DOI: 10.5061/dryad.m63xsj3xz
title: Data from msGBS: A new high-throughput approach to quantify the relative species abundance in root samples of multi-species plant communities

description: Plant interactions are as important belowground as aboveground.
 Belowground plant interactions are however inherently difficult to
 quantify, as roots of different species are difficult to disentangle.
 Although for a couple of decades molecular techniques have been
 successfully applied to quantify root abundance, root identification and
 quantification in multi-species plant communities remains particularly
 challenging. Here we present a novel methodology, multi-species Genotyping
 By Sequencing (msGBS), as a next step to tackle this challenge. First, a
 multi-species meta-reference database containing thousands of gDNA
 clusters per species is created from GBS derived High Throughput
 Sequencing (HTS) reads. Second, GBS derived HTS reads from multi-species
 root samples are mapped to this meta-reference which, after a filter
 procedure to increase the taxonomic resolution,  allows
 the parallel quantification of multiple species. 
 The  msGBS signal of 111 mock-mixture root samples, with
 up to 8 plant species per sample, was used to calculate the within-species
 abundance. Optional subsequent calibration yielded the across-species
 abundance. The within- and across-species abundances highly correlated
 (R2 range 0.72-0.94 and 0.85-0.98, respectively) to the
 biomass-based species abundance. Compared to a qPCR based method which was
 previously used to analyze the same set of samples, msGBS provided similar
 results. Additional data on 11 congener species groups within 105 natural
 field root samples showed high taxonomic resolution of the
 method.  msGBS is highly scalable in terms of sensitivity and
 species numbers within samples, which is a major advantage compared to the
 qPCR method and advances our tools to reveal hidden belowground
 interactions. This dataset belongs to the article "msGBS: A new
 high-throughput approach to quantify the relative species abundance in
 root samples of multi-species plant communities". msGBS is a
 technique that uses Genotyping By Sequencing on mixed plant species root
 samples which, after a filtering step to increase the taxonomic resolution
 and calibration, is able to estimate plant species abundances. 
 The article uses data of two different experiment: the Jena field survay
 (13 plant species) and the Dutch field survay (120 plant species).
description: The data is based on 3 lanes of High Throuput sequencing (Hiseq X
 PE 2*150bp) data. The Lab protocol is based on Genotyping By Sequencing
 (Elshire, 2011) but is adjusted in design as described in the manuscript
 and applied to mixed species samples instead of species pure material.
 Downstream analysis is not aimed to identify SNP's as in the original
 method but to quantify the relative abundance of the species present the
 the plant root mixture. The data was processed by the
 msGBS pipeline (available at: GitHub (https://github.com/NielsWagemaker/scripts_msGBS/tree/msGBS-1.0). A new, easy to install, snakemake pipelin is also available but not descripbed in the article (https://github.com/NielsWagemaker/scripts_msGBS/tree/msGBS-snake).
description: Included in this Dryad dataset are a Barcode file for
 demultiplexing the Raw sequence data available at NCBI Sequence read
 Archive (SRA; https://www.ncbi.nlm.nih.gov/bioproject/PRJNA604964), the
 metareference genome and a raw .csv data analysis output file. Also
 included are several -in-between- analysis files as described in the
 article supplemental data.  In the Dryad overview file
 (overview_DOCS.pptx), the available files are annotated to the several
 bioinformatical processes of the pipeline in order to navigate through the
 data files. Extended descriptions on bioinformatics are
 available in the supplemental data of the article. Raw
 data of Figures and Tables of the Manuscript are available
 (RAW_DATA_Wagemaker_et_al_MER_2020.xlsx) as well as metareference
 genomes.