WHAM!: a web-based visualization suite for user-defined analysis of metagenomic shotgun sequencing data

被引:10
作者
Devlin, Joseph C. [1 ]
Battaglia, Thomas [2 ,3 ]
Blaser, Martin J. [2 ,3 ]
Ruggles, Kelly V. [1 ,2 ,4 ]
机构
[1] New York Sch Med, Sackler Inst Grad Biomed Sci, New York, NY USA
[2] New York Sch Med, Dept Med, Div Translat Med, New York, NY USA
[3] New York Sch Med, Dept Microbiol, New York, NY USA
[4] New York Sch Med, Appl Bioinformat Labs, New York, NY USA
来源
BMC GENOMICS | 2018年 / 19卷
基金
美国国家卫生研究院;
关键词
Microbiome; Metatranscriptomic; Data exploration; RShiny; DNA analysis; Expression analysis; DISCOVERY; TOOL;
D O I
10.1186/s12864-018-4870-z
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Background: Exploration of large data sets, such as shotgun metagenomic sequence or expression data, by biomedical experts and medical professionals remains as a major bottleneck in the scientific discovery process. Although tools for this purpose exist for 16S ribosomal RNA sequencing analysis, there is a growing but still insufficient number of user-friendly interactive visualization workflows for easy data exploration and figure generation. The development of such platforms for this purpose is necessary to accelerate and streamline microbiome laboratory research. Results: We developed the Workflow Hub for Automated Metagenomic Exploration (WHAM!) as a web-based interactive tool capable of user-directed data visualization and statistical analysis of annotated shotgun metagenomic and metatranscriptomic data sets. WHAM! includes exploratory and hypothesis-based gene and taxa search modules for visualizing differences in microbial taxa and gene family expression across experimental groups, and for creating publication quality figures without the need for command line interface or in-house bioinformatics. Conclusions: WHAM! is an interactive and customizable tool for downstream metagenomic and metatranscriptomic analysis providing a user-friendly interface allowing for easy data exploration by microbiome and ecological experts to facilitate discovery in multi-dimensional and large-scale data sets.
引用
收藏
页数:11
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