Using Metagenomics to Connect Microbial Community Biodiversity and Functions

Mendes, Lucas William; Braga, Lucas Palma Perez; Navarrete, Acácio Aparecido; Goss‐Souza, Dennis; Silva, Genivaldo Gueiros Zacarias; Tsai, Siu Mui

doi:10.21775/9781910190593.06

Cited by 3 publications

(2 citation statements)

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“…The study of microbial communities through whole metagenomic (WMG) shotgun sequencing opens new avenues for the investigation of the metabolic potentials of microbiomes, in addition to their taxonomic composition [1][2][3]. This greatly improves the ability to interpret and predict functional interactions, antibiotic resistance, and population dynamics of microbiomes, with applications in human health, waste treatment, agriculture, and environmental stewardship [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

MetaWRAP - a flexible pipeline for genome-resolved metagenomic data analysis

Uritskiy¹,

DiRuggiero²,

Taylor³

2018

Preprint

149

205

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Background:The study of microbiomes using whole-metagenome shotgun sequencing enables the analysis of uncultivated microbial populations that may have important roles in their environments. Extracting individual draft genomes (bins) facilitates metagenomic analysis at the single genome level. Software and pipelines for such analysis have become diverse and sophisticated, resulting in a significant burden for biologists to access and use them. Furthermore, while bin extraction algorithms are rapidly improving, there is still a lack of tools for their evaluation and visualization. Results:To address these challenges, we present metaWRAP, a modular pipeline software for shotgun metagenomic data analysis. MetaWRAP deploys state-of-the-art software to handle metagenomic data processing starting from raw sequencing reads and ending in metagenomic bins and their analysis. MetaWRAP is flexible enough to give investigators control over the analysis, while still being easy-to-install and easy-to-use. It includes hybrid algorithms that leverage the strengths of a variety of software to extract and refine high-quality bins from metagenomic data through bin consolidation and reassembly. MetaWRAP's hybrid bin extraction algorithm outperforms individual binning approaches and other bin consolidation programs in both synthetic and real datasets. Finally, metaWRAP comes with numerous modules for the analysis of metagenomic bins, including taxonomy assignment, abundance estimation, functional annotation, and visualization. Conclusions:MetaWRAP is an easy-to-use modular pipeline that automates the core tasks in metagenomic analysis, while contributing significant improvements to the extraction and interpretation of high-quality metagenomic bins. The bin refinement and reassembly modules of metaWRAP consistently outperform other binning approaches. Each module of metaWRAP is also a standalone component, making it a flexible and versatile tool for tackling metagenomic shotgun sequencing data. MetaWRAP is open-source software available at https://github.com/ bxlab/metaWRAP.

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Section: Introductionmentioning

confidence: 99%

MetaWRAP - a flexible pipeline for genome-resolved metagenomic data analysis

Uritskiy¹,

DiRuggiero²,

Taylor³

2018

Preprint

149

205

View full text Add to dashboard Cite

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“…Advances in molecular biology techniques such as next generation sequencing (NGS), PCR, molecular cloning, DNA Microarrays and protein mass spectrometry have improved our knowledge of microbiomes (Jovel, Patterson et al 2016, Mendes, Braga et al 2017, Quince, Walker et al 2017, Heyer, Schallert et al 2019, Sun, Liao et al 2020). Among them, NGS is becoming one the most popular techniques used to generate targeted amplicon sequencing, shotgun metagenomics, and meta-transcriptomics data to study microbial communities.…”

Section: Introductionmentioning

confidence: 99%

CuBi-MeAn Customized Pipeline for Metagenomic Data Analysis

Keshani-Langroodi

Sales

2021

Preprint

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Whole genome shotgun sequencing is a powerful to study microbial community is a given environment. Metagenomic binning offers a genome centric approach to study microbiomes. There are several tools available to process metagenomic data from raw reads to the interpretation there is still lack of standard approach that can be used to process the metagenomic data step by step. In this study CuBi-MeAn (Customizable Binning and Metagenomic Analysis) create a customizable and flexible processing pipeline, to process the metagenomic data and generate results for further interpretation. This study aims to perform metagenomic binning to enhance taxonomical classification, functional potentials, and interactions among microbial populations in environmental systems. This customized pipeline which is comprised of a series of genomic/metagenomic tools designed to recover better quality results and reliable interpretation of the system dynamics for the given systems. For this reason, a metagenomic data processing pipeline is developed to evaluate metagenomic data from three environmental engineering projects. The use of our pipeline was demonstrated and compared on three different datasets that were of different sizes, from different sequencing platforms, and generated from three different environmental sources. By designing and developing a flexible and customized pipeline, this study has showed how to process large metagenomic data sets with limited resources. This result not only would help to uncover new information from environmental samples, but also, could be applicable to any other metagenomic studies across various disciplines.

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