PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures.
PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures.
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DOI：
10.1093/nar/gkx1321
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发表时间：
2018-04-06
影响因子：
14.9
通讯作者：
Dziembowski A
中科院分区：
文献类型：
--
作者：
Krawczyk PS;Lipinski L;Dziembowski A
质体是移动的遗传元件，在微生物的环境适应中起着重要作用。尽管质粒通常在培养的微生物中进行分析，但仍需要允许分析环境样品中的质粒池（质粒体）的方法。为此，已经开发了几种分子生物学和生物信息学方法;然而，它们仅限于低多样性的环境，并且不能回收大质粒。在这里，我们提出了PlasFlow，一种基于基因组签名的新工具，采用神经网络方法识别环境样品中的细菌质粒序列。PlasFlow可以从组装的宏基因组中回收质粒序列，而无需事先了解样品的分类或功能组成，准确率高达96%。它还可以恢复环状和线性质粒的序列，并可以执行序列的初始分类。与目前可用的其他工具相比，PlasFlow在测试数据集上表现出更好的性能。对两个重金属污染微生物垫样品的分析表明，质粒可能构成其宏基因组的重要组成部分，并携带参与重金属稳态的基因，证明质粒在微生物适应环境条件中的关键作用。
Plasmids are mobile genetics elements that play an important role in the environmental adaptation of microorganisms. Although plasmids are usually analyzed in cultured microorganisms, there is a need for methods that allow for the analysis of pools of plasmids (plasmidomes) in environmental samples. To that end, several molecular biology and bioinformatics methods have been developed; however, they are limited to environments with low diversity and cannot recover large plasmids. Here, we present PlasFlow, a novel tool based on genomic signatures that employs a neural network approach for identification of bacterial plasmid sequences in environmental samples. PlasFlow can recover plasmid sequences from assembled metagenomes without any prior knowledge of the taxonomical or functional composition of samples with an accuracy up to 96%. It can also recover sequences of both circular and linear plasmids and can perform initial taxonomical classification of sequences. Compared to other currently available tools, PlasFlow demonstrated significantly better performance on test datasets. Analysis of two samples from heavy metal-contaminated microbial mats revealed that plasmids may constitute an important fraction of their metagenomes and carry genes involved in heavy-metal homeostasis, proving the pivotal role of plasmids in microorganism adaptation to environmental conditions.
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影响因子：
4.4
作者：
HILL, KE;WEIGHTMAN, AJ;FRY, JC
通讯作者：
FRY, JC
影响因子：
4.4
作者：
Jones BV;Sun F;Marchesi JR
通讯作者：
Marchesi JR
DOI：
10.4137/ebo.s20523
发表时间：
2015
期刊：
Evolutionary bioinformatics online
影响因子：
--
作者：
Hou T;Liu F;Liu Y;Zou QY;Zhang X;Wang K
通讯作者：
Wang K
影响因子：
4.4
作者：
Bohlin, Jon;Skjerve, Eystein;Ussery, David W.
通讯作者：
Ussery, David W.
影响因子：
4.4
作者：
Bohlin J;van Passel MW;Snipen L;Kristoffersen AB;Ussery D;Hardy SP
通讯作者：
Hardy SP