Supplementary Dataset and Workflows for 'Soil origin affects gut microbiota and immune response in a wild rodent'

Open data API in a single place

Provided by Zenodo

Get early access to Supplementary Dataset and Workflows for 'Soil origin affects gut microbiota and immune response in a wild rodent' API!

Let us know and we will figure it out for you.

Dataset information

Country of origin
Updated
2025.12.03 00:00
Created
2025.11.19
Available languages
English
Keywords
Quality scoring

Dataset description

Dataset and Workflows for "Soil origin affects gut microbiota and immune response in a wild rodent" Description of the data and file structure This dataset includes code to preprocess short read data deposited in ENA SRA (PRJEB87226) using QIIME2 and subsequent data analysis using R with related packages, processed 16S and ITS2 datasets required for the R pipeline, and reproducible nf-core workflow reports for RNA sequencing and differential expression analysis. Please see project GitHub repo for updates on scripts. Files and variables File: readme.md Description: This file. File: FECES_16S_QIIME2.sh Description: Bank vole faecal 16S sequence data pre-processing pipeline using QIIME2. File: ITS2_QIIME2_vsearch.sh Description: Bank vole faecal ITS2 sequence data pre-processing pipeline using QIIME2. File: dna-sequences_16S.fasta Description: Denoised and decontaminated 16S V3-V4 DNA sequences produced by QIIME2 pipeline. File: dna-sequences_ITS2_c97.fasta Description: Denoised and decontaminated 16S V3-V4 DNA sequences clustered at 97% sequence identity produced by QIIME2 pipeline. File: feature-table_ITS2_c97.tsv Description: Decontaminated fungal ITS2 OTU abundance table produced by QIIME2 pipeline. File: feature-table_16S.tsv Description: Decontaminated bacterial 16S ASV abundance table produced by QIIME2 pipeline. File: rooted-tree_16S.nwk Description: Rooted phylogenetic tree for bacterial 16S data. File: metadata_feces-soil_16S.txt Description: Metadata file for 16S samples to be loaded in R. Variables SampleID: Sample ID Sample_ID: Sample alias Pup_ID: Subject ID of the experimental animal Sample_type: Sample type. Sampling_timepoint: Sampling time pre- (T1) or post (T2) treatment. is.neg: Identifier for negative control used in DECONTAM in QIIME2 pipeline. pup_birth_date: Birth date of subject. cage: Cage number of subject. sex: Sex of subject. Soil_location: City or national park from which experimental soil was collected. Soil_type: Main experimental factor - Control, Park or Urban. Pup_head: Subject head width at birth Pup_wt: Subject body weight at birth (g) End_head: Subject head width at end of experiment (mm) End_wt: Subject body weight at end of experiment (g) Testis_wt: Left testis weight (g) (males only) Sampling_date: Date of endpoint sampling Mother_head: Head width of the subject's mother at subject's weaning (mm). Mother_wt: Body weight of the subject's mother at subject's weaning (g). Mother_ID: ID of subject's mother. Used as random intercept in linear mixed models to control for genetic component. Mother_type: Information on subject's mother. FEAST: Subsetting factor for sourcetracking using FEAST. Env: Sample name in format required for FEAST source tracking. SourceSink: Source or Sink population identifier for FEAST. id: Sample identifier in format required for FEAST.   sample_accession: ENA sample accession ID File: metadata_ITS2_c97.tsv Description:Metadata file for ITS2 samples to be loaded in R. Variables As above. File: taxonomy_ITS2_c97.guilds.txt Description: Taxonomic classifications for ITS2 data, including FUNGuild annotations. File: taxonomy_16S.tsv Description: Taxonomic classifications for 16S data. File: firstdistances_bacteria_bray.tsv Description: Table of Bray-Curtis first distances of faecal bacteria pre- and post-treatment (=distance of the same subject between two time points). Data produced by QIIME2 pipeline, to be plotted in R. Variables SampleID: Sample ID SubjectID: Sample alias Distance: Distance Group: Experimental factor File: firstdistances_bacteria_jaccard.tsv Description: Table of Jaccard first distances of faecal bacteria pre- and post-treatment (=distance of the same subject between two time points). Data produced by QIIME2 pipeline, to be plotted in R. Variables As above File: firstdistances_fungi_bray.tsv Description: Table of Bray-Curtis first distances of faecal fungi pre- and post-treatment (=distance of the same subject between two time points). Data produced by QIIME2 pipeline, to be plotted in R. Variables As above File: firstdistances_fungi_jaccard.tsv Description: Jaccard first distances of faecal fungi pre- and post-treatment (=distance of the same subject between two time points). Data produced by QIIME2 pipeline, to be plotted in R. Variables As above File: FEAST_F_contributions.txt Description: Combined fungal source contributions from soil and food to faecal communities. Data produced in "faecal_metataxonomics_markdown.html" pipeline and externally edited in excel. Used as input for FEAST figure. Data also available in excel file Supplementary Data SI8. Variables Sample: Sample ID Soil: Source contribution of soil mixture to which subject was exposed. Food: Source contribution of food pellets. Unknown: Unknown source component. Group: Experimental factor Location: Origin of soil mixture File: faecal_metataxonomics_corestats.html Description: Microbial diversity analyses in R markdown. All required input files are published along this pipeline. File: faecal_metataxonomics_FEAST.html Description: Microbial source tracking analysis in R markdown. All required input files are published along this pipeline. File: faecal_metataxonomics_diffabund.html Description: Microbial differential abundance analysis in R markdown. All required input files are published along this pipeline. File: nfcore_RNAseq.html Description: Nextflow nf-core/RNAseq workflow report and settings for preprocessing of bank vole colonic mRNA reads. Includes extensive quality control and run settings. File: nfcore_differentialabundance.html Description: Nextflow nf-core/differentialabundance workflow report for exploratory data analysis and differential expression analysis. File: medoid_subsampling.R Description: R script for unsupervised subsampling for RNA extraction based on *a priori* knowledge of amplicon sequencing data.     File: soil2gut.network.R Description: Script for microbial co-occurrence network analysis. Code/software Amplicon sequencing data preprocessing: QIIME2 distribution qiime2-amplicon-2024.10. Preprocessing pipeline and settings are described in Materials & Methods and files "FECES_16S_QIIME2.bash" and "ITS2_QIIME2_vsearch.bash". RNAseq data preprocessing and differential expression analysis: Data was processed using nf-core/rnaseq v3.18.0 (doi: [10.5281/zenodo.1400710](https://doi.org/10.5281/zenodo.1400710)) of the nf-core collection of workflows ([Ewels *et al.*, 2020](https://doi.org/10.1038/s41587-020-0439-x)), utilising reproducible software environments from the Bioconda ([Grüning *et al.*, 2018](https://doi.org/10.1038/s41592-018-0046-7)) and Biocontainers ([da Veiga Leprevost *et al.*, 2017](https://doi.org/10.1093/bioinformatics/btx192)) projects. The pipeline was executed with Nextflow v24.10.3 ([Di Tommaso *et al.*, 2017](https://doi.org/10.1038/nbt.3820)). Full details available in "nfcore_RNAseq.html" and "nfcore_differentialabundance.html". Data analysis: R 4.3.1. Analysis pipeline is available in R markdown file "faecal_metataxonomics_markdown.html". All required input files are included in this release. Required packages are displayed in the markdown file.
European data infrastructure with broad catalog discovery, free evaluation access and production-grade API options.
190K+
indexed dataset pages
32
countries and EU institutions
2019
API-first since
Free API quota
for evaluation and prototypes
SLA
history and push on production APIs
FAQ

Questions before production use

Practical answers on evaluation, licensing, freshness, versioning and support.

api.store is built and operated by Apitalks s.r.o. Company details and a direct contact path are linked in the footer for vendor checks and procurement review.
Yes. Selected APIs include a free API quota, so your team can validate coverage, freshness, response shape and workflow fit before asking for a production plan.
Often yes, but usage rights depend on the source license and dataset. We surface source, license and update metadata where available, and can help review terms before a production integration.
Maintained APIs include update metadata where available. For production integrations, we can add history, monitoring and push updates so changes are easier to detect and act on.
Production APIs can add SLA, stable identifiers, versioning support, history, push updates and direct support around the data your product or AI workflow depends on.

Didn't find the API you need?

Let us know and we will figure it out for you.

European data discovery with free evaluation access and production-grade API options.

Copyright © 2026. Made by Apitalks