Abstract
The National Ecological Observatory Network (NEON) annually performs shotgun metagenomic sequencing to sample genes within soils at 47 sites across the United States. NEON serves as a valuable educational resource, thanks to its open data policies and programming tutorials, but there is currently no introductory tutorial for performing analyses with the soil shotgun metagenomic dataset. Here, we describe a workflow for processing raw soil metagenome sequencing reads using the Sunbeam bioinformatics pipeline. The workflow includes cleaning and processing raw reads, taxonomic classification, assembly into contigs, annotation of predicted genes using custom protein databases, and exporting assemblies to the KBase platform for downstream analysis. This workflow is designed to be robust to annual data releases from NEON, and the underlying Snakemake framework can manage complex software dependencies. The workflow presented here aims to increase the accessibility of NEON's shotgun metagenome data, which can provide important clues about soil microbial communities and their ecological roles.
Highlights
The soil microbiome is responsible for key ecological processes, such as decomposition and nitrogen cycling (Allison et al 2013)
The National Ecological Observatory Network (NEON) soil metagenomics data can only be accessed in two formats: as completely raw reads released by NEON, or as processed files through the default protocols of the MG-RAST storage server
To facilitate future scientific analysis, we present a workflow for taking raw sequences and generating a processed dataset that can be linked to other NEON data products, which include soil biogeochemistry, root measurements, or aboveground plant communities
Summary
The soil microbiome is responsible for key ecological processes, such as decomposition and nitrogen cycling (Allison et al 2013). One powerful tool for studying the soil microbiome is shotgun metagenomic sequencing, in which all of the genetic material within the DNA extract of a soil sample is sequenced at once, without targeting specific organisms (Quince et al 2017, Pérez-Cobas et al 2020). Neither format is suitable for most metagenomic analyses, which generally answer scientific questions using custom data processing pipelines that use specific algorithms and targeted reference databases (Ladoukakis et al 2014; Quince et al 2017). To facilitate future scientific analysis, we present a workflow for taking raw sequences and generating a processed dataset that can be linked to other NEON data products, which include soil biogeochemistry, root measurements, or aboveground plant communities. We recommend the review by Pérez-Cobas et al (2020) for an overview of software alternatives for each step of this shotgun metagenomics analysis
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