Dataset Information

ViromeFlowX: a Comprehensive Nextflow-based Automated Workflow for Mining Viral Genomes from Metagenomic Sequencing Data.

ABSTRACT: Understanding the link between the human gut virome and diseases has garnered significant interest in the research community. Extracting virus-related information from metagenomic sequencing data is crucial for unravelling virus composition, host interactions, and disease associations. However, current metagenomic analysis workflows for viral genomes vary in effectiveness, posing challenges for researchers seeking the most up-to-date tools. To address this, we present ViromeFlowX, a user-friendly Nextflow workflow that automates viral genome assembly, identification, classification, and annotation. This streamlined workflow integrates cutting-edge tools for processing raw sequencing data for taxonomic annotation and functional analysis. Application to a dataset of 200 metagenomic samples yielded high-quality viral genomes. ViromeFlowX enables efficient mining of viral genomic data, offering a valuable resource to investigate the gut virome's role in virus-host interactions and virus-related diseases.

SUBMITTER: Wang X

PROVIDER: S-EPMC10926697 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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ViromeFlowX: a Comprehensive Nextflow-based Automated Workflow for Mining Viral Genomes from Metagenomic Sequencing Data.

Wang Xiaokai X Ding Zhimin Z Yang Ying Y Liang Lifeng L Sun Yingshuai Y Hou Chaojian C Zheng Yuning Y Xia Yan Y Dong Lixin L

Microbial genomics 20240201 2

Understanding the link between the human gut virome and diseases has garnered significant interest in the research community. Extracting virus-related information from metagenomic sequencing data is crucial for unravelling virus composition, host interactions, and disease associations. However, current metagenomic analysis workflows for viral genomes vary in effectiveness, posing challenges for researchers seeking the most up-to-date tools. To address this, we present ViromeFlowX, a user-friendl ...[more]

PMID: 38381034

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Project description:BACKGROUND:Viruses are important components of microbial communities modulating community structure and function; however, only a couple of tools are currently available for phage identification and analysis from metagenomic sequencing data. Here we employed the random forest algorithm to develop VirMiner, a web-based phage contig prediction tool especially sensitive for high-abundances phage contigs, trained and validated by paired metagenomic and phagenomic sequencing data from the human gut flora. RESULTS:VirMiner achieved 41.06% ± 17.51% sensitivity and 81.91% ± 4.04% specificity in the prediction of phage contigs. In particular, for the high-abundance phage contigs, VirMiner outperformed other tools (VirFinder and VirSorter) with much higher sensitivity (65.23% ± 16.94%) than VirFinder (34.63% ± 17.96%) and VirSorter (18.75% ± 15.23%) at almost the same specificity. Moreover, VirMiner provides the most comprehensive phage analysis pipeline which is comprised of metagenomic raw reads processing, functional annotation, phage contig identification, and phage-host relationship prediction (CRISPR-spacer recognition) and supports two-group comparison when the input (metagenomic sequence data) includes different conditions (e.g., case and control). Application of VirMiner to an independent cohort of human gut metagenomes obtained from individuals treated with antibiotics revealed that 122 KEGG orthology and 118 Pfam groups had significantly differential abundance in the pre-treatment samples compared to samples at the end of antibiotic administration, including clustered regularly interspaced short palindromic repeats (CRISPR), multidrug resistance, and protein transport. The VirMiner webserver is available at http://sbb.hku.hk/VirMiner/ . CONCLUSIONS:We developed a comprehensive tool for phage prediction and analysis for metagenomic samples. Compared to VirSorter and VirFinder-the most widely used tools-VirMiner is able to capture more high-abundance phage contigs which could play key roles in infecting bacteria and modulating microbial community dynamics. TRIAL REGISTRATION:The European Union Clinical Trials Register, EudraCT Number: 2013-003378-28 . Registered on 9 April 2014.

Dataset Information

ViromeFlowX: a Comprehensive Nextflow-based Automated Workflow for Mining Viral Genomes from Metagenomic Sequencing Data.

Publications

ViromeFlowX: a Comprehensive Nextflow-based Automated Workflow for Mining Viral Genomes from Metagenomic Sequencing Data.

Similar Datasets

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