摩奇（Mochi）：一款基于扩增子的微生物群落分析的综合性跨平台工具。

MOCHI: a comprehensive cross-platform tool for amplicon-based microbiota analysis.

机构信息

Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 30068, Taiwan.

Department of Microbiology and Immunology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan.

出版信息

Bioinformatics. 2022 Sep 15;38(18):4286-4292. doi: 10.1093/bioinformatics/btac494.

DOI:10.1093/bioinformatics/btac494

PMID:35876544

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9477538/

Abstract

MOTIVATION

Microbiota analyses have important implications for health and science. These analyses make use of 16S/18S rRNA gene sequencing to identify taxa and predict species diversity. However, most available tools for analyzing microbiota data require adept programming skills and in-depth statistical knowledge for proper implementation. While long-read amplicon sequencing can lead to more accurate taxa predictions and is quickly becoming more common, practitioners have no easily accessible tools with which to perform their analyses.

RESULTS

We present MOCHI, a GUI tool for microbiota amplicon sequencing analysis. MOCHI preprocesses sequences, assigns taxonomy, identifies different abundant species and predicts species diversity and function. It takes either taxonomic count table or FASTQ of partial 16S/18S rRNA or full-length 16S rRNA gene as input. It performs analyses in real time and visualizes data in both tabular and graphical formats.

AVAILABILITY AND IMPLEMENTATION

MOCHI can be installed to run locally or accessed as a web tool at https://mochi.life.nctu.edu.tw.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

微生物组分析对健康和科学具有重要意义。这些分析利用 16S/18S rRNA 基因测序来识别分类群并预测物种多样性。然而，大多数用于分析微生物组数据的可用工具都需要熟练的编程技能和深入的统计知识才能正确实施。虽然长读长扩增子测序可以导致更准确的分类群预测，并且正在迅速变得更加普遍，但从业者没有易于访问的工具来进行他们的分析。

结果

我们提出了 MOCHI，这是一个用于微生物组扩增子测序分析的 GUI 工具。MOCHI 预处理序列，分配分类群，识别不同的丰富物种，并预测物种多样性和功能。它可以接受分类计数表或部分 16S/18S rRNA 或全长 16S rRNA 基因的 FASTQ 作为输入。它实时进行分析，并以表格和图形格式可视化数据。