DeepNOG: fast and accurate protein orthologous group assignment.
DeepNOG: fast and accurate protein orthologous group assignment.
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DOI：
10.1093/bioinformatics/btaa1051
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发表时间：
2021-04-01
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影响因子：
--
通讯作者：
Rattei T
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文献类型：
--
作者：
Feldbauer R;Gosch L;Lüftinger L;Hyden P;Flexer A;Rattei T
蛋白质同源类群数据库是进化分析、功能注释或跨谱系代谢途径建模的强大工具。序列通常被分配给基于比对的方法，例如轮廓隐马尔可夫模型，这已经成为计算瓶颈。提出了一种基于深卷积网络的快速、准确、无对齐的正畸分配方法DeepNOG。我们在两个正畸数据库(COG，eggnog 5)上将DeepNOG与最先进的基于比对的方法(HMMER，Diamond)和非比对方法(DeepFam)进行了比较。DeepNOG可以扩展到像eggnog这样的大型正字法数据库，在准确率和召回率方面远远超过DeepFam。虽然基于比对的方法在所研究的方法中仍然提供最准确的分配，但DeepNOG的计算时间在CPU上要低一个数量级。可选的GPU使用进一步大幅提高了吞吐量。命令行工具使用户能够快速采用。源代码和包可在https://github.com/univieCUBE/deepnog.上免费获得使用$pip Install Deepnog安装独立于平台的Python程序。补充数据可在生物信息学在线上获得。
Protein orthologous group databases are powerful tools for evolutionary analysis, functional annotation or metabolic pathway modeling across lineages. Sequences are typically assigned to orthologous groups with alignment-based methods, such as profile hidden Markov models, which have become a computational bottleneck. We present DeepNOG, an extremely fast and accurate, alignment-free orthology assignment method based on deep convolutional networks. We compare DeepNOG against state-of-the-art alignment-based (HMMER, DIAMOND) and alignment-free methods (DeepFam) on two orthology databases (COG, eggNOG 5). DeepNOG can be scaled to large orthology databases like eggNOG, for which it outperforms DeepFam in terms of precision and recall by large margins. While alignment-based methods still provide the most accurate assignments among the investigated methods, computing time of DeepNOG is an order of magnitude lower on CPUs. Optional GPU usage further increases throughput massively. A command-line tool enables rapid adoption by users. Source code and packages are freely available at https://github.com/univieCUBE/deepnog. Install the platform-independent Python program with $pip install deepnog. Supplementary data are available at Bioinformatics online.
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