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FunFun:基于ITS的真菌群落功能注释工具

FunFun: ITS-based functional annotator of fungal communities.

作者信息

Krivonos Danil V, Konanov Dmitry N, Ilina Elena N

机构信息

Research Institute for Systems Biology and Medicine (RISBM) Moscow Russia.

出版信息

Ecol Evol. 2023 Mar 8;13(3):e9874. doi: 10.1002/ece3.9874. eCollection 2023 Mar.

Abstract

The study of individual fungi and their communities is of great interest to modern biology because they might be both producers of useful compounds, such as antibiotics and organic acids, and pathogens of various diseases. And certain features associated with the functional capabilities of fungi are determined by differences in gene content. Information about gene content is most often taken from the results of functional annotation of the whole genome. However, in practice, whole genome sequencing of fungi is rarely performed. At the same time, usually sequence amplicons of the ITS region to identify fungal taxonomy. But in the case of amplicon sequencing there is no way to perform a functional annotation. Here, we present FunFun, the instrument that allows to evaluate the gene content of an individual fungus or mycobiome from ITS sequencing data. FunFun algorithm based on a modified -nearest neighbors algorithm. As input, the program can use ITS1, ITS2, or a full-size ITS cluster (ITS1-5.8S-ITS2). FunFun was realized as a pip-installed command line instrument and validated using a shuffle-split approach. The developed instrument can be very useful in the fungal community comparing and estimating functional capabilities of fungi under study. Also, the program can predict with high accuracy the most variable functions.

摘要

对单个真菌及其群落的研究是现代生物学非常感兴趣的领域,因为它们可能既是有用化合物(如抗生素和有机酸)的生产者,也是各种疾病的病原体。与真菌功能能力相关的某些特征由基因含量的差异决定。关于基因含量的信息大多取自全基因组功能注释的结果。然而,在实际操作中,很少对真菌进行全基因组测序。同时,通常会对ITS区域的序列扩增子进行测序以鉴定真菌分类。但在扩增子测序的情况下,无法进行功能注释。在此,我们展示了FunFun,这是一种能够根据ITS测序数据评估单个真菌或真菌群落基因含量的工具。FunFun算法基于改进的最近邻算法。该程序可以将ITS1、ITS2或完整大小的ITS簇(ITS1 - 5.8S - ITS2)作为输入。FunFun被实现为一个可通过pip安装的命令行工具,并使用随机分割方法进行了验证。所开发的工具在比较真菌群落和评估所研究真菌的功能能力方面可能非常有用。此外,该程序能够高精度地预测最具变异性的功能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/55a2/9994472/855a38feb8f7/ECE3-13-e9874-g004.jpg

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