DOI: 10.6084/m9.figshare.5657215
title: Comparative metatranscriptomics of wheat rhizosphere microbiomes in disease suppressive and non-suppressive soils for Rhizoctonia solani AG8.

description: A comparative metatranscriptomic approach was applied to assess the taxonomic and functional characteristics of the rhizosphere microbiome of wheat plants grown in adjacent fields which are suppressive and non-suppressive to the plant pathogen, R. solani AG8.<br>The paper on this research has now been published: Hayden, H. L., K. W. Savin, J. Wadeson, V. V. S. R. Gupta and P. M. Mele (2018). Comparative Metatranscriptomics of Wheat Rhizosphere Microbiomes in Disease Suppressive and Non-suppressive Soils for Rhizoctonia solani AG8. Frontiers in Microbiology 9(859).https://doi.org/10.3389/fmicb.2018.00859<br><br><br> The files in this set include:1. Metatranscriptome assembly - TrinityAllMRZincrRNA.fasta.gz2. Trinotate generated annotation file for the metatranscriptome assembly - Trinotate_report_wAnnotsOnly.txt<br>3. The count matrix generated in Trinity using RSEM for differential expression analysis. 4. BLASTN of NCBI nt database (E ≤ 1e<sup>-5</sup>) annotation file for differentially expressed genes as identified using EdgeR<br>- de.7219.Trinity.isoforms.srtXlogFC.nt.tophits.txt 5. BLASTX of NCBI nr database (E ≤ 1e<sup>-5</sup>) annotation file for differentially expressed genes as identified using EdgeR<br>- de.7219.Trinity.isoforms.srtXlogFC.nr.tophits.txt6. Testing of differential expression software EdgeR compared to DeSeq2 on the metatranscriptome counts matrix<br>-EdgeR&DeSeq2Analysis.docx <br> Trinity (version 2.2.0) was used for <i>de novo</i> metatranscriptome assembly. A set of 348,722,194 quality filtered reads from 12 rhizosphere libraries (six suppressive soil, six non-suppressive soil) was combined into a single reference transcriptome assembly. <br>The assembly was annotated using Trinotate. Transcripts were subjected to a BLASTX search (E ≤ 1e-5) of the protein database Swiss-Prot downloaded from UniProt (http://www.uniprot.org/). The software Transdecoder (http://transdecoder.github.io) was used to predict likely coding regions within transcripts, and resulting protein products were subjected to a BLASTP search (E ≤ 1e-5) against the Swiss-Prot database. To identify conserved protein domains we used Hmmer software (http://hmmer.org/) and PFam. KEGG, Gene Ontology (GO), and Eggnog annotations were retrieved from Swiss-Prot where transcripts could be assigned to these databases. All results for the reference assembly annotation were parsed by Trinotate, stored in a SQLite database and then reported as a tab-delimited summary file. Only contigs with annotations are reported in the attached file.<br> A count matrix produced in Trinity using RSEM was used for differential expression analysis in edgeR. For the assembly transcript abundance was filtered at a count per million (CPM) of 0.5 though expression was required in five of the six replicate samples. Normalisation to allow comparison between samples was performed for each count table in edgeR using TMM (trimmed mean of M-values). EdgeR settings included using the generalised linear model (GLM) likelihood ratio test with the contrast option (suppressive minus non-suppressive). Differentially expressed transcripts from the Trinity assembly were also subject to BLASTX and BLASTN searches of Genbank (E ≤ 1e<sup>-5</sup>).<br>Exploration was done to examine the numbers and types of of contigs identified as being differentially expressed when the software DESeq2 was used for differential gene expression analysis, in comparison to the dataset above produced by EdgeR analyses. <br>