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派克:牛津纳米孔扩增子宏基因组学的操作分类单元(OTU)水平分析

Pike: OTU-Level Analysis for Oxford Nanopore Amplicon Metagenomics.

作者信息

Krivonos Danil V, Fedorov Dmitry E, Konanov Dmitry N, Vvedensky Andrey V, Sonets Ignat V, Korneenko Elena V, Speranskaya Anna S, Ilina Elena N

机构信息

Research Institute for Systems Biology and Medicine (RISBM), 18, Nauchniy Proezd, 117246 Moscow, Russia.

Department of Molecular and Translational Medicine, Moscow Institute of Physics and Technology, State University, 141700 Dolgoprudny, Russia.

出版信息

Int J Mol Sci. 2025 Apr 28;26(9):4168. doi: 10.3390/ijms26094168.

Abstract

The Oxford Nanopore platform and nanopore sequencing are gaining increasing popularity in modern metagenomic research. However, there is a limited set of dedicated tools for analyzing this type of data. The tools used for nanopore amplicon sequencing data analysis often provide only taxonomy annotation without OTU sequence assembly. Conversely, tools that facilitate OTU assembly are constrained in their analysis to long reads, such as the V1-V9 regions of 16S rRNA for bacterial community studies or the full-length ITS cluster (ITS1-5.8S-ITS2) for fungal community studies. In other cases, researchers propose their own solutions without dedicated tools. In this paper, we present Pike, a novel tool for analyzing Oxford Nanopore amplicon sequencing data. Pike allows analysis without amplicon size limitations and allows de novo assembly of OTU sequences. In our research, we created mock communities of fungi and bacteria, which we then used to demonstrate the efficiency of our algorithm. Furthermore, we validated the algorithm using externally available data. We also compared our approach with similar ones to show its applicability.

摘要

牛津纳米孔平台和纳米孔测序在现代宏基因组学研究中越来越受欢迎。然而,用于分析这类数据的专用工具却很有限。用于纳米孔扩增子测序数据分析的工具通常只提供分类注释,而不进行OTU序列组装。相反,便于进行OTU组装的工具在分析时仅限于长读长,例如用于细菌群落研究的16S rRNA的V1 - V9区域,或用于真菌群落研究的全长ITS簇(ITS1 - 5.8S - ITS2)。在其他情况下,研究人员在没有专用工具的情况下提出了自己的解决方案。在本文中,我们介绍了Pike,这是一种用于分析牛津纳米孔扩增子测序数据的新型工具。Pike允许在没有扩增子大小限制的情况下进行分析,并允许对OTU序列进行从头组装。在我们的研究中,我们创建了真菌和细菌的模拟群落,然后用它们来证明我们算法的效率。此外,我们使用外部可用数据对该算法进行了验证。我们还将我们的方法与类似方法进行了比较,以展示其适用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0939/12071631/069ad67c097a/ijms-26-04168-g001.jpg

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