DOI: 10.6084/m9.figshare.9730760.v1
title: Plant expansion drives bacteria and collembola communities under winter climate change in frost-affected tundra

description: This dataset supports the article "Plant expansion drives bacteria and collembola communities under winter climate change in frost-affected tundra" by E.J. Krab et al. (2019, Soil Biology and Biochemistry).The Jupyter notebook processes raw sequence data (ENA accession number PRJEB26326) into files used downstream for figures production.<br>The R script generates figures found throughout the article.<br>Extract both archives into a given folder. <br>- Running the /R/SBB_NSC_script.R from the /R/ folder will reproduce the figures shown in the article. <br>- Running the /Bioinformatics/SBB_NSC_script.ipynb will produce a /R_new/ folder from which the R script can be run to produce the same figures from scratch. The randomness introduced by the rarefaction step may induce small changes and invert NMDS axes direction in these new bacteria figures.<br><br><br>Folder structure and files description:- <b>R.zip</b> : <i>Figure-generation script and data</i>Archive content: <br>SBB_NSC_script.R - <i>Figure generation script</i>/Data/global_wunifrac2000_clean.txt - <i>bacteria weighted UniFrac distance matrix</i><br>/Data/global2000_alphadiv.txt - <i>bacteria alpha diversity metrics</i><br> /Data/global2000_ggtax_L2.txt - <i>bacteria taxa summary</i><br> /Data/Mapping_NSC.csv - <i>metadata for bacterial samples</i><br> /Data/NSC_count_and_met.csv - <i>collembola abundances and metadata</i><br> /Data/NSC_richness.csv - <i>collembola alpha diversity</i><br> /Data/snow_depth_20130326.csv - <i>snow depth measurements</i><br> /Data/snow_depth_20140403.csv - <i>snow depth measurements</i> /Data/snow_depth_20140429.csv - <i>snow depth measurements</i> /Data/wunifrac_deep.txt - <i>bacteria weighted UniFrac distance matrix (deep samples only)</i><br> /Data/wunifrac_surf.txt - <i>bacteria weighted UniFrac distance matrix (surface samples only)<br></i><br>- <b>Bioinformatics.zip</b> : <i>Raw sequence data processing</i><br>Archive content:SBB_NSC_script.ipynb - <i>Jupyter notebook with bioinformatics pipeline</i><b>EDIT</b><i>: There is currently an issue in assigning the correct sample ID to the reads, which is due to a change in the formatting of sequence headers after uploading to ENA. I will try fixing that issue as soon as possible.</i><br>Mapping_NSC.txt - <i>QIIME mapping file</i>/scripts/cleaner.awk - <i>custom awk script to clean file headers inherited from biom format</i><br> /scripts/Control_otu_removal.R - <i>custom executable R script to remove the 3 OTUs abundant in control samples</i><br> /scripts/gold.fa - <i>GOLD database (Haas et al. 2011 https://dx.doi.org/10.1101/gr.112730.110)</i><br> /scripts/relabel.awk - <i>custom awk script to relabel reads according to barcode label</i><br> /scripts/remove_primers.awk - c<i>ustom awk script to trim primers</i><br> /scripts/python_scipts/:<i> Robert C. Edgar's suite of python scripts for UPARSE: http://drive5.com/python/ </i><br> /scripts/rar/clean_tsv_from_biom_rarefied_tables.sh - <i>custom bash script to remove biom-inherited headers from rarefied tables (batch)</i><br> /scripts/rar/convert_table.sh - <i>custom bash script to convert biom tables into tsv (batch)</i><br> /scripts/rar/make_consensus_from_rarefactions.R -<i> custom executable R script to average multiple rarefactions into a consensus table</i><br>