I am working with Illumina MiSeq paired-end data with the ITS2 rDNA region using metabarcoding to identify fungal communities present in the soil and in the seedling roots of isolated and mixed oak (Quercus rugosa) and juniper (Juniperus deppeana) populations. To do so, I used the fungal specific primers ITS4ngsUNI and gITS7ngs, which were designed to act on a broad spectrum of fungi (Leho Tedersoo & Lindahl, 2016). The goal is to study belowground interactions between Juniperus and Quercus along a disturbance gradient in Central Mexico.
Because oak and juniper are plants with different mycorrhizal types, oak being known as an ectomycorrhizal plant and juniperus as an arbuscular-mycorrhizal plant, I expect the presence of one influences the other through belowground fungal interactions causing changes to plant growth and health and to the fungal communities abundance and diversity (Teste, 2019; Dean et al., 2015).
The general and bigger outlook is in the context of reforestation where these two plants grow together. I want to know if the fungal communities are affected by the vecinity of the other plant, and if this causes any changes that might be relevant to consider when improving reforestation practices and management strategies. Forest management might be improved through studying the biology of these interactions, therefore I consider that in the context of reforestation the study of fungal communities could be a key element for success (Bennett et al., 2017; Suz et al., 2017)
The samples I used for sequencing were root and soil samples. Samples were taken from 3 different sites: i) disturbed site with a population of J. deppeana, ii) mixed site where J. deppeana and Q. rugosa grow side by side (regeneration zone), and iii) a native site in forest dominated by Q. rugosa. Root samples come from collecting the root system of 6 seedlings in each site of J. deppeana (sites i and ii) and Q. rugosa (sites ii and iii), for a total of 24 root samples. For soil samples, we collected 3 soil cores for each plant species in each site, for a total of 12 soil samples.
MacOS Mojave version 10.14.4
Bash version: 3.2.57(1)-release
R version 3.6.2 (2019-12-12) -- "Dark and Stormy Night"
AMPtk requires a few other installations (indicated in 1_amptk_for_illumina.sh),make sure to carefully check their recommendations for installation of required dependencies on their website
In this repository you can find scripts, data, metadata and results to identify fungal communities using Illumina MiSeq paired-end data and to analyse fungal diversity and fungal community composition.
/binThe bin directory contains the bash, manual steps and R scripts to denoise Illumina MiSeq pair-end data, create the OTU table, assign taxonomy and trophic mode of the OTUs, filter the OTU table in R, and analyze alpha- and beta-diversity.
Here you can find the scripts for pre-processing Illumina MiSeq data with AMPtk (Palmer, J. et al. 2018), the manual steps for assigning OTU taxonomy and trophic mode, and converting metadata from .txt to .biom to import into R, as well as the numbered (in order of use) R scripts for filtering the OTU table and performing alpha- and beta- diversity analyses.
The steps to follow are listed in order:
Preprocessing and taxonomy and trophic mode assignment within AMPtk
1_amptk_for_illumina.sh: This is a bash script to denoise Illumina MiSeq pair-end data, create an OTU table, and assign taxonomy and fungal trophic guilds within AMPtk. AMPtk_pipeline.md: this is a text file that describes what the amptk_for_illumina.sh script does.
You might not need to, but if you need to edit the file you obtained from AMPtk (biom table) to add new taxonomic and trophic assigments:
2_Assign_trophic_mode.md: instructions for manually assigning taxonomy and trophic mode with UNITE database to OTUs that could not be identified beyond Order (or higher up in taxonomy rank) in previous step with AMPtk.
3_Convert_txt_to_biom.md: instructions for manually converting the edited 2_taxonomy.txt into 4_new_tax.biom, the file that you will be able to use in R with phyloseq package.
Use in number order to run alpha- and beta- diversity analyses as follows:
All the sequence data associated with this project are deposited in OSF while the final OTU table (.biom) is in data
/dataThe data directory contains the metadata with samples information and the data with the output file from the AMPtk pipeline that later acts as input file for R scripts. The data files were created and used in the numbered order.
1_taxonomy.biom : otu and taxonomy tables obtained from AMPtk pipeline before adding new taxonomy and fungal trophic mode information
2_taxonomy.txt : otu and taxonomy table in .txt format obtained from AMPtk pipeline that can be edited to add categories of taxonomy and trophic guild for all OTUs
3_new_tax_biom : this is a residual file, this is not to be used and should be ignored. I will delete this file soon.
4_new_tax.biom : otu and taxonomy tables that have been edited to contain new taxonomy and fungal trophic information and that are now converted back to .biom. This is the file to call initially in the first R script: 4_Filter_otu_table.R
amptk.mapping_file.csv: contains all sample data to be used as input for initial R script. The first 13 columns are all different identifiers of each sample.
ampt.mapping_file.txt: contains all sample data to be edited as input for initial R script. This is the same as the amptk.mappin_file.csv file but in .txt format for editing
metadata_soil_roots.xlsx: contains soil characterization for soil samples and mycorrhizal colonization information for all samples
/outputThe output directory contains the obtained results at this point and output images of how some manual steps look like. In the future this directory is expected to show the figures that will be used for the final publication.
Bermudez_Graphical_Abstract_MSA_2020.pdf: graphical abstract presented at the 2020 conference of the Mycological Society of America
Reporte_TallerBioinf.Rmd: R markdown file for the most important results obtained as of January/2021.
Reporte_TallerBioinf.html: (not working at the moment) html file for the most important results obtained as of January/2021.
Reporte_TallerBioinf.pdf: R-generated (knit package) PDF file for the most important results obtained as of January/2021. This is the most readable presentation of the research results so far.
excel_concatenate.png files: used to show manual steps in Script2
Bennett, J. A., Maherali, H., Reinhart, K. O., Lekberg, Y., Hart, M. M., & Klironomos, J. (2017). Plant-soil feedbacks and mycorrhizal type influence temperate forest population dynamics. Science, 355, 181–184. https://doi.org/10.1007/978-1-4020-2625-6_7
Dean, S. L., Warnock, D. D., Litvak, M. E., Porras-Alfaro, A., & Sinsabaugh, R. (2015). Root-associated fungal community response to drought-associated changes in vegetation community. Mycologia, 107(6), 1089–1104.
Palmer JM, Jusino MA, Banik MT, Lindner DL. 2018. Non-biological synthetic spike-in controls and the AMPtk software pipeline improve mycobiome data. PeerJ 6:e4925; DOI 10.7717/peerj.4925. https://peerj.com/articles/4925/
Suz, L. M., Kallow, S., Reed, K., Bidartondo, M. I., & Barsoum, N. (2017). Pine mycorrhizal communities in pure and mixed pine-oak forests: Abiotic environment trumps neighboring oak host effects. Forest Ecology and Management, 406(September), 370–380.
Tedersoo, Leho, & Lindahl, B. (2016). Fungal identification biases in microbiome projects. Environmental Microbiology Reports, 8(5), 774–779. https://doi.org/10.1111/1758-2229.12438
Teste, F. P., Jones, M. D., & Dickie, I. A. (2019). Dual‐mycorrhizal plants: their ecology and relevance. New Phytologist. https://doi.org/10.1111/nph.16190