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\ No newline at end of file
diff --git a/README.md b/README.md
index 69d75ba6924f979aae96d6890669ca7b7c84f138..af0db93e622b146273bf01f10ea77827301b20a3 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,19 @@
+# Global expression patterns of *R*-genes in tomato and potato
+
+## Original Publication
+
+von Dahlen JK, Schulz K, Nicolai J and Rose LE (2023) Global expression patterns of *R*-genes in tomato and potato. Front. Plant Sci. 14:1216795. doi: 10.3389/fpls.2023.1216795
+
+## Abstract
+
The study explores how resistance genes (R-genes) in tomato and potato plants respond to pathogen infection by analyzing the expression of 940 R-genes across 315 transcriptome libraries. Results show that most R-genes are expressed at low levels, with a subset showing moderate to high expression across various conditions, regardless of infection. This subset includes NRCs (NLR required for cell death). About 10% of R-genes were differentially expressed during infection, with both up- and down-regulation patterns. Tissue-specific expression emerged as a key factor influencing R-gene activity. The findings challenge the belief that R-gene expression is primarily induced by pathogen attack, suggesting instead that a core set of R-genes is constitutively active, providing plants with a constant state of readiness for defense.
-
\ No newline at end of file
+<img src=./_publication/vonDahlen.png width=50%>
+
+## License
+
+Copyright © 2023 von Dahlen, Schulz, Nicolai and Rose. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
+
+## Data availability
+
+Publicly available datasets were analyzed in this study. A total of 315 transcriptome datasets of tomato (Zouari et al., 2014; Du et al., 2015; Barad et al., 2017; Sarkar et al., 2017; Sugimura and Saito, 2017; Xue et al., 2017; Yang et al., 2017; Zheng et al., 2017; Chen et al., 2018; Shukla et al., 2018; Fawke et al., 2019; Pesti et al., 2019; Wang et al., 2019b) and potato (Goyer et al., 2015; Zuluaga et al., 2015; Dees et al., 2016; Gao and Bradeen, 2016; Kochetov et al., 2017; Levy et al., 2017; Li et al., 2017; Lysøe et al., 2017; Hao et al., 2018; Kumar et al., 2018) were obtained from the Sequence Read Archive (Figure S1).
\ No newline at end of file
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+# Supplementary material
+
+The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1216795/full#supplementary-material
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diff --git a/assays/AnalysisOfDifferencesInExpression/protocols/AnalysingDifferencesInExpression.md b/assays/AnalysisOfDifferencesInExpression/protocols/AnalysingDifferencesInExpression.md
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+## Analysis of differences in expression
+
+To identify the factors associated with differences in expression of *R*-genes across transcriptomes, we performed an ANOSIM in Primer 7.0.13 (PRIMER-e; Figure S2). ANOSIM is a non-parametric statistical test similar to ANOVA. The starting point of the analysis is a pairwise dissimilarity matrix. In our case, the dissimilarity matrix was computed as follows: First the TPM values for each gene within each transcriptome were LOG (x+1) transformed. On the basis of these transformed TPM values, the dissimilarity in gene expression patterns between transcriptomes were calculated based on Euclidean distances. Ranking was applied to the distance matrix. The two libraries from potato (SRR6511453 and ERR791944) with exceptionally low expression of the entire *R*-gene repertoire were excluded in these analyses.
+
+To determine if gene expression is more similar within groups than between groups (for example when groups are defined by infection status or tissue type) the R test statistic value was calculated. The R values can range from -1 to 1, with larger values corresponding to greater differences between groups. Statistical significance is calculated through permutation of the group labels and recalculation of the R value for each replicate. In our case, 999 permutations were generated. The following factors were evaluated: BioProject, tissue type, type of treatment, specific treatment organism, life cycle of the organism, type/kingdom of the organism, susceptible vs. resistant cultivar, relative sequencing depth, paired- or single-end sequencing and days post infection. The ANOSIM analysis was also applied to the differential gene expression data (see below).
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diff --git a/assays/CalculationOfTranscriptAbundance/README.md b/assays/CalculationOfTranscriptAbundance/README.md
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diff --git a/assays/CalculationOfTranscriptAbundance/protocols/CalculationOfTranscriptAbundanceUsingKallisto.md b/assays/CalculationOfTranscriptAbundance/protocols/CalculationOfTranscriptAbundanceUsingKallisto.md
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+## Calculation of transcript abundance using Kallisto
+
+The program Kallisto (v.0.46.0) was used to estimate the relative expression of genes in tomato and potato (Bray et al., 2016). As a first step, the raw sequence reads were compared to the transcript sequences. This step in Kallisto is designated as the pseudoalignment step. To improve the quality of the pseudoalignment, low-quality reads and adapters were removed from the transcriptomes using Trimmomatic under the following settings: seed mismatch = 2; palindrome clip threshold = 30; simple clip threshold = 10; LEADING = 3; TRAILING = 3; SLIDINGWINDOW= 4:15; MINLEN =36 (Bolger et al., 2014; Figure S2). Subsequent quality controls were performed using FastQC (Andrews, 2010). As Kallisto requires information on fragment length for single-end sequenced transcriptomes, the fragment length denoted by the authors was used. If this information was not available, the recommended fragment length of the reported RNA isolation kit was used. The standard deviation was set to ±17.5 bp. Kallisto indices (used for generating the pseudoalignments) were based on the tomato ITAG4.0 and the potato PGSC_DM_v4.03 genome releases. *R*-genes missing from the current genome releases were manually added to the list of transcripts (indices in Kallisto). Transcript abundance was calculated as transcripts per million (TPM; Wagner et al., 2012). We chose to use TPM since it normalizes the transcript abundance for gene length and library size, making TPM values comparable across experiments. Genes for which the TPM values were less than 1 were treated as “off†and for these genes, TPM was set to zero. All scripts and settings used for these analyses are available at the following site: https://github.com/LauraERose/LargeScaleTranscriptomeAnalysis.
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diff --git a/assays/DifferentialExpressionAnalysis/protocols/DifferentialGeneExpressionAnalysis.md b/assays/DifferentialExpressionAnalysis/protocols/DifferentialGeneExpressionAnalysis.md
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+## Differential expression analysis
+
+Differentially expressed genes between microbe treatments and mock treatments were identified using Sleuth (Pimentel et al., 2017; Figure S2). The p-values were adjusted using the Benjamini-Hochberg correction (FDR ≤ 0.05; Benjamini and Hochberg, 1995). Since Sleuth relies on replicates within treatments, BioProjects without replicates were removed from this part of analysis. We evaluated differences between i) *R*-genes and all genes, ii) proportion of up- versus down-regulation iii) average absolute fold changes
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diff --git a/assays/GeneExpressionComparison/protocols/ComparisonOfGeneExpressionAcrossGeneSets.md b/assays/GeneExpressionComparison/protocols/ComparisonOfGeneExpressionAcrossGeneSets.md
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+## Comparison of gene expression across gene sets
+
+To compare the mean relative expression between *R*-genes (*R*-gene set size for tomato = 359 and for potato = 581) and non-*R*-genes (the rest of the genome) we generated 100 replicate datasets for each transcriptome by sampling the TPM values of 359 random genes from tomato and 581 random genes from potato. The average TPM of all expressed genes was calculated for each replicate dataset. To compare expression values, four reference genes were used: ubiquitin (*Solyc09g018730.4.1*) and actin4 (*Solyc04g011500.3.1*) for tomato (Müller et al., 2015) an importin subunit (*PGSC0003DMG400007289*) and elongation factor-1 (*PGSC0003DMG400023270*) for potato (Mariot et al., 2015; Tang et al., 2017). TPM values were tested for normality using the Anderson-Darling (>5000 data points; Thode, 2002) or Shapiro test (< 5000 data points; Shapiro and Wilk, 1965) and for equal variances using the test from Kendall (1938). Significant differences in expression were identified using a Mann-Whitney-U test (Mann and Whitney, 1947) for non-normally distributed data or a two-sample t-test for normally distributed data.
+
+We visualized *R*-gene expression using heatmaps created in R (v. 3.6.1). Genes were classified as off (if TPM < 1) or on (if TPM ≥1). In the heatmaps, libraries were clustered by similarity in patterns of expression between libraries and *R*-genes were sorted by the number of libraries expressing the corresponding gene. Correlations between 1) the total number of expressed *R*-genes and the total number of expressed genes, 2) the total number of expressed genes and the number of pseudo-aligned reads, as well as 3) the number of libraries in which an *R*-gene was expressed and the average level of expression of each *R*-gene were performed using a Spearman’s rank correlation test (Hollander et al., 2013).
+
+To investigate the extent to which expression patterns of *R*-genes were similar to wild close relatives of tomatoes, we evaluated additional transcriptomes of four wild tomato species: *S. peruvianum*, *S. chilense*, *S. ochranthum*, and *S. lycopersicoides* (Beddows et al., 2017). A subset of *R*-genes was further analyzed for their patterns of sequence variation within and between these wild species. Standard population genetic parameters including intraspecific variation (Ï€) and interspecific divergence (K) were estimated using DNaSP v. 5.10 (Librado and Rozas, 2009).
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diff --git a/assays/IdentificationOfClustersOfR-genes/protocols/IdentificationOfPhysicalClustersOfR-genes.md b/assays/IdentificationOfClustersOfR-genes/protocols/IdentificationOfPhysicalClustersOfR-genes.md
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+## Identification of physical clusters of *R*-genes
+
+*R*-genes were classified as belonging to a cluster when more than on *R*-gene was located in a region of 200 kilobases (kb) on a chromosome (Van de Weyer et al., 2019). Since Jupe et al. (2013) performed their analysis on an earlier release of the tomato genome assembly (ITAG2.4 release, Tomato Genome Consortium, 2012), the positions of all tomato *R*-genes had to be re-defined (Table S1). Positions of *RDCs* were verified using Blastn v2.6.0 (Altschul et al., 1990; Camacho et al., 2009) against the tomato (ITAG4.0; Hosmani et al., 2019) and potato genomes (PGSC_DM_v4.03; Potato Genome Sequencing Consortium, 2011; Table S1). All *R*-genes without defined chromosomal positions (39 genes in tomato) were classified as *R*-genes with unknown clustering.
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diff --git a/assays/miRNATargetingPrediction/README.md b/assays/miRNATargetingPrediction/README.md
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+version https://git-lfs.github.com/spec/v1
+oid sha256:1b3e8ce55dee3e7721be46020ed3bb2032606cce029f8425e385521492b17924
+size 71531
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+version https://git-lfs.github.com/spec/v1
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+size 15896
diff --git a/assays/miRNATargetingPrediction/isa.assay.xlsx b/assays/miRNATargetingPrediction/isa.assay.xlsx
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diff --git a/assays/miRNATargetingPrediction/protocols/TargetingPredictionOfmiRNA.md b/assays/miRNATargetingPrediction/protocols/TargetingPredictionOfmiRNA.md
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--- /dev/null
+++ b/assays/miRNATargetingPrediction/protocols/TargetingPredictionOfmiRNA.md
@@ -0,0 +1,3 @@
+## miRNA targeting prediction
+
+To predict potential regulation of *R*-genes by the miR482-superfamily (de Vries et al., 2015), we used psRNATarget (release 2017; Dai et al., 2018). We used the coding sequence (CDS) of our *R*-genes as the target library. To ensure a low rate of false-positives, the maximum expectation was set to ≤3, since higher expectation values represent less likely mRNA/miRNA interactions. We evaluated the likelihood of an *R*-gene being targeted by the miR482 superfamily and whether the *R*-gene encoded a full length NBS-LRR and or belonged to a *R*-gene cluster using a chi-square test (Greenwood and Nikulin, 1996).
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diff --git a/assays/qRT-PCR/README.md b/assays/qRT-PCR/README.md
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diff --git a/assays/qRT-PCR/dataset/S9.xlsx b/assays/qRT-PCR/dataset/S9.xlsx
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+version https://git-lfs.github.com/spec/v1
+oid sha256:d183e4ccf24161fada60b6ae247c585f37cd14e77dac2255449f1d55fe1a5aa9
+size 20380
diff --git a/assays/qRT-PCR/isa.assay.xlsx b/assays/qRT-PCR/isa.assay.xlsx
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diff --git a/assays/qRT-PCR/protocols/VerificationOfGeneExpressionUsingqRT-PCR.md b/assays/qRT-PCR/protocols/VerificationOfGeneExpressionUsingqRT-PCR.md
new file mode 100644
index 0000000000000000000000000000000000000000..9623e18c96886b3a2ca59f2e14780448f90cee25
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+++ b/assays/qRT-PCR/protocols/VerificationOfGeneExpressionUsingqRT-PCR.md
@@ -0,0 +1,3 @@
+## Verification of gene expression using qRT-PCR
+
+To verify the overall consistency of our estimated TPM values in this metaanalysis, we performed qRT-PCR on twelve NBS-LRR-genes and three reference genes (de Vries et al., 2018). We evaluated the expression of these fifteen genes over six time points on the Moneymaker cultivar inoculated with *Phytophthora infestans (P. infestans)* isolate IPO-C. Three replicates were studied at each sampling time point and treatment type. Additional details of this experiment are reported in de Vries et al., 2018. The Bioproject L (PRJNA487149) from Fawke et al., 2019 is the most similar in design to our 2018 study, since that project sampled transcriptomes from leaves of the tomato cultivar ‘MicroTom’ inoculated with *P. infestans* isolate 88069. We evaluated the consistency between the average TPM of these 15 genes from Fawke et al. with our estimated Cq values at 72 hours post infection, the single overlapping timepoint between both data sets.
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diff --git a/isa.investigation.xlsx b/isa.investigation.xlsx
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diff --git a/studies/R-geneDataSet/README.md b/studies/R-geneDataSet/README.md
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diff --git a/studies/R-geneDataSet/isa.study.xlsx b/studies/R-geneDataSet/isa.study.xlsx
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diff --git a/studies/R-geneDataSet/protocols/R-geneData.md b/studies/R-geneDataSet/protocols/R-geneData.md
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index 0000000000000000000000000000000000000000..33cb4a2e826c54b7fcac432a0f4d1e7fe447b4a7
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@@ -0,0 +1,3 @@
+## *R*-gene Data Set
+
+The lists of the *R*-gene repertoires of *S. lycopersicum* and *S. tuberosum* were retrieved from Jupe et al. (2013). *R*-genes were classified as “full-length†NBS-LRRs if they contained both NBS and LRR domains as identified using InterPro (Mitchell et al., 2019). A slightly modified pipeline as described by Jupe et al. (2013) was used to verify their novel *R*-genes (Figure S2). These novel *R*-genes were designated by the authors as *R gene discovery consortium (RDC)* genes. Using AUGUSTUS (version 3.3.1), a gene-prediction tool developed by Stanke et al. (2008), we analyzed these *RDC* genes for coding regions and searched for NBS and LRR domains using InterPro. *RDCs* were classified as true *R*-genes if they possessed a coding region and an NBS-LRR domain. Otherwise they were excluded from further analysis. In cases in which multiple splice variants were identified, the longest splice variant was analyzed. The well-established *R*-genes *Pto* (Martin et al., 1993) and *EDS1* (Hu et al., 2005) from tomato were included in the dataset. In total, the expression patterns of 359 *R*-genes of tomato and 581 *R*-genes of potato were analyzed.
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diff --git a/studies/R-geneDataSet/resources/.gitkeep b/studies/R-geneDataSet/resources/.gitkeep
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diff --git a/studies/TranscriptomeDataSets/README.md b/studies/TranscriptomeDataSets/README.md
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+++ b/studies/TranscriptomeDataSets/README.md
@@ -0,0 +1,15 @@
+# Transcriptome Data Sets
+
+
+
+**Figure S1: Composition of data set**
+
+This study is based on 315 transcriptomes (133 from tomato (a), 182 from potato (b)) and includes
+seven plant pathogen domains (bacteria, fungi, oomycetes, nematodes, viroids, viruses, insects). The
+pathogens include common pests of tomato and potato as well as mycorrhizal forming organisms and
+potential biocontrol agents (green cross/grey text). The pathogens belong to biotrophic (green circle),
+necrotrophic (brown circle), hemibiotrophic (green-brown circle) as well as unknown (grey circle) or
+none categorical (pink circle) pathogens. We included transcriptomes from root, fruit, stem and leaves
+of resistant (shield) as well as susceptible cultivars (crossed-out shield) that were generated at 12
+different time points (0 dpi until the end of the life cycle of the plant). Cultivars ranged from 13 (a) to
+15 (b) per host.
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diff --git a/studies/TranscriptomeDataSets/protocols/DataSetResources.md b/studies/TranscriptomeDataSets/protocols/DataSetResources.md
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+++ b/studies/TranscriptomeDataSets/protocols/DataSetResources.md
@@ -0,0 +1,4 @@
+## Transcriptome data sets
+
+A total of 315 transcriptome datasets of tomato (Zouari et al., 2014; Du et al., 2015; Barad et al., 2017; Sarkar et al., 2017; Sugimura and Saito, 2017; Xue et al., 2017; Yang et al., 2017; Zheng et al., 2017;
+Chen et al., 2018; Shukla et al., 2018; Fawke et al., 2019; Pesti et al., 2019; Wang et al., 2019b) and potato (Goyer et al., 2015; Zuluaga et al., 2015; Dees et al., 2016; Gao and Bradeen, 2016; Kochetov et al., 2017; Levy et al., 2017; Li et al., 2017; Lysøe et al., 2017; Hao et al., 2018; Kumar et al., 2018) were obtained from the Sequence Read Archive (Figure S1). These studies included treatments with potentially beneficial organisms (arbuscular mycorrhizal fungi (AMF) and biocontrol agents) as well as detrimental organisms (pathogenic bacteria, nematodes, fungi, viruses, viroids, insects and oomycetes). Only studies with at least one mock treatment were included. The collected tissues included roots, stems, leaves, fruits and tubers. The time points of sampling after infection range from 0 days post-infection (dpi) up to 42 dpi or until the end of the host’s life cycle (Figure S1). Approximately 20% of all tomato and potato cultivars were denoted as resistant to the applied pathogens.
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