DOI: 10.5061/dryad.gqnk98szx
title: Data from: An accessible metagenomic strategy allows for better characterization of invertebrate bulk samples

description: DNA-based techniques are a popular approach for assessing biodiversity in
 ecological research, especially for organisms that are difficult to detect
 or identify morphologically. Metabarcoding, the most established method
 for determining species composition and relative abundance in bulk
 samples, can be more sensitive and time- and cost-effective than
 traditional morphological approaches. However, one drawback of this method
 is PCR bias caused by between-species variation in the amplification
 efficiency of a marker gene. Metagenomics, bypassing PCR amplification,
 has been proposed as an alternative to overcome this bias. Several studies
 have already shown the promising potential of metagenomics, but they all
 indicate the unavailability of reference genomes for most species in any
 ecosystem as one of the primary bottlenecks preventing its wider
 implementation. This dataset is from a study that present a strategy which
 combines unassembled reads of low-coverage whole genome sequencing and
 publicly available reference genomes to construct a genomic reference
 database, thus circumventing high sequencing costs and intensive
 bioinformatic processing. This dataset was used to show that this approach
 is superior to metabarcoding for approximating relative biomass of
 macrobenthos species from bulk samples. Furthermore, these results can be
 obtained with a sequencing effort comparable to metabarcoding. This
 strategy can accelerate the implementation of metagenomics in biodiversity
 assessments, as it should be relatively easy to adopt by laboratories
 familiar with metabarcoding and can be used as an accessible alternative.
description: # An accessible metagenomic strategy allows for better characterization of
 invertebrate bulk samples Dataset DOI:
 [10.5061/dryad.gqnk98szx](10.5061/dryad.gqnk98szx) ## Description of the
 data and file structure The Kraken2 custom database was created using
 unassembled low-coverage genome sequencing data of 24 macrobenthos species
 from the Belgian part of the North Sea and one publicly available
 assembled reference genome. The results from the metagenomic analysis
 (BIOM file) were obtained by classifying shotgun sequencing reads from
 environmental macrobenthos bulk samples using our custom database.  ###
 Files and variables #### File: macrobenthos\_classification.biom
 **Description:** BIOM format v1.0
 (see [https://biom-format.org/](https://biom-format.org/) for details)
 containing the number of metagenomic reads assigned to a particular
 taxonomic group for each environmental sample.  #### File:
 Data\_analysis\_macrobenthos.Rmd **Description:** R markdown file to
 perform the statistical analysis. Annotations are provided within the
 code. #### File: metadata\_macrobenthos\_stalen.txt
 **Description:** metadata for the environmental macrobenthos bulk samples
 that were analysed by shotgun metagenomics ##### Variables * sample: ID
 for each sample * zone: zone in the Belgian part of the North Sea where
 the sample was taken (used to differentiate the TB samples from the other
 two samples) * impact: For TB samples, indication if a sample is from a
 sand extraction area (IMP) or a reference area (REF). Not applicable (NA)
 for stations not in the TB zone * year: Year of sampling * total_reads:
 number of shotgun PE reads in metagenomic analysis #### File:
 metadata\_macrobenthos\_metagenomics.txt **Description:** biomass and
 metabarcoding data (for each species and sample) with associated metadata
 for bulk macrobenthos samples. ##### Variables * sample: sample ID *
 species: Bionomial name of the species * ncbi_txid: NCBI tax id of the
 species * dataset: indication if samples comes from the long-term (LT) or
 sand extraction (SE) dataset * station: ID of the station were the sample
 was taken * sand_extraction: indication if sand extraction was taking
 place for a given station (only applicable for SE dataset samples:
 reference = no extraction; impact = extraction; NA = Not Applicable). *
 year: year the sample was taken (NA = Not Applicable) * morph_indiv:
 number of individuals per m2 of a given species in a sample *
 morph_biomass: biomass (in gram) of a species in a sample (NA = Not
 Available) * metabarcoding_reads: number of metabarcoding reads assigned
 to a particular species in a a sample #### File: Biomass\_correction.txt
 **Description:** This file is used in the statistical analysis to
 'promote' the biomass that was assigned to Processa sp. and
 Glycera sp. to respectively Processa modica and Glycera alba.  #####
 Variables * station: station ID * Processa: biomass (in gram) assigned to
 Processa sp. by morphological identification for a particular station *
 Glycera: biomass (in gram) assigned to Glycera sp. by morphological
 identification for a particular station #### File: Add\_Taxid.py
 **Description:** Script for adding taxid's to fasta sequence headers
 so these are recognised by Kraken2 to build a custom database. #### File:
 Macrobenthos\_BPNS\_v0.1.tar.gz **Description:** Kraken2 custom database
 used in this study to classify metagenomic reads. This contains three
 files necessary for the database (description
 on [kraken2/docs/MANUAL.markdown at master · DerrickWood/kraken2 ·
 GitHub](https://github.com/DerrickWood/kraken2/blob/master/docs/MANUAL.markdown) ): * `hash.k2d`: Contains the minimizer to taxon mappings * `opts.k2d`: Contains information about the options used to build the database * `taxo.k2d`: Contains taxonomy information used to build the database ## Code/software The data_analysis_macrobenthos.Rmd file can be opened in R studio. The analysis was performed using R 4.3.0. The following packages were loaded when running the analysis: * phyloseq v1.41.1 * ggplot2 v3.5.0 * ggpubr v0.6.0  The Add_taxid.py script adds NCBI taxid's to fasta sequence headers so they can be used to build a a custom Kraken2 database. Requires Python version 3.2 or higher to run.   ## Access information Other publicly accessible locations of the data: * n/a Data was derived from the following sources: * Sequencing data for construction of the database and environmental bulk samples can be found on ENA project PRJEB83993.