Introducing the UK Crop Microbiome Cryobank resource: metabarcoding methods and case studies

Introducing the UK Crop Microbiome Cryobank resource: metabarcoding methods and case studies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Introducing the UK Crop Microbiome Cryobank resource: metabarcoding methods and case studies Payton To Yau, Rodrigo G Taketani, J Miguel Bonnin, Helen Stewart, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6445717/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Aug, 2025 Read the published version in Environmental Microbiome → Version 1 posted 9 You are reading this latest preprint version Abstract Background : Here, we describe AgMicrobiomeBase as an output of the UK Crop Microbiome Cryobank (UKCMCB) project, including details of the underlying meta-barcode sequence-based methods and three microbiome analysis case studies. The UKCMCB links genomic datasets and associated soil metadata with a cryobank collection of samples, for six economically significant crops: fava bean ( Vicia faba) , oil seed rape ( Brassica napus ), spring barley ( Hordeum vulgare ), spring oats ( Avena sativa) , spring wheat ( Triticum aestivum ) and sugar beet ( Beta vulgaris ). The crops were grown in nine agricultural soils from the UK, representing three major soil texture classes. The UKCMCB is a scalable sequence-based data catalogue linked to a cryo-preserved sample collection. Results : The focus of this paper is the amplicon sequencing, associated bioinformatics workflows, and development of the project data catalogue. Short-read amplicon sequencing (16S rRNA gene and ITS region) was implemented to describe the rhizosphere and bulk soil communities, for the multiple crop-soil combinations. Three case studies illustrate how different biological questions in phytobiome research can be addressed using this data resource. The three case studies illustrate how to (1) determine the impact of soil texture and location on microbiome composition, (2) determine a core microbiome for a single crop across different soil types, and (3) analyse a single genus, Fusarium within a single crop microbiome. The UKCMCB data catalogue AgMicroBiomeBase (https://agmicrobiomebase.org/data) links the sequence-based data with soil metadata and to cryopreserved samples. Conclusions: The UKCMCB provides baseline data and resources to enable researchers to assess the impact of soil type, location and crop type variables on crop soil microbiomes. The resource can be used to address biological questions and cross-study comparisons. Development of the UKCMCB will continue with the addition of metagenome and bacterial isolate genomic sequence data and has the potential to integrate additional data types including microbial phenotypes and synthetic microbial communities. microbiome cryobank crop soil rhizosphere metabarcoding Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Soil is an environment with one of the highest levels of microbe density and diversity. Microbiomes have been well described for a range of biological hosts and environmental habitats, transforming our understanding of biological functions and dysbiosis, and generating the concept of species or specific tissues as ecosystems and holobionts. An understanding of microbial communities associated with plants enable discoveries about plant-microbe interactions [1] and plant health, and potentially have wide applications for increasing crop plant productivity [2]. To date, research emphasis has been on the microbiomes of major crop species like wheat, rice and maize [3, 4]. Less attention has been given to other economically significant crop species grown in the United Kingdom (UK). Additional crops have mainly been investigated in different contexts, such as nitrogen fixation for legumes [5] or tillage systems for barley [6]. Agricultural practices influence the soil microbiota, impacting crop health, yet baseline data can be difficult to attain and/or be very diverse. In addition, there is a need to better understand the soil microbiota on a landscape and countrywide scale. Resources on crop plant microbiomes enable multiple types of research questions to be answered, and aid in development of sustainable agriculture practices. Access to microbiome data is facilitated by nucleotide archives. In Europe, microbiome datasets are accessed from MGnify [7], which stores diverse data from different environmental hosts and habitats, including plants and soils. Alternative resources such as microbiome atlases also collate datasets for context-specific investigations or provide more generalised information [8, 9]. Such resources contribute significantly to answering sequence-based research questions. However, there is a need to link rich, contextual metadata to the sequence data, as well as to physical resources held in biobanks to maximise the utility of all data types. The UK Crop Microbiome Cryobank (UKCMCB) has established a cryopreserved resource of fully characterised material from soil and rhizosphere crop microbiomes [10]. The resource comprises material from six economically significant crops: fava bean ( Vicia faba) , oil seed rape ( Brassica napus ), spring barley ( Hordeum vulgare ), spring oats ( Avena sativa) , spring wheat ( Triticum aestivum ) and sugar beet ( Beta vulgaris ), grown in nine UK agricultural soils of different textural types. The data collected included DNA sequences, soil chemistry, physical soil parameters, field locations, agricultural history of the fields, crop species and crop genotypes. The rhizosphere microbiota was characterised using a combination of sequence-independent and culture-based approaches. Multiple sequence-based data were obtained to link genomic datasets with cryo-preserved samples for each of the crop-soil combinations. Here, the amplicon sequence dataset is described alongside three case studies that illustrate how to (1) determine the impact of soil type and location on microbiome composition, (2) determine a core microbiome for a single crop across different soil types, and (3) analyse a single genus, ( Fusarium) within a single crop microbiome. More broadly, the UKCMCB has the potential to allow the analysis of plant growth traits, the generation of synthetic communities (SynComs) and comparisons across multiple microbiome datasets. Additional resources to be included in the UKCMCB are shotgun metagenomic sequences, bacterial isolate phenotype data, culturable bacterial 16S rRNA and rpoD gene taxonomy, bacterial isolate whole genome sequences and exemplar SynComs. These aspects will be discussed in additional publications. Methods The methods describe the three distinct stages of UKCMCB development for the amplicon-based data: (1) amplicon library preparation and sequencing, (2) identification and computational analysis of the amplicon sequence variants (ASVs), and (3) data deposition and data catalogue creation. The soil sample collection and pot experiment have been described previously [10]. In brief, the pot experiment comprised six crops (Table 1) and nine agricultural soils (Table 2). Each crop-soil combination, and no-crop bulk soil control comprised five biological replicates, generating a total of 270 crop-soil samples and 45 no-crop control samples. The 6 crops planted in the 9 agricultural soils gave 54 crop-soil combinations and the soils represent diverse textural classes and geographical locations within the UK. Table 1. Summary information for the agricultural soils collected for large-scale pot experiment as part of the UKCMCB project Textural Class UK County Project Code Latitude Longitude Clay loam Borders CL-BO 55.53806 -2.63665 Clay loam Yorkshire CL-YO 54.20593 -1.05670 Clay Buckinghamshire CY-BU 51.81865 -0.91014 Clay Yorkshire CY-YO 54.21591 -1.06591 Silty clay loam Shropshire SC-SH 52.49369 -2.47887 Sandy loam Angus SL-AN 56.48791 -3.13731 Sandy loam Bedfordshire SL-BE 52.00040 -0.61427 Sandy loam Shropshire SL-SH 52.42665 -2.47928 Silty clay loam Hertfordshire SC-HE 51.81805 -0.40552 Table 2 . Summary information for the six crops used for the large-scale pot experiment as part of the UKCMCB project. Common Name (Project Code) Species name Genotype Fava Beans (FB) Vicia faba Linx Oilseed Rape (OR) Brassica napus Campus Spring Barley (SB) Hordeum vulgare RGT-Planet Spring Oats (SO) Avena sativa WPB Elyann Spring Wheat (SW) Triticum aestivum Mulika Sugar Beet (SU) Beta vulgaris Dages Amplicon library preparation & sequencing The microbial composition of the rhizosphere and bulk soil were determined using amplicon sequencing from the V3-V4 region of the 16S ribosomal RNA gene for bacteria and the variable intergenic ITS-1 region for fungi. 16S rRNA gene amplicon sequencing was conducted on all six crops (Table 2) and ITS amplicon sequencing was conducted on spring wheat only. PCR controls included the no-template amplification control and a positive amplification control of a synthetic community, comprising purified gDNA from E. coli and presumptive plant-associated Bacillus spp. and Pseudomonas spp. isolates. The positive control ASV designations at the Genus level were Escherichia-Shigella, Arthrobacter and Pseudomonas respectively. Normalisation between sequencing runs was applied using a statistical technique to control for batch effects (see below). DNA was isolated for amplicon library preparation from 250 mg aliquots of each soil sample using DNeasy PowerSoil Pro Kits (Qiagen, UK) according to the manufacturer’s instructions. The DNA was quantified using QuantiFluor® ONE dsDNA System kits (Promega, UK) and normalised to ~5 ng/μL. The 16S rRNA gene sequences were amplified from the V3-V4 region using forward primer V3: 5'-CCTACGGGNGGCWGCAG-3’ and reverse primer V4: 5'- GACTACHVGGGTATCTAATCC-3’. The ITS rRNA spacer DNA sequences were amplified from the ITS1 region using forward primer ITS1-Fl2: 5'-GAACCWGCGGARGGATCA-3’ [11] and reverse primer ITS2: 5'-GCTGCGTTCTTCATCGATGC-3’ [12], both sets in 25 µl reaction volumes. The PCR amplification was carried out in a 5PrimeG/02Techne Thermal cycler (Alpha Laboratories, UK) using KAPA HiFi HotStart ReadyMixPCR Kits (Roche Life Sciences, UK). and Illumina protocols [13]. The cycling conditions for both the 16S rRNA gene and the ITS amplicon reactions were 95 °C for 3 minutes; 25 cycles consisting of 30 seconds at 95 °C, 30 seconds at 55 °C, and 30 seconds at 72 °C; with a final extension at 72 °C for 5 minutes. A Nextera Flex DNA Library kit (Illumina, UK) was used to generate the sequencing libraries, by addition of indices for 96-sample multiplexing, reaction clean up, normalisation, and pooling, as per the manufacturer’s instructions. Aliquots of 1 μl of the indexed PCR products were quantified using the QuantiFluor® ONE dsDNA System kit (Promega, UK) and the final concentration measured on a GloMax explorer (Promega, UK). The libraries were then diluted with 10 mM of Tris buffer (pH 8.5) and validated using an Agilent 2100 Bioanalyzer (Agilent Technologies Ltd, UK). The libraries were sequenced on an Illumina MiSeq machine at the James Hutton Institute (Dundee, UK) using MiSeq reagent kit v2 (500 cycles) and read length 250 bp paired end. Amplicon sequencing data analysis The workflow used for the amplicon sequence analysis is summarised in Figure 1 and the code is available on GitHub [14]. In summary, the raw sequence reads were assessed using FastQC [15] [16] and the results combined across multiple samples using MultiQC [17]. Trimmomatic [18] was used to trim the overall length of the sequences to remove low quality start and end points as determined from FastQC. A Qiime2 (v2023.5) [19] workflow was used for further quality control, denoising, merging and taxonomic assignment. Within Qiime2 Cutadapt [20] was used to trim forward and reverse adaptor sequences. DADA2 [21] was used for filtering, dereplication, chimera identification and merging paired-end reads. DADA2 models and corrects Illumina based sequence errors, enabling a robust identification of biological variants. The filtering and merging parameters for DADA2 were determined using FIGARO [22], which models the error rate for each sequence to find optimal trimming sites that will maximize read retention. The complete Qiime2 workflow gave representative amplicon sequence variants for each sample. Reference databases Silva (v 138) and UNITE (v 9.0) [23, 24] were used for taxonomic assignment of the 16S rRNA gene and ITS amplicons, respectively. Reads were classified by taxon using Qiime2’s feature classifier classify-sklearn and taxonomic filtering conducted to exclude mitochondria and chloroplasts sequences. [Figure 1] This project comprised a total of 315 sequenced samples distributed over four multiplexed sequencing runs using 96-well plates. ConQuR [25], that uses a two-part quantile regression model, was then used to control for potential batch effects. After batch effect correction, variation within and between sample sets was assessed by calculating alpha and beta diversity, respectively, using the Phyloseq package (v1.46.0) [26] in R. The specific diversity measures calculated are presented in the results sections. Data deposition and data catalogue The amplicon data for the UKCMCB project has been deposited into the European Nucleotide Archive (ENA) [27], with project identifier PRJEB58189. The raw reads for each rhizosphere sample were uploaded using the genomic Standards Consortium (GSC) checklist GSC MIxS plant associated template (ERC000020). The bulk soil (no crop) control sample raw reads were uploaded using GSC MIxS soil (ERC000020) checklist template. The UKCMCB project comprises data for bulk soils and crop rhizospheres, which are linked in a parent (soil) - child (plant) relationship. This parent-child relationship has been established through submission of our own relationship template to BioSamples [28]. This template is available as part of our project catalogue AgMicrobiomeBase [29]. In addition to parent-child data relationships, the UKCMCB project has multiple data types. Here the 16S rRNA gene and ITS data is described, but the project also includes soil metagenomic sequences, whole genome bacterial isolate sequences, phenotypic data, as well as chemical and physical soil properties. All project metadata was stored in Excel spreadsheets, with each project consortium partner contributing their metadata to one spreadsheet version. Each partner spreadsheet was then merged to a master spreadsheet using the unique identifiers as outlined in Figure 2, creating a complete project data catalogue. Queries on the data catalogue are visualised and data reports created at agmicrobioembase.org using Microsoft PowerBI. [Figure 2] Results The UKCMCB amplicon data was deposited into the ENA under study accession PRJEB58189. A summary of the raw, trimmed and merged read counts for the complete dataset is shown in Table S1. The data catalogue To enable data management and access to the UKCMCB project data in a way that met FAIR principles [30] a data catalogue, AgMicrobiomeBase [29], was created. It is a public website which links the genomic resources with extensive soil metadata [10]. Whilst the sequence data can be uploaded to public repositories, there was a need to create our own data catalogue to act as a hub for the whole project. Our catalogue creates a link between the sequence data and the cryopreserved samples: a unique feature of the project. Figure 2 outlines the relationships between the multiple data types produced by the UKCMCB project and the unique identifiers that link data within the data catalogue. The catalogue allows users to download the metadata for subsets of the amplicon and metagenome data as a spreadsheet. The metadata spreadsheet includes the ENA run accessions (ERR), allowing access to the raw sequence data with a knowledge of the sample structure (how samples relate to each other), something usually only obtained after reference to a publication. Batch effect correction To account for difference between multiple sequencing runs (n=4), a batch correction was applied. The impact was evident for the taxonomic distributions before and after batch correction with ConQuR, as shown in a non-metric multidimensional scaling (NMDS) ordination plot (Fig. 3). The overall effect was a reduction in the variation between individual samples. Prior to normalisation there was some evidence for a batch effect (Fig. 3A), especially for plate 1 (green) and plate 2 (orange). Application of the ConQuR normalisation tightened the distributions for plates 2,3 and 4 with crop-dependent groupings more evident (Fig. 3B). After normalisation, there was still an apparent batch effect for Plate 1, which might be explained by the grouping and distribution of the bulk soil / no-crop control samples on this plate. [Figure 3] After batch correction the influence of both crop type and soil type / local combination on beta diversity was assessed for the complete dataset using an NMDS ordination plot (based on the Bray-Curtis distance measure) (Fig. 4A). This plot shows distinct grouping by soil type / location combination for the different crops. Different visualisations highlight the influence for each variable of crop species (Fig. 4B), location (Fig. 4C) and soil textural class (Fig. 4D). Figure 4 also provides evidence for concurrence of sample replicates for each soil type / location - crop species combination (there were 5 sample replicates for each combination). [Figure 4] Case Studies To demonstrate the utility of our data catalogue (which combines sample meta data with ENA sequence run identifiers (ERR)), we present three case studies that address different biological questions. The first examined the influence of soil type / location on a single crop species on bacterial taxonomies; the second determined the core microbiome for the 16S rRNA gene amplicon dataset, for a single crop in all soil / location combinations; and the third assessed the fungal taxonomy for a sub-set of soil types / locations on a single crop species. Case Study One: assessing the impact of soil type and location on the bacterial microbiome composition in the sugar beet rhizosphere The variation in the rhizosphere microbiome composition was compared for a single crop species, sugar beet, grown in all nine agricultural soils. Differences in taxonomic composition between two set samples, sugar beet and the no-crop control (bulk soil), were quantified. An NMDS ordination plot of beta-diversity (based on the Bray-Curtis distance measure) shows that there is variation between the rhizosphere (and bulk soil) microbiomes across different soil types (Fig. 5A). The different combinations of soil types and soil locations showed varying diversity distributions. Microbiomes from the same soil types or same location did not clearly cluster together. For example, the silt-clay (SC) soils from two different locations, Hertfordshire and Shropshire (HE & SH) showed distinct communities, as did the two clay soils from two locations, Yorkshire and Buckinghamshire (YO & BU). However, silt-loam (SL) soils from two different locations, Angus and Shropshire (AN and SH) had overlapping community distributions, and one Bedfordshire (BE) had a separate distribution. Locations HE, YO, BU and BE each group as distinct clusters. This implies that soil type and location combined can act as a driver for distinct microbial communities. Three soil types (SC-HE, CY-BU and SL-BE) give distinctly segregated distributions, indicating that some soil type/location combinations supported unique microbial communities. A rhizosphere effect, where the presence of the plant type has a strong influence on the microbiota composition, appeared to be more pronounced in some soil type/location combinations than others, e.g. SC-HE compared to CY-BU (Fig. 5A). This was assessed further by calculating alpha diversities (Fig. 5B). Whilst different microbial communities between rhizosphere and bulk soil were evident in some locations only SC-HE showed a significant difference (adjusted p-value 0.07). [Figure 5] Case Study Two: defining a core bacterial rhizosphere microbiome for sugar beet The concept of a core microbiome refers to a set of microbiota taxa and their functional attributes that are characteristics of one environment. In Case Study One we observed that rhizosphere microbial communities varied between different soil types and locations. Yet, the diversity data also raises the question of what taxa do these communities have in common? Is there a core microbiome characteristic of the sugar beet rhizosphere regardless of the soil type and location? To answer this question, the sugar beet rhizosphere ASVs from the nine agricultural soils were first assigned to bacterial families (Fig. 6A), and the intersections between the 100 most abundant ASVs assigned at the genus level and visualised with an UpSet plot (Lex et al. 2014) created using UpSetR (Conway et al., 2017) (Fig. 6B). The UpSet visualisation showed there was variation between the number of sequences according to soil type / location. For example, the rhizosphere microbiome of sugar beet in SC-HE soil contained the highest percentage of Bacilliaceae and the rhizosphere microbiome of sugar beet in CY-BU soil had the highest percentage of Chthoniobacteraceae . Forty genera were shown to present in all nine soils from the UpSet plot (Fig. 6B). These 40 genera (Table S2) were defined as the core microbiome for sugar beet rhizospheres at the genus level (for this sample set). Eight of the nine soils had at least one genus that was specific (Fig. 6B, indicated by a green dot with no line edge), only soil CL-YO did not have a unique genus. The only soil type that contained common genera across different locations was clay (CY). These included Luteitalea , a member of the Acidobacteriota phylum, and Ohtaekwangia from the Bacteroidota phylum. No common genera were identified in other soil types, clay loam (CL), sandy loam (SL), or silty clay loam (SC). [Figure 6] Case study three: assessing the impact of soil type and location on the fungal microbiome composition in wheat, with a focus on Fusarium The fungal rhizosphere microbiome composition was compared for spring wheat across the nine soils. Variation was evident between the rhizosphere microbiomes across all the different soil types as determined from an NMDS ordination plot of beta-diversity (Fig. 7A). There were also significant differences in the variation between the sample sets, as determine by the alpha diversity (Fig 7B). The clay (CY-BU & CY-YO) soils showed the lowest diversity (Fig 7B) and distinct community distributions (Fig. 7A). Silty clay loam soil from Hertfordshire (SC-HE) had the highest diversity and a distinct community distribution from silty clay loam from Shropshire (SC-SH). The total number of fungal genera classified for each rhizosphere microbiome ranged between 180 and 259, and a total of 54 common fungi at genus level were identified across all soil rhizospheres. Interestingly, each soil rhizosphere microbiome exhibited unique fungi, ranging from 6 to 37 genera (Table S3). Within the ITS amplicon dataset multiple ASVs were classified to the genus Fusarium , corresponding to five species: F. waltergamsii , F. nurragi , and F. equiseti and F. culmorum , and F. tonkinense . Only one rhizosphere sample included F. tonkinense (SC-SH) and one F. culmorum (CY-YO), although there were additional ASVs within these samples that could only be classified to the Fusarium genus level.Hence, further analysis focused on the ASVs assigned to the Fusarium genus and three Fusarium species (Fig. 8). The samples from clay soils in Buckinghamshire (CY-BU) exhibited the highest abundance of total Fusarium ASVs, which corresponded to the highest relative level of Fusarium equiseti (Fig. 8B). In contrast, the Fusarium in silty loam from Shropshire (SL-SH) appeared to comprise more of F. nurragi (Fig. 8C) and to some extent F. waltergamsii (Fig. 8D). The abundance of F. waltergamsii ASVs was relatively low, with some degree of variation in detection within samples (Fig. 8D). [Figure 7] [Figure 8] Discussion The UK Crop Microbiome Cryobank is a publicly available resource comprising sequence-based data, meta data and cryo-preserved material related UK crop microbiomes [10]. The sequence-based data (generated from rhizosphere and bulk soil samples for six UK major crops grown in nine agricultural soils), plus associated metadata on the crop varieties and genotypes, soil type, location, heritage, and the soil chemistry, are available from the project’s AgMicrobiomeBase catalogue [29]. The data were obtained and analysed according to suggested standardisation and terminology for microbiome research [31, 32]. Generation of rhizosphere microbiomes for multiple crops and multiple agricultural soil type / location combinations allows for comparative analyses. Here, specific analyses within three use cases are presented to demonstrate the utility of the data to answer biological questions. The case studies illustrated some of the key influences on the microbiota associated with the UKCMCB resource. Soil type and geographical differences are shown to be major determinants influencing the structure of microbial communities, mirroring what has been reported elsewhere in crop focused studies (e.g. lettuce, wheat) [33-35]. The focus for the taxonomic composition of the microbiomes was on bacteria as predominant members of the rhizosphere community [36]. The analysis pipeline was developed to account for sequencing and data variability including batch correction and strand merging rates. Rarefaction was not required as there was no large imbalance of sequence reads [37]. Application of the analysis pipeline on both the bacterial (16S V3-V4 region amplicon) dataset and the partial fungal (ITS-1 region amplicon) dataset revealed that the distribution of the taxonomies varied based on both the host plant and the soil type / location combinations. While the soil is the main reservoir for the rhizosphere population, root exudation selectively recruits community members, known as the rhizosphere effect [38]. This was evident for the different crops, although to variable extents, affected mostly by the location / soil type combination. The focus on a single crop type, sugar beet, highlighted that enrichment of certain members of the soil bacteria microbiota occurred, especially for one soil type / location (silty clay loam from Hertfordshire) over others, including the clays. Although this study was not designed to compare locations, an influence of location on the taxonomic diversity was identified by examining the pan- and core-taxonomies, for a single crop (sugar beet). Soil type was also a key driver of diversity, with three out of the four soil types not sharing any of the major taxa. Only the clay soils shared common taxa. This could be due to the soil architecture of clay [39], which has the effect of enriching particular genera compared to other physical structures. Highlighting such diversity within the community composition, even in the presence of a significant rhizosphere effect, raises important questions about function. This is best determined from direct sequencing to identify functional groups coupled to functional phenotype analyses. Investigation of the fungal composition revealed functional groups of interest. The focus was on ASVs associated with the Fusarium species in spring wheat because there are several species within this genus that are phytopathogens, with wheat as a susceptible host [40]. Although ASVs derived from the fungal ITS region are insufficient to define sequence variants to the species level, they allow differential comparisons. There was a strong dependency on soil type / location for the total Fusarium population detected as well as on individual ASVs. This could be related to the presence of non-pathogenic species that occur in ‘suppressive’ soils and out-compete other, phytopathogenic species. Notably, bacterial ASVs related to the Ohtaekwangia genus were identified as one of the common taxa in the pan-genome assessment, and have previously been identified to potentially suppresses disease-causing Fusarium species [41]. Sample metadata, sufficient to allow data re-use is often lacking in datasets deposited in nucleotide repositories, like ENA. This means the re-use of the datasets relies on information held within publications that can be difficult to find and access. To ensure the UKCMCB project data meets FAIR principles (Findable, Accessible, Interoperable, and Reusable) our data catalogue aims to make the link between sample metadata and genomic data easier. The AgMicrobiomeBase catalogue also provides access to our complete bioinformatics workflow code for the amplicon dataset analysis via GitHub. Whilst only the amplicon data has been described here, the complete UKCMCB project has produced multiple additional data types (metagenomic sequence, bacterial isolate sequences and phenotypic data) that will be described elsewhere. As the AgMicrobiomeBase catalogue expands, incorporating further crop and soil datasets, the key to meeting the FAIR principle of reusability will be the sharing of bioinformatics workflows [42] and the development of and adherence to data standards to allow data integration [43]. These aspects are still in development for microbiome research but need to be made a priority: in order to realise the promise of microbiome data re-use and integration with additional data including functional phenotypes, biochemistry and pathology. Conclusion In summary, the UKCMCB provides a comprehensive resource relating to crop microbiomes, grown in different UK agricultural soils. The case studies illustrate how the dataset can be queried to answer different biological questions. Since the sequence-based data can be accessed with the extensive soil and crop metadata, they also lend themselves to comparative investigations against other datasets. A meta-analysis on cereal crops found that there was a gap in knowledge and associated datasets for barley in comparison to wheat, rice and maize, [4] and our resource helps to fill this gap. The dataset also serves as a baseline to investigate perturbations, whether from abiotic stress that may arise through climatic changes (drought, heat, salt stress), or biotic stress from infection with pathogens. Equally, since the metadata includes the plant genotype, it has relevance for investigation of alternative races or for breeding strategies, as crop genotypes are known to drive community composition [44]. The metadata within the resource also describes agronomic practices such as the heritage of the soil / location site, in turn allowing comparison of management strategies, whether for application of amendments, pesticides or tillage strategies (e.g. in [45]. Finally, as this dataset focused on the rhizosphere microbiomes, it lends itself to comparison with microbiota derived from other plant tissues, e.g. endophytic compartments or the phylosphere [46]. Abbreviations ENA: European Nucleotide Archive ITS: Internal Transcribed Spacer rRNA: ribosomal RNA SynComs: synthetic communities UKCMCB: United Kingdom Crop Microbiome Cryobank Declarations Ethics approval and consent to participate: not applicable Consent for publication: Availability of data and materials: All sequence data for the UKCMCB project has been submitted to the European Nucleotide Archive (ENA) under study accession PRJEB58189. All UKCMCB project code is available via GitHub [14]. Competing interests: none declared Funding: This work was funded by BBSRC awards: BB/T019492/1, BB/T019484/1, BB/T019700/1, BB/T019808/1 Authors' contributions: This paper was conceived and written by Nicola Holden and Susan Jones. Payton To Yau and Nicola Holden constructed the amplicon libraries. Payton To Yau developed R code and shell scripts for the UKCMCB project and conducted the data analysis for the case studies. Payton To Yau, Rodrigo G Taketani, J Miguel Bonnin, Helen Stewart, Catriona MA Thompson, Ian M Clark, Tim H Mauchline, Jacob G Malone, Matthew J Ryan, Susan Jones and Nicola Holden contributed to the development of the UKCMCB resource, from soil sample collection to nucleic acid extraction, analysis and resource development. All authors read and approved the final manuscript. Our thanks go to Tim Khouri, Paul Cutler and Simon Hill (CABI) for data visualisation of AgmicrobiomeBase using the PowerBI platform. 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Ou Y, Penton CR, Geisen S, Shen Z, Sun Y, Lv N, Wang B, Ruan Y, Xiong W, Li R et al : Deciphering underlying drivers of disease suppressiveness against pathogenic Fusarium oxysporum . Front Microbiol 2019, 10; https://doi.org/10.3389/fmicb.2019.02535. Jacobsen A, Kaliyaperumal R, da Silva Santos LOB, Mons B, Schultes E, Roos M, Thompson M: A generic workflow for the data FAIRification process. Data Intelligence 2020, 2(1-2):56-65; https://doi.org/10.1162/dint_a_00028. Nijsse B, Schaap PJ, Koehorst JJ: FAIR data station for lightweight metadata management and validation of omics studies. GigaScience 2023, 12; https://doi.org/10.1093/gigascience/giad014. Bulgarelli D, Garrido-Oter R, Münch PC, Weiman A, Dröge J, Pan Y, McHardy AC, Schulze-Lefert P: Structure and function of the bacterial root microbiota in wild and domesticated barley. Cell Host Microbe 2015, 17(3):392-403; https://doi.org/10.1016/j.chom.2015.01.011. Luo G, Li L, Friman V-P, Guo J, Guo S, Shen Q, Ling N: Organic amendments increase crop yields by improving microbe-mediated soil functioning of agroecosystems: A meta-analysis. Soil Biol Biochem 2018, 124:105-115; https://doi.org/10.1016/j.soilbio.2018.06.002. Compant S, Cambon MC, Vacher C, Mitter B, Samad A, Sessitsch A: The plant endosphere world – bacterial life within plants. Environ Microbiol 2021, 23(4):1812-1829; https://doi.org/10.1111/1462-2920.15240. Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx Table S1: A summary of the raw, trimmed and merged read counts for the complete dataset 16S metabarcoding for fava bean, oil seed rape, spring barley, spring oats, spring wheat and sugar beet (data sheet 1) and ITS metabarcoding for spring wheat only (data sheet 2). TableS2.xlsx Table S2: Case study 2: Presence and absence of bacterial genera for sugar beet in 9 agricultural soils. TableS3.xlsx Table S3: Case study 3: Presence and absence of fungal genera for spring wheat in 9 agricultural soils. Cite Share Download PDF Status: Published Journal Publication published 21 Aug, 2025 Read the published version in Environmental Microbiome → Version 1 posted Editorial decision: Revision requested 09 Jun, 2025 Reviews received at journal 06 Jun, 2025 Reviews received at journal 02 Jun, 2025 Reviewers agreed at journal 15 May, 2025 Reviewers agreed at journal 15 May, 2025 Reviewers invited by journal 14 May, 2025 Editor assigned by journal 12 May, 2025 Submission checks completed at journal 25 Apr, 2025 First submitted to journal 14 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6445717","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":457166621,"identity":"289440eb-ff33-4b57-ae1b-1499ee8b50c5","order_by":0,"name":"Payton To Yau","email":"","orcid":"","institution":"Scotland's Rural College","correspondingAuthor":false,"prefix":"","firstName":"Payton","middleName":"To","lastName":"Yau","suffix":""},{"id":457166622,"identity":"aaa928a6-5531-4332-b0b1-317e97567de6","order_by":1,"name":"Rodrigo G Taketani","email":"","orcid":"","institution":"Rothamsted Research","correspondingAuthor":false,"prefix":"","firstName":"Rodrigo","middleName":"G","lastName":"Taketani","suffix":""},{"id":457166623,"identity":"a291a207-1990-4951-b46b-2659a80a4d75","order_by":2,"name":"J Miguel Bonnin","email":"","orcid":"","institution":"CAB International","correspondingAuthor":false,"prefix":"","firstName":"J","middleName":"Miguel","lastName":"Bonnin","suffix":""},{"id":457166624,"identity":"99e64988-391a-455b-b55e-623236e0f600","order_by":3,"name":"Helen Stewart","email":"","orcid":"","institution":"CAB International","correspondingAuthor":false,"prefix":"","firstName":"Helen","middleName":"","lastName":"Stewart","suffix":""},{"id":457166625,"identity":"e165a8a1-6e86-4e69-a97c-6d1e58fba3f4","order_by":4,"name":"Catriona MA Thompson","email":"","orcid":"","institution":"John Innes Centre","correspondingAuthor":false,"prefix":"","firstName":"Catriona","middleName":"MA","lastName":"Thompson","suffix":""},{"id":457166626,"identity":"f3eb1876-5f5d-434b-b71a-3056a0bdd289","order_by":5,"name":"Ian M Clark","email":"","orcid":"","institution":"Rothamsted Research","correspondingAuthor":false,"prefix":"","firstName":"Ian","middleName":"M","lastName":"Clark","suffix":""},{"id":457166627,"identity":"5f8cb787-a412-4e69-897e-fc03321bd7b2","order_by":6,"name":"Tim H Mauchline","email":"","orcid":"","institution":"Rothamsted Research","correspondingAuthor":false,"prefix":"","firstName":"Tim","middleName":"H","lastName":"Mauchline","suffix":""},{"id":457166628,"identity":"2ae8d90e-1828-480e-aec0-5e2b1f96cc80","order_by":7,"name":"Jacob G Malone","email":"","orcid":"","institution":"John Innes Centre","correspondingAuthor":false,"prefix":"","firstName":"Jacob","middleName":"G","lastName":"Malone","suffix":""},{"id":457166629,"identity":"a8dba9a3-2ecc-4450-a352-68319cdfc4e3","order_by":8,"name":"Matthew J Ryan","email":"","orcid":"","institution":"CAB International","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"J","lastName":"Ryan","suffix":""},{"id":457166630,"identity":"5e18e660-61d5-4526-b61d-7b22218b99cc","order_by":9,"name":"Susan Jones","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYLACCRDB3oMsdIAYLTxngAoTiNUC0ZdDpBb5GckHGCwq7sjJz3x7TPrjD5s8BvbDD5hBVuICBjfSEhgkzjwzNridlyZxICGtmIEnzYCZ5wYeLRI5BgySbYcTN0jnmAG1HE5sYMhhYOb5gM9h+R9AWurnzzwD1cL/Br8WhhtAXwO1JDDc4IFqkQDZgs9hZ54ZHJA4c9hww5kcY4szaWmJbRLPDA7OweN9+fbkh48lKg7Ly7efMbxRYWOT2M+f/PDBm2N4HAYEhyWQeWwMREQkIz6/joJRMApGwShgAAAk3VKzZ/h4NwAAAABJRU5ErkJggg==","orcid":"","institution":"James Hutton Institute","correspondingAuthor":true,"prefix":"","firstName":"Susan","middleName":"","lastName":"Jones","suffix":""},{"id":457166631,"identity":"51284291-c891-4ce8-902b-5bda3a9a1e3f","order_by":10,"name":"Nicola Holden","email":"","orcid":"","institution":"Scotland's Rural College","correspondingAuthor":false,"prefix":"","firstName":"Nicola","middleName":"","lastName":"Holden","suffix":""}],"badges":[],"createdAt":"2025-04-14 11:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6445717/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6445717/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40793-025-00768-5","type":"published","date":"2025-08-21T16:29:40+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82896840,"identity":"00e0080f-ac1e-408f-bf52-da851ff7d6ec","added_by":"auto","created_at":"2025-05-16 13:01:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":173355,"visible":true,"origin":"","legend":"\u003cp\u003eThe amplicon sequencing analysis workflow. \u003cstrong\u003e(1)\u003c/strong\u003e FastQC [11]and MultiQC [6] were used for initial quality checking. \u003cstrong\u003e(2)\u003c/strong\u003e Trimmomatic [18] was used to remove low quality sequence start and end points. Within Qiime2 [8] \u003cstrong\u003e(3)\u003c/strong\u003e Cutadapt [12]was implemented for adapter trimming. \u003cstrong\u003e(4)\u003c/strong\u003e DADA2 for denoising and merging the reads, with parameters estimated using FIGARO [13,14]. \u003cstrong\u003e(5)\u003c/strong\u003e Amplicon sequence variants were then defined and taxon assignments made using the Silva v138 [15]and UNITE v9 [24]reference databases. A final step within Qiime was the filtering of ASVs identified as mitochondrial and chloroplastic. \u003cstrong\u003e(7)\u003c/strong\u003e ConQuR [25]was then used for batch effect correction and the \u003cstrong\u003e(8)\u003c/strong\u003e Phyloseq R package [26]for the calculation of alpha and beta diversity.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6445717/v1/9e9d4348c59f657c549ba6d4.png"},{"id":82898313,"identity":"c65ab438-7c9c-4a55-91ed-6ecb29ed41f2","added_by":"auto","created_at":"2025-05-16 13:09:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":100752,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of the (\u003cstrong\u003eA\u003c/strong\u003e) relationships between the multiple data types generated for the UKCMCB project and (\u003cstrong\u003eB\u003c/strong\u003e) the unique identifiers assigned for use within the agmicrobiome catalogue and deposited with the sequence data into the public sequence databases.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6445717/v1/63ae36811e1bdfd54efbeeeb.png"},{"id":82901004,"identity":"5847ee34-20bf-4ab5-83ef-bb1b0b518755","added_by":"auto","created_at":"2025-05-16 13:25:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":162379,"visible":true,"origin":"","legend":"\u003cp\u003eTaxonomic distribution of the 16S rRNA gene ASVs displayed on an ordinance plot for β diversity based on the Bray-Curtis distance (\u003cstrong\u003eA\u003c/strong\u003e) before normalisation and (\u003cstrong\u003eB\u003c/strong\u003e) after normalisation using ConQuR. The datapoints are displayed by sequencing plate batch (1-4: colours) and by crop type (symbols).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6445717/v1/d74bcc2664582c541b478007.png"},{"id":82898320,"identity":"7b71f24e-a600-4bc9-8979-66089c06ca15","added_by":"auto","created_at":"2025-05-16 13:09:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":189485,"visible":true,"origin":"","legend":"\u003cp\u003eLocation driven microbe recruitment. NMDS ordination plots of beta diversity based on the Bray-Curtis distance for the complete dataset (6 crops and 9 agricultural soils). Colours based on (\u003cstrong\u003eA\u003c/strong\u003e) soil type/location (\u003cstrong\u003eB\u003c/strong\u003e) crop (\u003cstrong\u003eC\u003c/strong\u003e) location (\u003cstrong\u003eD\u003c/strong\u003e) soil type. See Table 2 for soil legend.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6445717/v1/08b944b96139129fde1765da.png"},{"id":82899478,"identity":"19d5dcda-f2dc-4623-9e22-b80a7c068ada","added_by":"auto","created_at":"2025-05-16 13:17:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":98653,"visible":true,"origin":"","legend":"\u003cp\u003eCase study 1: assessing the impact of soil type and location on the bacterial microbiome composition of the sugar beet rhizosphere. (\u003cstrong\u003eA\u003c/strong\u003e) NMDS ordination plot of beta diversity based on the Bray-Curtis distance for sugar beet rhizosphere (green) and bulk soil (brown) microbiomes of nine agricultural soils. The symbols and ellipsoids indicate the soil location and type. \u003cstrong\u003e(B)\u003c/strong\u003e\u003cem\u003e \u003c/em\u003eShannon alpha diversity for sugar beet rhizosphere (green) and bulk soil (brown) microbiomes of nine agricultural soils (see Table 2 for soil legend)).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6445717/v1/2a6d91a5025aa40f172ed980.png"},{"id":82896850,"identity":"d561b099-84c3-45ef-bb45-7539eba70785","added_by":"auto","created_at":"2025-05-16 13:01:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":56161,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCase study 2. Defining a core bacterial microbiome for the sugar beet rhizosphere. (A) \u003c/strong\u003eStacked bar chart showing relative percentage of ASVs assigned to top 10 taxonomic groups at the family level for the 9 soil types. \u003cstrong\u003e(B\u003c/strong\u003e) UpSet plot of the intersection of taxa sets across the 9 soil types for the 100 most abundant ASVs assigned at the genus level. The x-axis on the bar chart (upper panel) denotes counts by pattern of genera. One the lower panel each row corresponds to a soil, and each column denotes genus counts. Green dots indicate genera (either common or unique) present in groups. A line edge joins the dots to indicate common genera between soil types. see Table 2 for soil legend).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6445717/v1/6cffd94899c2c26e9f12dc7d.png"},{"id":82899481,"identity":"6d22a8df-07c4-4f6c-9ce9-920ce6c88480","added_by":"auto","created_at":"2025-05-16 13:17:54","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":81594,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCase Study 3. Fungal microbiome of spring wheat rhizosphere for nine agricultural soils (A)\u003c/strong\u003e NMDS ordination plot of beta diversity based on the Bray-Curtis distance, \u003cstrong\u003e(B)\u003c/strong\u003e\u003cem\u003e \u003c/em\u003eShannon alpha diversity (see Table 2 for soil legend))\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6445717/v1/6b59c11f3ebe6a1e01ed5ad4.png"},{"id":82901606,"identity":"bc081225-1286-48bb-aaa5-eb9700c87325","added_by":"auto","created_at":"2025-05-16 13:33:53","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":81210,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCase Study 3: A focus on \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFusarium\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e genus in wheat rhizosphere: \u003c/strong\u003eAbundance of ASVs classified as \u003cem\u003eFusarium\u003c/em\u003e across nine agricultural soils\u003cstrong\u003e.\u003c/strong\u003e: (\u003cstrong\u003eA)\u003c/strong\u003e \u003cem\u003eFusarium \u003c/em\u003egenus\u003cem\u003e \u003c/em\u003e(all). (\u003cstrong\u003eB)\u003c/strong\u003e \u003cem\u003eFusarium equiseti\u003c/em\u003e. (\u003cstrong\u003eC)\u003c/strong\u003e \u003cem\u003eFusarium nurragi\u003c/em\u003e, (\u003cstrong\u003eD)\u003c/strong\u003e \u003cem\u003eFusarium waltergamsii. \u003c/em\u003eSee Table 2 for soil legend.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6445717/v1/3e09413a18c3b0068cc879fe.png"},{"id":89847372,"identity":"13f56117-0598-468e-8509-3939e4e0d52b","added_by":"auto","created_at":"2025-08-25 16:43:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1744324,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6445717/v1/a44c311f-d4d0-41bd-b0c2-c8030e738de2.pdf"},{"id":82896842,"identity":"58f9244e-abb6-4ceb-b06b-d533c7010d5b","added_by":"auto","created_at":"2025-05-16 13:01:53","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":60207,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1\u003c/strong\u003e: A summary of the raw, trimmed and merged read counts for the complete dataset 16S metabarcoding for fava bean, oil seed rape, spring barley, spring oats, spring wheat and sugar beet (data sheet 1) and ITS metabarcoding for spring wheat only (data sheet 2).\u003c/p\u003e","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6445717/v1/995e479a80f5e546b6d63f84.xlsx"},{"id":82898314,"identity":"de12425c-b78c-4382-86c1-fe1cdaf7215e","added_by":"auto","created_at":"2025-05-16 13:09:53","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":27146,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S2\u003c/strong\u003e: Case study 2: Presence and absence of bacterial genera for sugar beet in 9 agricultural soils.\u003c/p\u003e","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6445717/v1/a6c1a781b08738a2c85f634a.xlsx"},{"id":82896856,"identity":"0b92462e-3b08-4484-8c76-6cc1bdaa9cd6","added_by":"auto","created_at":"2025-05-16 13:01:54","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":42075,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S3\u003c/strong\u003e: Case study 3: Presence and absence of fungal genera for spring wheat in 9 agricultural soils.\u003c/p\u003e","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6445717/v1/d119930ada2cb4132e628f3e.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Introducing the UK Crop Microbiome Cryobank resource: metabarcoding methods and case studies","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSoil is an environment with one of the highest levels of microbe density and diversity. Microbiomes have been well described for a range of biological hosts and environmental habitats, transforming our understanding of biological functions and dysbiosis, and generating the concept of species or specific tissues as ecosystems and holobionts. An understanding of microbial communities associated with plants enable discoveries about plant-microbe interactions [1] and plant health, and potentially have wide applications for increasing crop plant productivity [2]. To date, research emphasis has been on the microbiomes of major crop species like wheat, rice and maize [3, 4]. Less attention has been given to other economically significant crop species grown in the United Kingdom (UK). Additional crops have mainly been investigated in different contexts, such as nitrogen fixation for legumes [5] or tillage systems for barley [6]. Agricultural practices influence the soil microbiota, impacting crop health, yet baseline data can be difficult to attain and/or be very diverse. In addition, there is a need to better understand the soil microbiota on a landscape and countrywide scale. Resources on crop plant microbiomes enable multiple types of research questions to be answered, and aid in development of sustainable agriculture practices.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccess to microbiome data is facilitated by nucleotide archives. In Europe, microbiome datasets are accessed from MGnify [7], which stores diverse data from different environmental hosts and habitats, including plants and soils. Alternative resources such as microbiome atlases also collate datasets for context-specific investigations or provide more generalised information [8, 9]. Such resources contribute significantly to answering sequence-based research questions. However, there is a need to link rich, contextual metadata to the sequence data, as well as to physical resources held in biobanks to maximise the utility of all data types.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe UK Crop Microbiome Cryobank (UKCMCB) has established a cryopreserved resource of fully characterised material from soil and rhizosphere crop microbiomes [10]. The resource comprises material from six economically significant crops:\u0026nbsp;fava bean (\u003cem\u003eVicia faba)\u003c/em\u003e, oil seed rape (\u003cem\u003eBrassica napus\u003c/em\u003e), spring barley\u003cem\u003e\u0026nbsp;\u003c/em\u003e(\u003cem\u003eHordeum vulgare\u003c/em\u003e), spring oats (\u003cem\u003eAvena sativa)\u003c/em\u003e, spring wheat\u003cem\u003e\u0026nbsp;\u003c/em\u003e(\u003cem\u003eTriticum aestivum\u003c/em\u003e) and sugar beet\u003cem\u003e\u0026nbsp;\u003c/em\u003e(\u003cem\u003eBeta vulgaris\u003c/em\u003e), grown in\u0026nbsp;nine UK agricultural soils of different textural types. The data collected included DNA sequences, soil chemistry, physical soil parameters, field locations, agricultural history of the fields, crop species and crop genotypes. The rhizosphere microbiota was characterised using a combination of sequence-independent and culture-based approaches.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMultiple sequence-based data were obtained to link genomic datasets with cryo-preserved samples for each of the crop-soil combinations. Here, the amplicon sequence dataset is described alongside three case studies that illustrate how to (1) determine the impact of soil type and location on microbiome composition, (2) determine a core microbiome for a single crop across different soil types, and (3) analyse a single genus, (\u003cem\u003eFusarium)\u003c/em\u003e within a single crop microbiome.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMore broadly, the UKCMCB has the potential to allow the analysis of plant growth traits, the generation of synthetic communities (SynComs) and comparisons across multiple microbiome datasets. Additional resources to be included in the UKCMCB are shotgun metagenomic sequences, bacterial isolate phenotype data, culturable bacterial 16S rRNA and \u003cem\u003erpoD\u003c/em\u003e gene taxonomy, bacterial isolate whole genome sequences and exemplar SynComs. These aspects will be discussed in additional publications.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe methods describe the three distinct stages of\u0026nbsp;UKCMCB\u0026nbsp;development for the amplicon-based data: (1) amplicon library preparation and sequencing, (2) identification and computational analysis of the amplicon sequence variants (ASVs), and (3) data deposition and data catalogue creation. The soil sample collection and pot experiment have been described previously [10]. In brief, the pot experiment comprised six crops (Table 1) and nine agricultural soils (Table 2). Each crop-soil combination, and no-crop bulk soil control comprised five biological replicates, generating a total of 270 crop-soil samples and 45 no-crop control samples.\u0026nbsp;The 6 crops planted in the 9 agricultural soils gave 54 crop-soil combinations and the soils represent diverse textural classes and geographical locations within the UK.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 1.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eSummary information for the agricultural soils collected for large-scale pot experiment as part of the UKCMCB project\u003c/em\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" title=\"\" summary=\"\" width=\"539\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTextural Class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUK County\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProject Code\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLatitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLongitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eClay loam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eBorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eCL-BO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e55.53806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e-2.63665\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eClay loam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eYorkshire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eCL-YO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e54.20593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e-1.05670\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eClay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eBuckinghamshire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eCY-BU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e51.81865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e-0.91014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eClay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eYorkshire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eCY-YO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e54.21591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e-1.06591\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eSilty clay loam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eShropshire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eSC-SH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e52.49369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e-2.47887\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eSandy loam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eAngus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eSL-AN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e56.48791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e-3.13731\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eSandy loam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eBedfordshire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eSL-BE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e52.00040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e-0.61427\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eSandy loam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eShropshire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eSL-SH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e52.42665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e-2.47928\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eSilty clay loam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eHertfordshire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eSC-HE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e51.81805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e-0.40552\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 2\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e.\u0026nbsp;\u003c/em\u003e\u003cem\u003eSummary information for the six crops used for the large-scale pot experiment as part of the UKCMCB project.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"520\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommon Name (Project Code)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecies name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eFava Beans (FB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cem\u003eVicia faba\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eLinx\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eOilseed Rape (OR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cem\u003eBrassica napus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eCampus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eSpring Barley (SB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cem\u003eHordeum vulgare\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eRGT-Planet\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eSpring Oats (SO)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cem\u003eAvena sativa\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eWPB Elyann\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eSpring Wheat (SW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cem\u003eTriticum aestivum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eMulika\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eSugar Beet (SU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cem\u003eBeta vulgaris\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eDages\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch2\u003eAmplicon library preparation \u0026amp; sequencing\u003c/h2\u003e\n\u003cp\u003eThe microbial composition of the rhizosphere and bulk soil were determined using amplicon sequencing from the V3-V4 region of the 16S ribosomal RNA gene for bacteria and the variable intergenic ITS-1 region for fungi. 16S rRNA gene amplicon sequencing was conducted on all six crops (Table 2) and ITS amplicon sequencing was conducted on spring wheat only. PCR controls included the no-template amplification control and a positive amplification control of a synthetic community, comprising purified gDNA from \u003cem\u003eE. coli\u003c/em\u003e and presumptive plant-associated \u003cem\u003eBacillus\u0026nbsp;\u003c/em\u003espp. and \u003cem\u003ePseudomonas\u0026nbsp;\u003c/em\u003espp. isolates. The positive control ASV designations at the Genus level were \u003cem\u003eEscherichia-Shigella,\u003c/em\u003e \u003cem\u003eArthrobacter\u003c/em\u003e and \u003cem\u003ePseudomonas\u003c/em\u003e respectively. Normalisation between sequencing runs was applied using a statistical technique to control for batch effects (see below).\u003c/p\u003e\n\u003cp\u003eDNA was isolated for amplicon library preparation from 250 mg aliquots of each soil sample using DNeasy PowerSoil Pro Kits (Qiagen, UK) according to the manufacturer\u0026rsquo;s instructions. The DNA was quantified using QuantiFluor\u0026reg; ONE dsDNA System kits (Promega, UK) and normalised to ~5 ng/\u0026mu;L. The 16S rRNA gene sequences were amplified from the V3-V4 region using forward primer V3: 5\u0026apos;-CCTACGGGNGGCWGCAG-3\u0026rsquo; and reverse primer V4: 5\u0026apos;- GACTACHVGGGTATCTAATCC-3\u0026rsquo;. The ITS rRNA spacer DNA sequences were amplified from the ITS1 region using forward primer ITS1-Fl2: 5\u0026apos;-GAACCWGCGGARGGATCA-3\u0026rsquo; [11] and reverse primer ITS2: 5\u0026apos;-GCTGCGTTCTTCATCGATGC-3\u0026rsquo; [12], both sets in 25 \u0026micro;l reaction volumes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe PCR amplification was carried out in a 5PrimeG/02Techne Thermal cycler (Alpha Laboratories, UK) using KAPA HiFi HotStart ReadyMixPCR Kits (Roche Life Sciences, UK). and Illumina protocols [13]. The cycling conditions for both the 16S rRNA gene and the ITS amplicon reactions were 95 \u0026deg;C for 3 minutes; 25 cycles consisting of 30 seconds at 95 \u0026deg;C, 30 seconds at 55 \u0026deg;C, and 30 seconds at 72 \u0026deg;C; with a final extension at 72 \u0026deg;C for 5 minutes. A Nextera Flex DNA Library kit (Illumina, UK) was used to generate the sequencing libraries, by addition of indices for 96-sample multiplexing, reaction clean up, normalisation, and pooling, as per the manufacturer\u0026rsquo;s instructions. Aliquots of 1 \u0026mu;l of the indexed PCR products were quantified using the QuantiFluor\u0026reg; ONE dsDNA System kit (Promega, UK) and the final concentration measured on a GloMax explorer (Promega, UK). The libraries were then diluted with 10 mM of Tris buffer (pH 8.5) and validated using an Agilent 2100 Bioanalyzer (Agilent Technologies Ltd, UK). The libraries were sequenced on an Illumina MiSeq machine at the James Hutton Institute (Dundee, UK) using MiSeq reagent kit v2 (500 cycles) and read length 250 bp paired end.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAmplicon sequencing data analysis\u003c/h2\u003e\n\u003cp\u003eThe workflow used for the amplicon sequence analysis is summarised in Figure 1 and the code is available on GitHub [14]. \u0026nbsp;In summary, the raw sequence reads were assessed using FastQC [15] [16] and the results combined across multiple samples using MultiQC [17]. Trimmomatic [18] was used to trim the overall length of the sequences to remove low quality start and end points as determined from FastQC. A Qiime2 (v2023.5) [19] workflow was used for further quality control, denoising, merging and taxonomic assignment. Within Qiime2 Cutadapt [20] was used to trim forward and reverse adaptor sequences. DADA2 [21] was used for filtering, dereplication, chimera identification and merging paired-end reads. DADA2 models and corrects Illumina based sequence errors, enabling a robust identification of biological variants. The filtering and merging parameters for DADA2 were determined using FIGARO [22], which models the error rate for each sequence to find optimal trimming sites that will maximize read retention. The complete Qiime2 workflow gave representative amplicon sequence variants for each sample. Reference databases Silva (v 138) and UNITE (v 9.0) [23, 24]\u0026nbsp;were used for taxonomic assignment of the 16S rRNA gene and ITS amplicons, respectively. Reads were classified by taxon using Qiime2\u0026rsquo;s feature classifier classify-sklearn and taxonomic filtering conducted to exclude mitochondria and chloroplasts sequences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Figure 1]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project comprised a total of 315 sequenced samples distributed over four multiplexed sequencing runs using 96-well plates.\u0026nbsp;ConQuR [25], that uses a two-part quantile regression model, was then used to control for potential batch effects.\u003c/p\u003e\n\u003cp\u003eAfter batch effect correction, variation within and between sample sets was assessed by calculating alpha and beta diversity, respectively, using the\u0026nbsp;Phyloseq package (v1.46.0) [26] in R. The specific diversity measures calculated are presented in the results sections.\u003c/p\u003e\n\u003ch2\u003eData deposition and data catalogue \u003c/h2\u003e\n\u003cp\u003eThe amplicon data for the UKCMCB project has been deposited into the European Nucleotide Archive (ENA) [27], with project identifier\u0026nbsp;PRJEB58189.\u0026nbsp;The raw reads for each rhizosphere sample were uploaded using the genomic Standards Consortium (GSC) checklist GSC MIxS plant associated template (ERC000020). The bulk soil (no crop) control sample raw reads were uploaded using GSC MIxS soil (ERC000020) checklist template. The\u0026nbsp;UKCMCB\u0026nbsp;project comprises data for bulk soils and crop rhizospheres, which are linked in a parent (soil) - child (plant) relationship. This parent-child relationship has been established through submission of our own relationship template to BioSamples\u0026nbsp;[28]. This template is available as part of our project catalogue\u0026nbsp;AgMicrobiomeBase\u0026nbsp;[29].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition to parent-child data relationships, the UKCMCB project has multiple data types. Here the 16S rRNA gene and ITS data is described, but the project also includes soil metagenomic sequences, whole genome bacterial isolate sequences, phenotypic data, as well as chemical and physical soil properties. All project metadata was stored in Excel spreadsheets, with each project consortium partner contributing their metadata to one spreadsheet version. Each partner spreadsheet was then merged to a master spreadsheet using the unique identifiers as outlined in Figure 2, creating a complete project data catalogue. Queries on the data catalogue are visualised and data reports created at agmicrobioembase.org using Microsoft PowerBI.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Figure 2]\u003c/strong\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe UKCMCB amplicon data was deposited into the ENA under study accession PRJEB58189. A summary of the raw, trimmed and merged read counts for the complete dataset is shown in Table S1.\u003c/p\u003e\n\u003ch2\u003eThe data catalogue\u003c/h2\u003e\n\u003cp\u003eTo enable data management and access to the UKCMCB project data in a way that met FAIR principles [30] a data catalogue, AgMicrobiomeBase [29], was created. It is a public website which links the genomic resources with extensive soil metadata [10].\u0026nbsp;Whilst the sequence data can be uploaded to public repositories, there was a need to create our own data catalogue to act as a hub for the whole project. Our catalogue creates a link between the sequence data and the cryopreserved samples: a unique feature of the project. Figure 2 outlines the relationships between the multiple data types produced by the UKCMCB project and the unique identifiers that link data within the data catalogue. The catalogue allows users to download the metadata for subsets of the amplicon and metagenome data as a spreadsheet. The metadata spreadsheet includes the ENA run accessions (ERR), allowing access to the raw sequence data with a knowledge of the sample structure (how samples relate to each other), something usually only obtained after reference to a publication.\u003c/p\u003e\n\u003ch2\u003eBatch effect correction\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eTo account for difference between multiple sequencing runs (n=4), a batch correction was applied. The impact was evident for the taxonomic distributions before and after batch correction with ConQuR, as shown in a non-metric multidimensional scaling (NMDS) ordination plot (Fig. 3). The overall effect was a reduction in the variation between individual samples. Prior to normalisation there was some evidence for a batch effect (Fig. 3A), especially for plate 1 (green) and plate 2 (orange). Application of the ConQuR normalisation tightened the distributions for plates 2,3 and 4 with crop-dependent groupings more evident (Fig. 3B). After normalisation, there was still an apparent batch effect for Plate 1, which might be explained by the grouping and distribution of the bulk soil / no-crop control samples on this plate.\u003c/p\u003e\n\u003cp\u003e[Figure 3]\u003c/p\u003e\n\u003cp\u003eAfter batch correction the influence of both crop type and soil type / local combination on beta diversity was assessed for the complete dataset using an NMDS ordination plot (based on the Bray-Curtis distance measure) (Fig. 4A). This plot shows distinct grouping by soil type / location combination for the different crops. Different visualisations highlight the influence for each variable of crop species (Fig. 4B), location (Fig. 4C) and soil textural class (Fig. 4D). Figure 4 also provides evidence for concurrence of sample replicates for each soil type / location - crop species combination (there were 5 sample replicates for each combination).\u003c/p\u003e\n\u003cp\u003e[Figure 4]\u003c/p\u003e\n\u003ch2\u003eCase Studies\u003c/h2\u003e\n\u003cp\u003eTo demonstrate the utility of our data catalogue (which combines sample meta data with ENA sequence run identifiers (ERR)), we present\u0026nbsp;three case studies that address different biological questions. The first examined the influence of soil type / location on a single crop species on bacterial taxonomies; the second determined the core microbiome for the 16S rRNA gene amplicon dataset, for a single crop in all soil / location combinations; and the third assessed the fungal taxonomy for a sub-set of soil types / locations on a single crop species.\u003c/p\u003e\n\u003ch2\u003eCase Study One: assessing the\u0026nbsp;impact of soil type and location on the bacterial microbiome composition in the sugar beet rhizosphere\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe variation in the rhizosphere microbiome composition was compared for a single crop species, sugar beet, grown in all nine agricultural soils. Differences in taxonomic composition between two set samples, sugar beet and the no-crop control (bulk soil),\u0026nbsp;were\u0026nbsp;quantified. An NMDS ordination plot of beta-diversity (based on the Bray-Curtis distance measure) shows that there is variation between the rhizosphere (and bulk soil) microbiomes across different soil types (Fig. 5A). The different combinations of soil types and soil locations showed varying diversity distributions. Microbiomes from the same soil types or same location did not clearly cluster together. For example, the silt-clay (SC) soils from two different locations,\u0026nbsp;Hertfordshire and Shropshire\u0026nbsp;(HE \u0026amp; SH) showed distinct communities, as did the two clay soils from two locations,\u0026nbsp;Yorkshire and Buckinghamshire\u0026nbsp;(YO \u0026amp; BU). However, silt-loam (SL) soils from two different locations, Angus and\u0026nbsp;Shropshire\u0026nbsp;(AN and SH) had overlapping community distributions, and one\u0026nbsp;Bedfordshire\u0026nbsp;(BE) had a separate distribution. Locations HE, YO, BU and BE each group as distinct clusters. This implies that soil type and location combined can act as a driver for distinct microbial communities. Three soil types (SC-HE, CY-BU and SL-BE) give distinctly segregated distributions, indicating that some soil type/location combinations supported unique microbial communities.\u003c/p\u003e\n\u003cp\u003eA rhizosphere effect, where the presence of the plant type has a strong influence on the microbiota composition, appeared to be more pronounced in some soil type/location combinations than others, e.g. SC-HE compared to CY-BU (Fig. 5A). This was assessed further by calculating alpha diversities (Fig. 5B). Whilst different microbial communities between rhizosphere and bulk soil were evident in some locations only SC-HE showed a significant difference (adjusted p-value 0.07). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Figure 5]\u003c/p\u003e\n\u003ch2\u003eCase Study Two: defining a core bacterial rhizosphere microbiome for sugar beet\u003c/h2\u003e\n\u003cp\u003eThe concept of a core microbiome refers to a set of microbiota taxa and their functional attributes that are characteristics of one environment. In Case Study One we observed that rhizosphere microbial communities varied between different soil types and locations. Yet, the diversity data also raises the question of what taxa do these communities have in common? Is there a core microbiome characteristic of the sugar beet rhizosphere regardless of the soil type and location? To answer this question, the sugar beet rhizosphere ASVs from the nine agricultural soils were first assigned to bacterial families (Fig. 6A), and\u0026nbsp;the intersections between the 100 most abundant ASVs assigned at the genus level and \u003cem\u003evisualised with\u0026nbsp;\u003c/em\u003ean UpSet plot (Lex et al.\u0026nbsp;2014) created using UpSetR (Conway et al., 2017) (Fig. 6B). The UpSet visualisation showed there was variation between the number of sequences according to soil type / location. For example, the rhizosphere microbiome of sugar beet in SC-HE soil contained the highest percentage of \u003cem\u003eBacilliaceae\u003c/em\u003e and the rhizosphere microbiome of sugar beet in CY-BU soil had the highest percentage of \u003cem\u003eChthoniobacteraceae\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eForty genera were shown to present in all nine soils from the UpSet plot (Fig. 6B). These 40 genera (Table S2) were defined as the core microbiome for sugar beet rhizospheres at the genus level (for this sample set).\u0026nbsp;Eight of the nine soils had at least one genus that was specific (Fig. 6B, indicated by a green dot with no line edge), only soil CL-YO did not have a unique genus. The only soil type that contained common genera across different locations was clay (CY). These included \u003cem\u003eLuteitalea\u003c/em\u003e, a member of the \u003cem\u003eAcidobacteriota\u003c/em\u003e phylum, and \u003cem\u003eOhtaekwangia\u003c/em\u003e from the \u003cem\u003eBacteroidota\u0026nbsp;\u003c/em\u003ephylum. No common genera were identified in other soil types, clay loam (CL), sandy loam (SL), or silty clay loam (SC).\u003c/p\u003e\n\u003cp\u003e[Figure 6]\u003c/p\u003e\n\u003ch2\u003eCase study three:\u0026nbsp;assessing the\u0026nbsp;impact of soil type and location on the fungal microbiome composition in wheat, with a focus on \u003cem\u003eFusarium\u003c/em\u003e\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe fungal rhizosphere microbiome composition was compared for spring wheat across the nine soils. Variation was evident between the rhizosphere microbiomes across all the different soil types as determined from an NMDS ordination plot of beta-diversity (Fig. 7A). There were also significant differences in the variation between the sample sets, as determine by the alpha diversity (Fig 7B). The clay (CY-BU \u0026amp; CY-YO) soils showed the lowest diversity (Fig 7B) and distinct community distributions (Fig. 7A).\u0026nbsp;Silty clay loam soil from Hertfordshire (SC-HE) had the highest diversity and a distinct community distribution from silty clay loam from Shropshire (SC-SH). The total number of fungal genera classified for each rhizosphere microbiome ranged between 180 and 259, and a total of 54 common fungi at genus level were identified across all soil rhizospheres. Interestingly, each soil rhizosphere microbiome exhibited unique fungi, ranging from 6 to 37 genera (Table S3).\u003c/p\u003e\n\u003cp\u003eWithin the ITS amplicon dataset multiple ASVs were classified to the genus \u003cem\u003eFusarium\u003c/em\u003e, corresponding to five species: \u003cem\u003eF. waltergamsii\u003c/em\u003e, \u003cem\u003eF. nurragi\u003c/em\u003e, and \u003cem\u003eF. equiseti\u003c/em\u003e and \u003cem\u003eF. culmorum\u003c/em\u003e, and \u003cem\u003eF. tonkinense\u003c/em\u003e. Only one rhizosphere sample included \u003cem\u003eF. tonkinense\u0026nbsp;\u003c/em\u003e(SC-SH) and one \u003cem\u003eF. culmorum\u0026nbsp;\u003c/em\u003e(CY-YO), although there were additional ASVs within these samples that could only be classified to the \u003cem\u003eFusarium\u003c/em\u003e genus level.Hence, further analysis focused on the ASVs assigned to the \u003cem\u003eFusarium\u003c/em\u003e genus and three \u003cem\u003eFusarium\u003c/em\u003e species (Fig. 8). The samples from clay soils in Buckinghamshire (CY-BU) exhibited the highest abundance of total \u003cem\u003eFusarium\u003c/em\u003e ASVs, which corresponded to the highest relative level of \u003cem\u003eFusarium equiseti\u003c/em\u003e (Fig. 8B). In contrast, the \u003cem\u003eFusarium\u003c/em\u003e in silty loam from Shropshire (SL-SH) appeared to comprise more of \u003cem\u003eF. nurragi\u003c/em\u003e (Fig. 8C) and to some extent \u003cem\u003eF. waltergamsii\u003c/em\u003e (Fig. 8D). The abundance of \u003cem\u003eF. waltergamsii\u003c/em\u003e ASVs was relatively low, with some degree of variation in detection within samples (Fig. 8D).\u003c/p\u003e\n\u003cp\u003e[Figure 7]\u003c/p\u003e\n\u003cp\u003e[Figure 8]\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe UK Crop Microbiome Cryobank is a publicly available resource comprising sequence-based data, meta data and cryo-preserved material related UK crop microbiomes [10]. The sequence-based data (generated from rhizosphere and bulk soil samples for six UK major crops grown in nine agricultural soils), plus associated metadata on the crop varieties and genotypes, soil type, location, heritage, and the soil chemistry, are available from the project\u0026rsquo;s AgMicrobiomeBase catalogue [29].\u0026nbsp;The data were obtained and analysed according to suggested standardisation and terminology for microbiome research [31, 32].\u003c/p\u003e\n\u003cp\u003eGeneration of rhizosphere microbiomes for multiple crops and multiple agricultural soil type / location combinations allows for comparative analyses. Here, specific analyses within three use cases are presented to demonstrate the utility of the data to answer biological questions. The case studies illustrated some of the key influences on the microbiota associated with the UKCMCB resource. Soil type and geographical differences are shown to be major determinants influencing the structure of microbial communities, mirroring what has been reported elsewhere in crop\u0026nbsp;focused studies (e.g. lettuce, wheat) [33-35].\u003c/p\u003e\n\u003cp\u003eThe focus for the taxonomic composition of the microbiomes was on bacteria as predominant members of the rhizosphere community [36]. The analysis pipeline was developed to account for sequencing and data variability including batch correction and strand merging rates.\u0026nbsp;Rarefaction was not required as there was no large imbalance of sequence reads [37].\u0026nbsp;Application of the analysis pipeline on both the bacterial (16S V3-V4 region amplicon) dataset and the partial fungal (ITS-1 region amplicon) dataset revealed that the distribution of the taxonomies varied based on both the host plant and the soil type /\u0026nbsp;location combinations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile the soil is the main reservoir for the rhizosphere population, root exudation selectively recruits community members, known as the rhizosphere effect [38]. This was evident for the different crops, although to variable extents, affected mostly by the location / soil type combination. The\u0026nbsp;focus on a single crop type, sugar beet, highlighted that enrichment of certain members of the soil bacteria microbiota occurred, especially for one soil type / location (silty clay loam from Hertfordshire) over others, including the clays.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough this study was not designed to compare locations, an influence of location on the taxonomic diversity was identified by examining the pan- and core-taxonomies, for a single crop (sugar beet). Soil type was also a key driver of diversity, with three out of the four soil types not sharing any of the major taxa. Only the clay soils shared common taxa. This could be due to the soil architecture of clay [39], which has the effect of enriching particular genera compared to other physical structures. Highlighting such diversity within the community composition, even in the presence of a significant rhizosphere effect, raises important questions about function. This is best determined from direct sequencing to identify functional groups coupled to functional phenotype analyses.\u003c/p\u003e\n\u003cp\u003eInvestigation of the fungal composition revealed functional groups of interest. The focus was on ASVs associated with the \u003cem\u003eFusarium\u003c/em\u003e species in spring wheat because there are several species within this genus that are phytopathogens, with wheat as a susceptible host [40]. Although ASVs derived from the fungal ITS region are insufficient to define sequence variants to the species level, they allow differential comparisons. There was a strong dependency on soil type / location for the total \u003cem\u003eFusarium\u003c/em\u003e population detected as well as on individual ASVs. This could be related to the presence of non-pathogenic species that occur in \u0026lsquo;suppressive\u0026rsquo; soils and out-compete other, phytopathogenic species. Notably, bacterial ASVs related to the \u003cem\u003eOhtaekwangia\u003c/em\u003e genus were identified as one of the common taxa in the pan-genome assessment, and have previously been identified to potentially suppresses disease-causing \u003cem\u003eFusarium\u003c/em\u003e species [41].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSample metadata, sufficient to allow data re-use is often lacking in datasets deposited in nucleotide repositories, like ENA. This means the re-use of the datasets relies on information held within publications that can be difficult to find and access. To ensure the UKCMCB project data meets FAIR principles (Findable, Accessible, Interoperable, and Reusable) our data catalogue aims to make the link between sample metadata and genomic data easier. The AgMicrobiomeBase catalogue also provides access to our complete bioinformatics workflow code for the amplicon dataset analysis via GitHub. Whilst only the amplicon data has been described here, the complete UKCMCB project has produced multiple additional data types (metagenomic sequence, bacterial isolate sequences and phenotypic data) that will be described elsewhere. As the AgMicrobiomeBase catalogue expands, incorporating further crop and soil datasets, the key to meeting the FAIR principle of reusability will be the sharing of bioinformatics workflows [42] and the development of and adherence to data standards to allow data integration [43]. These aspects are still in development for microbiome research but need to be made a priority: in order to realise the promise of microbiome data re-use and integration with additional data including functional phenotypes, biochemistry and pathology.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, the UKCMCB provides a comprehensive resource relating to crop microbiomes, grown in different UK agricultural soils. The case studies illustrate how the dataset can be queried to answer different biological questions. Since the sequence-based data can be accessed with the extensive soil and crop metadata, they also lend themselves to comparative investigations against other datasets. A meta-analysis on cereal crops found that there was a gap in knowledge and associated datasets for barley in comparison to wheat, rice and maize, [4] and our resource helps to fill this gap. The dataset also serves as a baseline to investigate perturbations, whether from abiotic stress that may arise through climatic changes (drought, heat, salt stress), or biotic stress from infection with pathogens. Equally, since the metadata includes the plant genotype, it has relevance for investigation of alternative races or for breeding strategies, as crop genotypes are known to drive community composition [44]. The metadata within the resource also describes agronomic practices such as the heritage of the soil / location site, in turn allowing comparison of management strategies, whether for application of amendments, pesticides or tillage strategies (e.g. in [45]. \u0026nbsp;Finally, as this dataset focused on the rhizosphere microbiomes, it lends itself to comparison with microbiota derived from other plant tissues, e.g. endophytic compartments or the phylosphere [46].\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eENA: European Nucleotide Archive\u003c/p\u003e\n\u003cp\u003eITS: Internal Transcribed Spacer\u003c/p\u003e\n\u003cp\u003erRNA: ribosomal RNA\u003c/p\u003e\n\u003cp\u003eSynComs: synthetic communities\u003c/p\u003e\n\u003cp\u003eUKCMCB: United Kingdom Crop Microbiome Cryobank\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate: not applicable\u003c/p\u003e\n\u003cp\u003eConsent for publication:\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials: All sequence data for the UKCMCB project has been submitted to the European Nucleotide Archive (ENA) under study accession PRJEB58189. All UKCMCB project code is available via GitHub [14].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting interests: none declared\u003c/p\u003e\n\u003cp\u003eFunding: This work was funded by BBSRC awards: BB/T019492/1, BB/T019484/1, BB/T019700/1, BB/T019808/1\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions:\u0026nbsp;This paper was conceived and written by Nicola Holden and Susan Jones. Payton To Yau and Nicola Holden constructed the amplicon libraries. Payton To Yau developed R code and shell scripts for the UKCMCB project and conducted the data analysis for the case studies. Payton To Yau, Rodrigo G Taketani, J Miguel Bonnin, Helen Stewart, Catriona MA Thompson, Ian M Clark, Tim H Mauchline, Jacob G Malone, Matthew J Ryan, Susan Jones and Nicola Holden contributed to the development of the UKCMCB resource, from soil sample collection to nucleic acid extraction, analysis and resource development. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eOur thanks go to Tim Khouri, Paul Cutler and Simon Hill (CABI) for data visualisation of AgmicrobiomeBase using the PowerBI platform. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSoldan R, Fusi M, Cardinale M, Daffonchio D, Preston GM: The effect of plant domestication on host control of the microbiota. \u003cem\u003eCommunications Biology \u003c/em\u003e2021, 4(1):1-9; https://doi.org/10.1038/s42003-021-02467-6.\u003c/li\u003e\n\u003cli\u003eRay P, Lakshmanan V, Labb\u0026eacute; JL, Craven KD: Microbe to Microbiome: A Paradigm Shift in the Application of Microorganisms for Sustainable Agriculture. \u003cem\u003eFront Microbiol \u003c/em\u003e2020, 11; https://doi.org/10.3389/fmicb.2020.622926.\u003c/li\u003e\n\u003cli\u003eKavamura VN, Mendes R, Bargaz A, Mauchline TH: Defining the wheat microbiome: Towards microbiome-facilitated crop production. \u003cem\u003eComputational and Structural Biotechnology Journal \u003c/em\u003e2021, 19:1200-1213; https://doi.org/10.1016/j.csbj.2021.01.045.\u003c/li\u003e\n\u003cli\u003eMichl K, Berg G, Cernava T: The microbiome of cereal plants: The current state of knowledge and the potential for future applications. \u003cem\u003eEnvironmental Microbiome \u003c/em\u003e2023, 18(1):28; https://doi.org/10.1186/s40793-023-00484-y.\u003c/li\u003e\n\u003cli\u003eCastellano-Hinojosa A, Strauss SL, Gonz\u0026aacute;lez-L\u0026oacute;pez J, Bedmar EJ: Changes in the diversity and predicted functional composition of the bulk and rhizosphere soil bacterial microbiomes of tomato and common bean after inorganic N-fertilization. \u003cem\u003eRhizosphere \u003c/em\u003e2021, 18:100362; https://doi.org/10.1016/j.rhisph.2021.100362.\u003c/li\u003e\n\u003cli\u003eNewton AC, Hawes C, Hackett CA: Adaptation of winter barley cultivars to inversion and non-Inversion tillage for yield and \u003cem\u003eRhynchosporium \u003c/em\u003esymptoms. \u003cem\u003eAgronomy \u003c/em\u003e2021, 11(1):30; https://doi.org/10.3390/agronomy11010030.\u003c/li\u003e\n\u003cli\u003eRichardson L, Allen B, Baldi G, Beracochea M, Bileschi Maxwell L, Burdett T, Burgin J, Caballero-P\u0026eacute;rez J, Cochrane G, Colwell Lucy J\u003cem\u003e et al\u003c/em\u003e: MGnify: the microbiome sequence data analysis resource in 2023. \u003cem\u003eNucl Acids Res \u003c/em\u003e2023, 51(D1):D753-D759; https://doi.org/10.1093/nar/gkac1080.\u003c/li\u003e\n\u003cli\u003eMatias Rodrigues JF, Schmidt TSB, Tackmann J, von Mering C: MAPseq: highly efficient k-mer search with confidence estimates, for rRNA sequence analysis. \u003cem\u003eBioinformatics \u003c/em\u003e2017, 33(23):3808-3810; https://doi.org/10.1093/bioinformatics/btx517.\u003c/li\u003e\n\u003cli\u003eThompson LR, Sanders JG, McDonald D, Amir A, Ladau J, Locey KJ, Prill RJ, Tripathi A, Gibbons SM, Ackermann G\u003cem\u003e et al\u003c/em\u003e: A communal catalogue reveals Earth\u0026rsquo;s multiscale microbial diversity. \u003cem\u003eNature \u003c/em\u003e2017, 551(7681):457-463; https://doi.org/10.1038/nature24621.\u003c/li\u003e\n\u003cli\u003eRyan MJ, Mauchline TH, Malone JG, Jones S, Thompson CMA, Bonnin JM, Stewart H, Yau PTO, Taketani RG, Clark IM\u003cem\u003e et al\u003c/em\u003e: The UK Crop Microbiome Cryobank: a utility and model for supporting Phytobiomes research. \u003cem\u003eCABI Agriculture and Bioscience \u003c/em\u003e2023, 4(1):53; https://doi.org/10.1186/s43170-023-00190-2.\u003c/li\u003e\n\u003cli\u003eSchmidt P-A, B\u0026aacute;lint M, Greshake B, Bandow C, R\u0026ouml;mbke J, Schmitt I: Illumina metabarcoding of a soil fungal community. \u003cem\u003eSoil Biol Biochem \u003c/em\u003e2013, 65:128-132; https://doi.org/10.1016/j.soilbio.2013.05.014.\u003c/li\u003e\n\u003cli\u003eWhite TJ, Bruns T, Lee S, Taylor J: 38 - Amplification and direct sequencing of fungal ribsomal RNA genes for phylogenetics. 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The UKCMCB links genomic datasets and associated soil metadata with a cryobank collection of samples, for six economically significant crops: fava bean (\u003cem\u003eVicia faba)\u003c/em\u003e, oil seed rape (\u003cem\u003eBrassica napus\u003c/em\u003e), spring barley\u003cem\u003e \u003c/em\u003e(\u003cem\u003eHordeum vulgare\u003c/em\u003e), spring oats (\u003cem\u003eAvena sativa)\u003c/em\u003e, spring wheat\u003cem\u003e \u003c/em\u003e(\u003cem\u003eTriticum aestivum\u003c/em\u003e) and sugar beet\u003cem\u003e \u003c/em\u003e(\u003cem\u003eBeta vulgaris\u003c/em\u003e). The crops were grown in nine agricultural soils from the UK, representing three major soil texture classes. The UKCMCB is a scalable sequence-based data catalogue linked to a cryo-preserved sample collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The focus of this paper is the amplicon sequencing, associated bioinformatics workflows, and development of the project data catalogue. Short-read amplicon sequencing (16S rRNA gene and ITS region) was implemented to describe the rhizosphere and bulk soil communities, for the multiple crop-soil combinations. Three case studies illustrate how different biological questions in phytobiome research can be addressed using this data resource. The three case studies illustrate how to (1) determine the impact of soil texture and location on microbiome composition, (2) determine a core microbiome for a single crop across different soil types, and (3) analyse a single genus, \u003cem\u003eFusarium\u003c/em\u003e within a single crop microbiome. The UKCMCB data catalogue AgMicroBiomeBase (https://agmicrobiomebase.org/data) links the sequence-based data with soil metadata and to cryopreserved samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe UKCMCB provides baseline data and resources to enable researchers to assess the impact of soil type, location and crop type variables on crop soil microbiomes. The resource can be used to address biological questions and cross-study comparisons. Development of the UKCMCB will continue with the addition of metagenome and bacterial isolate genomic sequence data and has the potential to integrate additional data types including microbial phenotypes and synthetic microbial communities.\u003c/p\u003e","manuscriptTitle":"Introducing the UK Crop Microbiome Cryobank resource: metabarcoding methods and case studies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 13:01:49","doi":"10.21203/rs.3.rs-6445717/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-09T16:57:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-06T07:42:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-02T19:29:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"167662457473860497572028688559392078247","date":"2025-05-15T14:37:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68614842334415188107109341919224179208","date":"2025-05-15T09:33:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-14T04:52:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-12T07:13:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-26T02:12:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Microbiome","date":"2025-04-14T11:42:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sigs","sideBox":"Learn more about [Environmental Microbiome](https://environmentalmicrobiome.biomedcentral.com)","snPcode":"40793","submissionUrl":"https://submission.nature.com/new-submission/40793/3","title":"Environmental Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6ea4c9a2-f563-445c-a4c2-7457956bd50e","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-25T16:37:18+00:00","versionOfRecord":{"articleIdentity":"rs-6445717","link":"https://doi.org/10.1186/s40793-025-00768-5","journal":{"identity":"environmental-microbiome","isVorOnly":false,"title":"Environmental Microbiome"},"publishedOn":"2025-08-21 16:29:40","publishedOnDateReadable":"August 21st, 2025"},"versionCreatedAt":"2025-05-16 13:01:49","video":"","vorDoi":"10.1186/s40793-025-00768-5","vorDoiUrl":"https://doi.org/10.1186/s40793-025-00768-5","workflowStages":[]},"version":"v1","identity":"rs-6445717","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6445717","identity":"rs-6445717","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}