千种物种。元基因组分析面临的一个主要挑战是将原始序列读入的系统发育分类成代表相同或相似分类群的组，这是组装基因组和分析样本生物多样性的先决条件。新的测序技术通过比以前的技术产生更短的读数，使测序变得更快、更困难，从而使元基因组学变得更容易。到目前为止，从短至100个碱基对的读数中对序列进行分类相对来说并不准确，这需要研究人员使用更老的、长时间阅读的技术。我们提出了PHYMM，一个用于元基因组数据的分类器，它已经在539个完整的精选基因组上进行了训练，可以准确地对短到100个碱基对的阅读进行分类，这比以前基于成分的分类方法有了很大的飞跃。我们还描述了如何将PHYMM与序列比对算法相结合，进一步提高准确性。
Metagenomics projects collect DNA from uncharacterized environments that may contain thousands of species per sample. One main challenge facing metagenomic analysis is phylogenetic classification of raw sequence reads into groups representing the same or similar taxa, a prerequisite for genome assembly and for analyzing the biological diversity of a sample. New sequencing technologies have made metagenomics easier, by making sequencing faster, and more difficult, by producing shorter reads than previous technologies. Classifying sequences from reads as short as 100 base pairs has until now been relatively inaccurate, requiring researchers to use older, long-read technologies. We present Phymm, a classifier for metagenomic data, that has been trained on 539 complete, curated genomes and can accurately classify reads as short as 100 bp, representing a substantial leap forward over previous composition-based classification methods. We also describe how combining Phymm with sequence alignment algorithms, further improves accuracy.
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