Analysis Methods for Shotgun Metagenomics

Woloszynek, Stephen; Zhao, Zhengqiao; Ditzler, Gregory; Price, Jacob R.; Reichenberger, Erin R.; Lan, Yemin; Chen, Jian; Earl, Joshua P.; Langroodi, Saeed Keshani; Ehrlich, Garth D.; Rosen, Gail

doi:10.1007/978-3-319-74974-7_5

Cited by 2 publications

(1 citation statement)

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“…In addition, taxonomical classification based on only marker genes fails to address the functional variation within closely related genomes (Hiergeist, Gläsner et al 2015, Tremblay, Singh et al 2015, Gohl, Vangay et al 2016). Isolated DNA from a mixed microbial community can also be used for metagenomic shotgun sequencing, which has become increasingly utilized to characterize both taxonomy and function of microbial communities (Woloszynek, Zhao et al 2018). Through metagenomic shotgun sequencing, it is possible to generate sequence libraries containing the genetic information from hundreds or even millions of cells in sample, in order to understand their taxonomical classification, potential functional capabilities, and physiological traits.…”

Section: Introductionmentioning

confidence: 99%

CuBi-MeAn Customized Pipeline for Metagenomic Data Analysis

Keshani-Langroodi

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2021

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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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