GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes.
GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes.
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DOI：
10.1093/nar/gky174
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发表时间：
2018-06-01
影响因子：
14.9
通讯作者：
Tyson GW
中科院分区：
文献类型：
--
作者：
Boyd JA;Woodcroft BJ;Tyson GW
大规模的宏基因组数据集能够从环境样本中恢复数百个种群基因组。然而，这些基因组通常不能代表复杂微生物群落的全部多样性。以基因为中心的方法可以通过独立检查每个读段来获得多样性的全面视图，但传统的成对比较方法通常会过度分类分类，并且随着宏基因组和数据库大小的增加而扩展性较差。在这里，我们介绍GraftM，一个工具，使用基因特异性软件包，以快速识别基因家族的宏基因组数据，使用隐马尔可夫模型（HISTORY）或DIAMOND数据库，并将这些序列使用放置到预先构建的基因树进行分类。GraftM的速度和准确性使用分类学标记以计算机模拟和体外模拟社区为基准，发现在家族水平上具有更高的准确性，处理时间比目前可用的软件快2.0-3.7倍。使用16 S rRNA和甲基辅酶M还原酶（McrA）特异性gpkgs的湿地宏基因组探索揭示了在深度梯度上的分类和功能转变。使用McrA gpkg分析NCBI nr数据库允许检测属于门级谱系的新序列。越来越多的gpkgs集合可以在线获得（https：//github.com/geronimp/graftM_gpkgs），在那里可以上传和交换策划的包。
Large-scale metagenomic datasets enable the recovery of hundreds of population genomes from environmental samples. However, these genomes do not typically represent the full diversity of complex microbial communities. Gene-centric approaches can be used to gain a comprehensive view of diversity by examining each read independently, but traditional pairwise comparison approaches typically over-classify taxonomy and scale poorly with increasing metagenome and database sizes. Here we introduce GraftM, a tool that uses gene specific packages to rapidly identify gene families in metagenomic data using hidden Markov models (HMMs) or DIAMOND databases, and classifies these sequences using placement into pre-constructed gene trees. The speed and accuracy of GraftM was benchmarked with in silico and in vitro mock communities using taxonomic markers, and was found to have higher accuracy at the family level with a processing time 2.0–3.7× faster than currently available software. Exploration of a wetland metagenome using 16S rRNA- and methyl-coenzyme M reductase (McrA)-specific gpkgs revealed taxonomic and functional shifts across a depth gradient. Analysis of the NCBI nr database using the McrA gpkg allowed the detection of novel sequences belonging to phylum-level lineages. A growing collection of gpkgs is available online (https://github.com/geronimp/graftM_gpkgs), where curated packages can be uploaded and exchanged.
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影响因子：
14.9
作者：
Ludwig, W;Strunk, O;Schleifer, KH
通讯作者：
Schleifer, KH
影响因子：
14.9
作者：
Haft DH;DiCuccio M;Badretdin A;Brover V;Chetvernin V;O'Neill K;Li W;Chitsaz F;Derbyshire MK;Gonzales NR;Gwadz M;Lu F;Marchler GH;Song JS;Thanki N;Yamashita RA;Zheng C;Thibaud-Nissen F;Geer LY;Marchler-Bauer A;Pruitt KD
通讯作者：
Pruitt KD
影响因子：
3.7
作者：
Matsen FA 4th;Evans SN
通讯作者：
Evans SN
影响因子：
4.2
作者：
de Vries, Maria;Schoeler, Anne;Schloter, Michael
通讯作者：
Schloter, Michael
影响因子：
5.6
作者：
KROGH, A;BROWN, M;HAUSSLER, D
通讯作者：
HAUSSLER, D