Figure S1, Venn diagram showing the number of differentially expressed genes identified by two versions of Cuffdiff2. Figure S2, The effects of biological replicates on the differential expression analysis for Cuffdiff v2.0.2. Figure S3, The detected fold changes of all the differentially expressed genes identified by three tools were compared and shown, including DESeq vs. edgeR (top panel), DESeq vs. Cuffdiff2 (middle panel) and edgeR vs. Cuffdiff2 (bottom panel). File S1, Analysis pipelines, methods and examples of commands for differential expression analysis, subsampling fastq files and generating SAM/BAM files based on simulated count values. File S2, The raw count values for genes with high fold changes were picked up by edgeR but not by DESeq. Genes with high fold changes (the absolute value of log2 fold changes larger than 2) identified as DEGs by edgeR but not by DESeq are listed in the file. The gene ID, the log2 fold changes (logFC) and FDR from DESeq, the logFC and FDR from edgeR, the raw count values for the four replicates of sample K (K1–K4) and sample N (N1–N4) are shown in each of the columns. Table S1, Numbers of reads for the human hbr and uhr samples from the MAQC dataset. Table S2, Numbers of reads for the mouse neurosphere samples for treatment groups of K and N (the K_N dataset). Table S3, The number of reads for each individual sample of the LCL3 dataset. Table S4, The definition for TP, FP, TN, FN, TPR and FPR. Table S5, The false positive rate for Cuffdiff2, DESeq and edgeR based on the LCL1 dataset. (ZIP)

Rvisdiff is an R/Bioconductor package that generates an interactive interface for the interpretation of differential expression results. It creates a local web page that enables the exploration of statistical analysis results through the generation of auto-analytical visualizations. Users can explore the differential expression results and the source expression data interactively in the same view. As input, the package supports the results of popular differential expression packages such as DESeq2, edgeR, and limma. As output, the package generates a local HTML page that can be easily viewed in a web browser. Rvisdiff is freely available at https://bioconductor.org/packages/Rvisdiff/.

This data set provides data files and R code to accompany the article Differential methylation analysis of reduced representation bisulfite sequencing experiments using edgeR published by F1000Research.

The data consists of Reduced Representation BS-seq methylation profiles of epithelial populations from the mouse mammary gland, with n=2 biological replicates for each of three cell populations.

RNA-seq expression profiles of luminal and basal mammary epithelial populations are also provided.

The R code undertakes an differential methylation analysis of the BS-seq profiles and demonstrates a strong negative correlation between the differential methylation and differential expression results.

Invasive species provide an opportune system to investigate how populations respond to new or changing environments. While the impacts of invasive species increase annually, many gaps in our understanding of how these species invade, adapt, and thrive in the areas they are introduced to remain. Using the perennial forb Gypsophila paniculata as a study system, we aimed to investigate how invasive species respond to different environments. Baby’s breath (Gypsophila paniculata) was introduced to North America in the late 1800’s and has since spread throughout the northwestern United States and western Canada. We used an RNA-seq approach to explore how molecular processes may be contributing to the success of invasive G. paniculata populations that are thought to share similar genetic backgrounds across distinct habitats. Transcription profiles were constructed for root, stem, and leaf tissue from seedlings collected from a sand dune ecosystem in Petoskey, MI (PSMI) and a sagebrush ecosystem in Chelan, WA (CHWA). Using these data we assessed differential gene expression between the two populations and identified SNPs within differentially expressed genes. We identified 1,146 transcripts that were differentially expressed across all tissues between the two populations. GO processes enriched by genes displaying higher expression in PSMI were associated with increased nutrient starvation, while enriched processes in CHWA were associated with abiotic stress. Only 7.4% of the differentially expressed genes across all three tissues contained SNPs differing in allele frequencies of at least 0.5 between the populations. In addition, common garden studies found the two populations differed in germination rate and seedling emergence success, but not in above- and below-ground tissue allocation. Our results suggest that the success of invasive G. paniculata across these two environments is likely the result of plasticity in molecular processes responding to different environmental conditions, although some genetic divergence may also be contributing to these differences.

Methods RNA Extraction. We collected 16 G. paniculata seedlings from CHWA (June 8, 2018) and 15 seedlings from PSMI (June 1, 2018). We then dissected seedlings into three tissue types (root, stem, and leaf), placed tissue in RNAlater™ (Thermo Fisher Scientific, Waltham, MA), and flash-froze them in an ethanol and dry ice bath. We extracted total RNA from frozen tissue using a standard TRIzol® (Thermo Fisher Scientific) extraction protocol (https://assets.thermofisher.com/TFS-Assets/LSG/manuals/trizol_reagent.pdf). We resuspended the extracted RNA pellet in DNase/RNase free water. The samples were then treated with DNase to remove any residual DNA using a DNA-Free Kit (Invitrogen, Carlsbad, CA). We assessed RNA quality with a Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA) and NanoDrop™ 2000 (Thermo Fisher Scientific). RNA Integrity Number (RIN) values for individuals used in this study ranged from 6.1-8.3. However, because both chloroplast and mitochondrial rRNA can artificially deflate RIN values in plant leaf tissuewe deemed these values to be sufficient for further analysis based upon visualization of the 18S and 28S fragment peaks (see Babu & Gassmann, 2016). This resulted in high quality total RNA from 10 PSMI leaf, 10 PSMI stem, 10 PSMI root, 10 CHWA leaf, 9 CHWA stem, and 10 CHWA root samples.

cDNA Library Construction and Sequencing. Prior to sequencing, all samples were treated with a Ribo-Zero rRNA Removal Kit (Illumina, San Diego, CA). cDNA libraries were constructed using the Collibri Stranded Library Prep Kit (Thermo Fisher Scientific) before being sequenced on a NovaSeq 6000 (Illumina) using S1 and S2 flow cells. Sequencing was performed using a 2 x 100 bp paired-end read format and produced approximately 60 million reads per sample, with 94% of reads having a Q-score >30.

Transcriptome Assembly. Prior to transcriptome assembly, read quality was assessed using FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Adapters and bases with a quality score less than 20 were first removed from the raw reads using Trim Galore (https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/). Next, rRNAs were identified using SortMeRNA (mean rRNA percent content of 5.31%) (Kopylova, Noé, & Touzet, 2012). A reference transcriptome was then assembled de novo using non-rRNA reads from all samples and Trinity v2.8.2 (Grabherr et al. 2011; Haas et al. 2013), with a normalized max read coverage of 100, a minimum k-mer coverage of 10, and k-mer size set to 32. The assembled transcriptome was annotated using Trinotate v3.1.1. Trinotate was given open reading frames (ORFs) predicted from TransDecoder and transcript homology (blastx and blastp) to the manually curated UniProt database (Bryant et al., 2017). The final assembly consisted of 223,810 putative genes and 474,313 putative transcripts (N50 = 3,121) from the 59 samples.

Differential Expression. To quantify transcript expression, reads were mapped back to the assembly using bowtie and quantified using the RSEM method as implemented in Trinity. Counts were generated for genes and transcripts. We then tested for differential gene expression using edgeR v3.22.5 in R v3.5.2 (Robinson, McCarthy, & Smyth, 2010; R Development Core Team, 2017). First, however, the count data was filtered and only transcripts with greater than 10 counts in at least 10 samples were included. Following filtering, 111,042 genes (49.61%) and 188,108 transcripts (39.66%) remained. Considering tissue type, 127,591 transcripts remained in the data from 20 root samples (26.90%), 125,261 transcripts remained in the 19 stem tissue samples (26.41%), and 112,499 transcripts remained in the 20 leaf tissue samples (23.72%). For differential expression testing, the data were stratified by tissue and filtered transcripts were then fit to the negative binomial (NB) model and tested using the quasi-likelihood F test with TMM (trimmed mean of M values) normalization. To be considered significantly differentially expressed, transcripts needed to have an adjusted p-value (BH method) below 0.05 and a log2 fold change greater than 2. For transcripts that were differentially expressed, we identified Gene Ontology (GO) biological processes that were either over- or under-represented using the PANTHER classification system v14.1, where transcripts were assessed against the Arabidopsis thaliana database (http://pantherdb.org/webservices/go/overrep.jsp). In addition, for those transcripts that were differentially expressed across all three tissues, we converted the UniProt IDs of the transcripts to GO biological process IDs using the online database bioDBnet (https://biodbnet-abcc.ncifcrf.gov/db/db2db.php), and used the metacoder package v0.3.3 (Foster, Sharpton, Grünwald, 2017) in R v3.6.0 to construct heat trees to visualize the relationship of our differentially expressed transcripts across GO biological process hierarchies.

Single Nucleotide Polymorphism (SNP) Variant Calling. We used the HaplotypeCaller tool from GATK4 to identify potential SNPs that were present in transcripts that were differentially expressed between populations (McKenna et al., 2010; DePristo et al. 2011). The bowtie mapped files were used to jointly genotype all 59 samples simultaneously with a minimum base quality and mapping quality of 30. Variant data was visualized using the vcfR package v1.8.0 (Knaus & Grünwald 2017). We identified variants associated with non-synonymous SNPs, synonymous SNPs, 5’ and 3’ UTR SNPs, 5’ and 3’ UTR indels, frame-shift and in-frame indels, premature or changes in stop codons and changes in start codons, and calculated population diversity estimates for all SNP types. The effect prediction was done using custom scripts (which can be found in the Dryad repository) and the Transdecoder predicted annotation in conjunction with the base change. We set a hard filter for the SNPs so that only those with QD scores > 2, MQ scores > 50, SOR scores < 3, and Read Post Rank Sums between -5 and 3 passed. We then calculated the allele frequencies for each SNP within PSMI and CHWA. For the subsequent evaluation, we focused on SNPs that had potential functional effects (i.e., they were not listed as ‘synonymous’ or ‘unclassified’), were in transcripts differentially expressed between PSMI and CHWA across all three tissues, and that exhibited differences in SNP allele frequencies between the populations by at least 0.5. We used the R package metacoder v0.3.3 to visualize the GO biological process hierarchies associated with transcripts containing these SNPs.

Germination Trial. On August 11, 2018 we returned to our sample sites in CHWA and PSMI and collected seeds from 20 plants per location. This date was chosen because Rice, Martínez-Oquendo, & McNair (2019) previously determined that this collection time can yield over 90% seed germination for G. paniculata collected from Empire, MI. To collect seeds, we manually broke seed pods off and placed them inside paper envelopes in bags half-filled with silica beads. We stored bags in the dark at 20 to 23˚C until the germination trial began one month later, We counted one hundred seeds from twenty plants per population and placed them in a petri dish lined with filter paper (n = 2,000 seeds per population). We established a control dish using 100 seeds from the ‘Early Snowball’ commercial cultivar (G. paniculata) sold by W. Atlee Burpee & Co in 2018, known to have germination percentages in excess of 90%. Incubators had a 12:12h dark:light photoperiod and growth chamber conditions were set at 20˚C with 114 μmol m-2 s-1 photosynthetically active radiation from fluorescent light bulbs. Each day we randomized petri dish locations within the incubator to avoid bias in temperature or light regimes. We conducted this study for fourteen days,

RNA sequencing data from the prefrontal cortex (PFC) and hippocampus (HIPP) of male (12 weeks old) hemizyguous CAG^HERV-Wenv mice ( C57BL6/J;129P2/Ola-Hprt mice; n = 3) relative to wild-type ( n = 3) littermates. Total RNA was extracted from prefrontal and hippocampal samples using the SPLIT RNA extraction kit (Lexogen, Austria) following the manufacturer’s recommendations and was sent to the Functional Genomics Center in Zurich (FGCZ) for quality control and RNA sequencing. The quality of the isolated RNA was determined with a Fragment Analyzer (Agilent, Santa Clara, California, USA). Only those samples with a 260 nm/280 nm ratio between 1.8–2.1, a 28S/18S ratio within 1.5–2, and RIN (>8) values qualified for a Poly-A enrichment strategy in order to generate the sequencing libraries applying the TruSeq mRNA Stranded Library Prep Kit (Illumina, Inc, California, USA). After Poly-A selection using Oligo-dT beads the mRNA was reverse-transcribed into cDNA. The cDNA was fragmented, end-repaired and poly-adenylated before ligation of TruSeq UD Indices (IDT, Coralville, Iowa, USA). The quality and quantity of the amplified sequencing libraries were validated using a Fragment Analyzer SS NGS Fragment Kit (1–6000 bp) (Agilent, Waldbronn, Germany). The equimolar pool of the samples was spiked into a NovaSeq6000 run targeting ~15M reads per sample on a S1 FlowCell (Novaseq S1 Reagent Kit, 100 cycles, Illumina, Inc, California, USA). Reads were quality-checked with FastQC. Sequencing adapters were removed with Trimmomatic and aligned to the reference genome and transcriptome of Mus Musculus (GENCODE, GRCm38,p5) with STAR v2.7.3. Distribution of the reads across genomic isoform expression was quantified using the R package GenomicRanges from Bioconductor Version 3.10. Minimum mapping quality, as well as minimum feature overlaps, was set to 10. Multi-overlaps were allowed. Differentially expressed genes (DEGs) were identified using the R package edgeR from Bioconductor Version 3.10, using a generalized linear model (glm) regression, a quasi-likelihood (QL) differential expression test and the trimmed means of M-values (TMM) normalization.

While quantitative PCR (qPCR) is widely recognized as being among the most accurate methods for quantifying gene expression, it is highly dependent on the use of reliable, stably expressed reference genes. With the increased availability of high-throughput methods for measuring gene expression, whole-transcriptome approaches may be increasingly utilized for reference gene selection and validation. In this study, RNA-seq was used to identify a set of novel qPCR reference genes and evaluate a panel of traditional "housekeeping" reference genes in two species of the evolutionary model plant genus Mimulus. More broadly, the methods proposed in this study can be used to harness the power of transcriptomes to identify appropriate reference genes for qPCR in any study organism, including emerging and nonmodel systems. We find that RNA-seq accurately estimates gene expression means in comparison to qPCR, and that expression means are robust to moderate environmental and genetic variation. However, measures of expression variability were only in agreement with qPCR for samples obtained from a shared environment. This result, along with transcriptome-wide comparisons, suggests that environmental changes have greater impacts on expression variability than on expression means. We discuss how this issue can be addressed through experimental design, and suggest that the ever-expanding pool of published transcriptomes represents a rich and low-cost resource for developing better reference genes for qPCR.

Each zipped folder contains results files from reanalysis of public data in our publication, "mirrorCheck: an R package facilitating informed use of DESeq2’s lfcShrink() function for differential gene expression analysis of clinical samples" (see also the Collection description).These files were produced by rendering the Quarto documents provided in the supplementary data with the publication (one per dataset). The Quarto codes for the 3 main analyses (COVID, BRCA and Cell line datasets) performed differential gene expression (DGE) analysis using both DESeq2 with lfcShrink() via our R package mirrorCheck, and also edgeR. Each zipped folder here contains 2 folders, one for each DGE analysis. Since DESeq2 was run on data without prior data cleaning, with prefiltering or after Surrogate Variable Analysis, the 'mirrorCheck output' folders themselves contain 3 sub-folders titled 'DESeq_noclean', 'DESeq_prefilt' and 'DESeq_sva". The COVID dataset also has a folder with results from Gene Set Enrichment Analysis. Finally, the fourth folder contains results from a tutorial/vignette-style supplementary file using the Bioconductor "parathyroidSE" dataset. This analysis only utilised DESeq2, with both data cleaning methods and testing two different design formulae, resulting in 5 sub-folders in the zipped folder.

Differentially expressed genes in the synovial tissue from FVIII-KO mice after hemarthrosis and FVIII treatment. Synovial tissue was harvested at baseline, day 3 and day 14 post-injury. RNA was purified and analyzed by RNA sequencing using an Illumina NextSeq500 platform (75 bp; single‐end). The R BioConductor packages tximport, edgeR, and limma were used to read estimate counts from RSEM, trimmed mean of M-values (TMM) normalization was applied, and the limma-voom method was used for differential expression analyses (criteria: adjusted p-value

Supplementary File 1 Transcriptome assembly of A. fragariae in fasta format. Note: all 147,621 sequences, including splicing isoforms, are included in this file. Supplementary File 2 Annotation table of A. fragariae transcriptome. Note: Annotation was performed on the longest isoforms only (48,541 sequences). Information in this file includes sequence names, closest match to known genes in the nr database (description), lowest e-value to known genes, number of gene ontology (GO) terms assigned, enzyme code and names, detailed GO information, and InterPro annotation. Supplementary File 3. Gene expression data of A. fragariae under desiccated and control conditions. Note: Genes with low expression were removed. Fragments per kilobase of transcript per million mapped reads (FPKM) of each condition was averaged across three biological replicates. In the fold change column, positive and negative values indicate genes were up-regulated and down-regulated in the desiccated condition, respectively; Differential expression was determined in R package edgeR using exact tests (Robinson et al., 2010). Differentially expressed transcriptional factors and their GO annotations were presented in the second tab. Supplementary File 4. Gene ontology (GO) terms enriched in the desiccation treatment (“desiccation” tab) and control (“control” tab). Nominal p-value, false discovery rate (FDR), and normalized enrichment score (NES) were determined in GSEA (Subramanian et al., 2005). Abbreviations in the category column: BP, biological process; CC, cellular component; MF, molecular function. The foliar nematode (Aphelenchoides fragariae) is a quarantined pest that infects a broad range of herbaceous and woody plants. Previous work has demonstrated its remarkable ability to survive rapid and extreme desiccation, although the specific mechanisms underlying its anhydrobiotic response have not been characterized. We used RNA sequencing and de novo transcriptome assembly to compare patterns of gene expression between hydrated and 24-hr desiccated nematodes. Two thousand eighty-three and 953 genes were significantly up- and downregulated, respectively, in desiccated nematodes. Of the one hundred annotated genes with the largest positive fold-changes, more than one third encoded putative detoxification-related proteins. Genes encoding enzymes of Phase I and Phase II detoxification systems were among the most strongly upregulated in the transcriptome, including 35 cytochrome p450s, 23 short chain dehydrogenase/reductases, five glutathione-S-transferases, and 22 UDP-glucuronosyltransferases. Genes encoding heat shock proteins, unfolded protein response enzymes, and intrinsically-disordered proteins were also upregulated. Anhydrobiosis in A. fragariae appears to involve both (1) strategies to minimize protein misfolding and aggregation and (2) wholesale induction of the cellular detoxification machinery. These processes may be controlled in part through the activity of forkhead transcription factors similar to C. elegans’ daf-16, a number of which were differentially expressed under desiccation. We used RNA sequencing and de novo transcriptome assembly to compare patterns of gene expression between hydrated and 24-hr desiccated foliar nematodes. We performed differetinal gene expression analysis using R library edgeR. Additionaly, we conducted Gene Set Enrichment Analysis to examine which gene sets that were asssgiend to gene ontology terms were enriched in desiccation treatment.

One of the fundamental aspects of genomic research is the identification of differentially expressed (DE) genes between two conditions. In the past decade, numerous DE analysis tools have been developed, employing various normalization methods and statistical modelling approaches. In this article, we introduce DElite, an R package that leverages the capabilities of four state-of-the-art DE tools: edgeR, limma, DESeq2, and dearseq. DElite returns the outputs of the four tools with a single command line, thus providing a simplified way for non-expert users to perform DE analysis. Furthermore, DElite provides a statistically combined output of the four tools, and in vitro validations support the improved performance of these combination approaches for the detection of DE genes in small datasets. Finally, DElite offers comprehensive and well-documented plots and tables at each stage of the analysis, thus facilitating result interpretation. Although DElite has been designed with the intention of being accessible to users without extensive expertise in bioinformatics or statistics, the underlying code is open source and structured in such a way that it can be customized by advanced users to meet their specific requirements. DElite is freely available for download from https://gitlab.com/soc-fogg-cro-aviano/DElite.

1. TruSeq mRNA stranded analysis of murine Fh1fl/fl kidney epithelial cells were treated with vehicle control (DMSO) or 20 micromolar fumarate hydratase inhibitor (FHIN-1) for 24 h. Three biological replicates per condition. README file - README_1_TruSeq_mRNA_stranded_FHIN1
2. TruSeq mRNA stranded analysis of murine Fh1fl/fl kidney epithelial cells treated with vehicle control (DMSO) or Atpenin A5 (AA5) for 24 h. Three biological replicates per condition. README file - README_2_TruSeq_mRNA_stranded_AA5
3. Label-free proteomic analysis of a murine Fh1fl/fl kidney epithelial cells treated with vehicle control (DMSO) or 20 micromolar FHIN-1 for 24 h. README file - README_3_Label-free_proteomics_FHIN1.txt
4. Label-free proteomic analysis of a murine Fh1fl/fl kidney epithelial cells treated with vehicle control (DMSO) and 500 micromolar thenoyltrifluoroacetone (TTFA). README file - README_4_Label-free_proteomics_TTFA.txt

Maintenance of the genome is essential for cell survival, and impairment of the DNA damage response is associated with multiple pathologies including cancer and neurological abnormalities. DNA-PKcs is a DNA repair protein and a core component of the classical nonhomologous end-joining pathway, but it also has roles in modulating gene expression and thus, the overall cellular response to DNA damage. Using cells producing either wild-type (WT) or kinase-inactive (KR) DNA-PKcs, we assessed global alterations in gene expression in the absence or presence of DNA damage. We evaluated differential gene expression in untreated cells and observed differences in genes associated with cellular adhesion, cell cycle regulation, and inflammation-related pathways. Following exposure to etoposide, we compared how KR versus WT cells responded transcriptionally to DNA damage. Downregulated genes were mostly involved in protein, sugar, and nucleic acid biosynthesis pathways in both genotypes, but enriched biological pathways were divergent, again with KR cells manifesting a more robust inflammatory response compared to WT cells. To determine what major transcriptional regulators are controlling the differences in gene expression noted, we used pathway analysis and found that many master regulators of histone modifications, proinflammatory pathways, cell cycle regulation, Wnt/β-catenin signaling, and cellular development and differentiation were impacted by DNA-PKcs status. Finally, we have used qPCR to validate selected genes among the differentially regulated pathways to validate RNA sequence data. Overall, our results indicate that DNA-PKcs, in a kinase-dependent fashion, decreases proinflammatory signaling following genotoxic insult. As multiple DNA-PK kinase inhibitors are in clinical trials as cancer therapeutics utilized in combination with DNA-damaging agents, understanding the transcriptional response when DNA-PKcs cannot phosphorylate downstream targets will inform the overall patient response to combined treatment. Methods The dataset contains ensemble ID, gene name if applicable, comparisons between genotypes with and without drug (etoposide) treatment. Within each comparison are log fold change, average expression, p value, and adjusted p value. Cells culture: V3-derived Chinese Hamster Ovary (CHO) cell lines were kindly provided by Dr. Katherine Meek, complemented with either human wild-type (WT), null (Null) or kinase inactivate DNA-PKcs, by K3753R mutation (KR), as previously reported (21). All cell lines were cultured in alpha-MEM (LifeTechnologies, Waltham, MA) supplemented with 10% FBS (MilliporeSigma, St. Louis, MO), 1% penicillin/streptomycin (LifeTechnologies), 200 µg/ml G418 (LifeTechnologies) and 10 µg/ml puromycin (Santa Cruz Biotechnology, Santa Cruz, CA) at 37ºC with 5% CO2and 100% humidity. All standard laboratory chemicals were purchased from MilliporeSigma unless otherwise indicated. Cells were cultured on 100 mm dishes overnight and next day, after washing, trizol reagent (MilliporeSigma) was applied to cells and the cell lysate was collected. Total RNA isolation was according to the manufacturer’s instructions. Total RNA was dissolved in nuclease-free water followed by spectrophotometric analysis of the RNA quantity; RNA was subjected to electrophoresis on an agarose gel to permit evaluation of both RNA quality and assure no DNA contamination was observed. Differentially expressed gene analysis: The low-quality raw reads (fastq format) were filtered based on Q30 and GC content, then the Illumina adaptors were trimmed of reads for a minimum read length of 36 bases using Trimmomatic v0.34 (Bolger, Lohse, and Usadel 2014). The index of the Chinese hamster reference genome (CHOK1GS_HDv1) was built using HISAT2 v2.1.0 (Kim, Langmead, and Salzberg 2015). Hisat2 v2.2.1 tool was utilized to align the reads to the genome sequences in FASTA format and output aligned reads in binary alignment map (BAM) format were translated into the transcriptomes of each sample using stringtie v2.0 tool which uses a novel network flow algorithm as well as an optional de novo assembly step to assemble and quantitate full-length transcripts representing multiple splice variants for each gene locus (Pertea et al. 2015). The stringtie outputs (GTF files) were merged to create a single master transcriptome GTF with exact same naming and numbering scheme across all transcripts. The feature counts tool implemented under subread v2.0 was utilized to quantify transcripts assembled by stringtie mapped to each gene (Liao, Smyth, and Shi 2014). Eventually, the differentially expressed (DE) gene profiles were statistically analyzed through edgeR and limma R libraries (Ritchie et al. 2015; Robinson, McCarthy, and Smyth 2010).

See material & methods section differential expression analysis of RNAseq data of DOI: 10.7554/eLife.61630

We used the R package edgeR (Robinson, McCarthy, & Smyth, 2010) to compare RNA sequencing profiles between FLCNPOS and FLCNNEG replicates, as well as between TP53POS and TP53NEG. This involved reading in the gene-level counts, computing library size normalizing factors using the trimmed-mean of M-values (TMM) method and then fitting a model to estimate the group effect. Obtained p-values were corrected for multiple testing using the Benjamini-Hochberg false discovery rate (FDR) step-up procedure (Benjamini & Hochberg, 1995).

Introduction

This database includes the script used for the differential gene expression (DGE) analysis linked with the paper "Joubert Syndrome-derived induced pluripotent stem cells show altered neuronal differentiation in vitro." published on "Cell and tissue research".

In this paper we analyzed the differentiation steps of JS patient-derived iPSCs towards cerebellar granule cells. We demonstrated that JS patient-derived iPSCs have an impaired expression of the genes of neuronal differentiation, analyzed through real time pcr and immunofluorescence in four different time points from D0, starting day, till D31 end of the differentiation protocol. This data were confirmed by transcriptomic analysis showing an impaired progression along the differentiation time course of the JS-patients derived IPSCs respect to control cells. We also analyzed cilia length and numbers in both patients and control cells, demonstrating notable ciliary defects in all differentiating JS patient-derived iPSCs compared to controls. Taken together our results shows that patient-derived iPSCs are an accessible and relevant in vitro model to analyze cellular phenotypes connected to the presence of JS gene mutations in a neuronal context.

Methods

Gene expression data from an RNA-seq experiment (Illumina platform) were initially processed with command line interface to align reads on the reference genomes and to count reads falling on coding regions (gene expression counts), with the pipeline outlined in the Materials and Methods section of the paper. The metadata contained sample names, mutated genes defining experimental conditions and time points for each sample.

This script, written with the R programming language, performed a comprehensive analysis of the processed next-generation sequencing data (read counts). To ensure the reproducibility of our analysis, we initialized the R environment by setting a specific seed for random number generation.

Several R libraries were used in our analysis, including edgeR, RColorBrewer, DESeq2, ggplot2, openxlsx, readxl, and ggrepel. These libraries provided essential functions for statistical analysis, data visualization, and manipulation.

The workflow contained in this script consisted of several steps: we generated count-per-million (CPM) values and filtered out genes with low expression. Reads Per Kilobase Million (RPKM) values were then calculated to account for gene length and normalize the data, to ensure compatibility of expression levels across experiments and genes. Differential gene expression was performed using the DESeq2 package, through the creation of a DESeqDataSet object, the estimation of size factors and dispersions, and the fitting of models. Differentially expressed genes between different conditions and time points were visualized with a volcano and heatmap plot.

Results

Results of the transcriptomic analysis show differences in the expression of genes relating to the central nervous system development already at D8, in particular cerebellum markers (LMX1A, OLIG3) being expressed only in controls cells. At D24, differences in gene expression became more evident, underlining for control samples the expression of genes specific to the terminal phases of cerebellar differentiation (ASTN2, CNTN1, WNT3, PLXNA2, LMX1B) and the expression of genes encoding proteins of neuronal functionality (GRIA4, SYT8, GRIK1, P2RX2, PCP4, GRIK2). In contrast, expression of these genes was significantly lower in JS lines, which showed early differentiation markers still predominantly expressed (ITGB1, CXCL12, HIF1A, EN2, GBX2, BMPR1B, CUL2, RPL37, RPL39, RPL35, PSMD2, and PSMD3). Taken together, these data support the observations, obtained with the other assays in our paper, that JS-iPSC lines show an impaired progression along the differentiation time course and a reduced ability to reach the maturation state seen in controls.

r scripts written for differential analysis and regression analysis to investigate the molecular basis of paralytic shellfish toxins load in the oyster crassostrea gigas exposed to the toxigenic alga alexandrium minutum. differentially expressed genes were analyzed with r (version 3.2.3, gentry et al. (2004) genome biol 5:r80) using the packages deseq2 (version 1.10.0; love et al. (2014) genome biol 15:1-21) and edger (version 3.12.0, robinson et al. (2010) bioinformatics 26:139-140). elastic-net regression was run with r using the package glmnet (version 2.0-2, friedman et al. (2010) j stat softw 33:1-22).

Mimulus Annotation and Expression AnalysesGene expression data (raw counts and RPKM) for all transcriptome libraries analyzed in the manuscript. In addition, genome annotation and orthogroups for syntenic orthologs that were further analyzed, with full descriptions, are in the zipped file.Mimulus_TPC_Dryad.zipMimulus_luteus.faaFasta file of M. luteus genome assemblyMimulus_luteus_complete_w_single_exons_standard_renamed_genes.gffGFF genome annotation file of Mimulus luteus. This GFF file provides gene annotation for the M. luteus genome assembly published in this paper.Mimulus_guttatus_complete_w_single_exons_standard_renamed_genes.gffGFF genome annotation file of Mimulus guttatus. This GFF file provides gene annotation for the M. guttatus genome v2.0 on available on Phytzome.AlignmentsThis folder contains coding sequence alignments and single copy ortholog alignments used to conduct phylogenetic analyses in this paper.alignments.tar

This dataset includes metadata associated with NCBI BioProject PRJNA377729 \Impacts of Evolution on the Response of Phytoplankton Populations to Rising CO2\ PRJNA377729: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA377729. The alga Heterosigma akashiwo was grown at CO2 levels from about 200 to 1000 ppm and then the DNA and RNA were sequenced. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv acquisition_description=Uni-algal, non-axenic cultures of Heterosigma akashiwo (CCMP2393) were grown in L1 medium (without silicate) made with a Long Island Sound seawater base collected from Avery Point, CT, USA (salinity 32) at 18\u00b0C with a 14:10 (light:dark) cycle with an irradiance of approximately 100 \u00b5mol m-2 s-1 . Cells were acclimated in exponential growth phase to different carbonate chemistries in 1.2 L of L1 media in 2.5-L polycarbonate bottles. To control the carbonate chemistry of the water, the headspace of each bottle was purged continuously with a custom gas mixture of ~21% oxygen, ~79% nitrogen and either 200, 400, 600, 800 or 1000 ppmv CO2 (TechAir, NY).

At the point of harvest, 150 mL (~6 x 106 cells) were filtered on to 5 \u00b5m pore size, 25 mm polycarbonate filter and flash frozen in liquid nitrogen. Genetic material from samples was extracted with the RNeasy Mini kit (Qiagen, Valencia, CA) and DNA was removed on-column using the RNase-free DNase Set (Qiagen), yielding total RNA. Total RNA extracts of the triplicate cultures were quantified on a 2100 Bioanalyzer (Agilent, Santa Clara, CA). Libraries were prepared using poly-A pull down with the TruSeq Stranded mRNA Library Prep kit (Illumina, San Diego, CA). Library preparation, barcoding, and sequencing from each library was performed by the JP Sulzberger Columbia University Genome Center (New York, NY).

Sequence reads were de-multiplexed and trimmed to remove sequencing barcodes. Reads were aligned using Bowtie2 (Langmead and Salzberg 2012) to the MMETSP consensus contigs for Heterosigma akashiwo CCMP2393 ("%5C%22https://omictools.com/marine-microbial-eukaryotic-%0Atranscriptome-sequencing-project-tool%5C%22">https://omictools.com /marine-microbial-eukaryotic-transcriptome- sequenci...).

Significant differences between physiological parameters by CO2 treatment were assessed with analysis of variance (ANOVA) and Tukey\u2019s honestly significant differences test (aov and TukeyHSD, stats, R). Differential expression of genes in any CO2 treatment compared to modern was determined using the general linear model (GLM) exact test (edgeR, R). Briefly, the read counts were normalized by trimmed mean of M-values (TMM) using the function calcNormFactors, tagwise dispersions were calculated with the function estimateGLMTagwiseDisp, a GLM was fit using glmFit, and log2 fold change (logFC) for each treatment was calculated relative to average expression at modern CO2. P-values from likelihood ratio tests were corrected for multiple testing using the false discovery method (fdr). awards_0_award_nid=55197 awards_0_award_number=OCE-1314336 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward?AWD_ID=1314336 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 cdm_data_type=Other comment=Hak_acclim The harmful alga Heterosigma akashiwo (CCMP2393) grown under a range of CO2 concentrations from 200-1000 ppm. PI's: S. Dyhrman (LDEO), J. Morris (U Alabama) version: 2018-10-11 See also: https://www.ncbi.nlm.nih.gov/bioproject/377729 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.747872.1 geospatial_vertical_positive=down geospatial_vertical_units=m infoUrl=https://www.bco-dmo.org/dataset/747872 institution=BCO-DMO instruments_0_acronym=Automated Sequencer instruments_0_dataset_instrument_description=Used to prepare the mRNA libraries. Samples were barcoded for multiplex sequencing and run on in a single lane by the Columbia University Genome Center (CUGC) (New York, NY). instruments_0_dataset_instrument_nid=747879 instruments_0_description=General term for a laboratory instrument used for deciphering the order of bases in a strand of DNA. Sanger sequencers detect fluorescence from different dyes that are used to identify the A, C, G, and T extension reactions. Contemporary or Pyrosequencer methods are based on detecting the activity of DNA polymerase (a DNA synthesizing enzyme) with another chemoluminescent enzyme. Essentially, the method allows sequencing of a single strand of DNA by synthesizing the complementary strand along it, one base pair at a time, and detecting which base was actually added at each step. instruments_0_instrument_name=Automated DNA Sequencer instruments_0_instrument_nid=649 instruments_0_supplied_name=Illumina Hi-seq 2500 paired-end sequencing (PE100) with TruSeq RNA sample Prep Kit (Illumina, San Diego, CA) keywords_vocabulary=GCMD Science Keywords metadata_source=https://www.bco-dmo.org/api/dataset/747872 param_mapping={'747872': {'collection_date': 'flag - time', 'depth': 'master - depth'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/747872/parameters people_0_affiliation=Lamont-Doherty Earth Observatory people_0_affiliation_acronym=LDEO people_0_person_name=Sonya T. Dyhrman people_0_person_nid=51101 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=University of Alabama at Birmingham people_1_affiliation_acronym=UA/Birmingham people_1_person_name=James Jeffrey Morris people_1_person_nid=51678 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=Lamont-Doherty Earth Observatory people_2_affiliation_acronym=LDEO people_2_person_name=Gwenn Hennon people_2_person_nid=546456 people_2_role=Scientist people_2_role_type=originator people_3_affiliation=Woods Hole Oceanographic Institution people_3_affiliation_acronym=WHOI BCO-DMO people_3_person_name=Nancy Copley people_3_person_nid=50396 people_3_role=BCO-DMO Data Manager people_3_role_type=related project=P-ExpEv projects_0_acronym=P-ExpEv projects_0_description=Note: This project is also affiliated with the NSF BEACON Center for the Study of Evolution in Action. Project Description from NSF Award: Human activities are driving up atmospheric carbon dioxide concentrations at an unprecedented rate, perturbing the ocean's carbonate buffering system, lowering oceanic pH, and changing the concentration and composition of dissolved inorganic carbon. Recent studies have shown that this ocean acidification has many short-term effects on phytoplankton, including changes in carbon fixation among others. These physiological changes could have profound effects on phytoplankton metabolism and community structure, with concomitant effects on Earth's carbon cycle and, hence, global climate. However, extrapolation of present understanding to the field are complicated by the possibility that natural populations might evolve in response to their changing environments, leading to different outcomes than those predicted from short-term studies. Indeed, evolution experiments demonstrate that microbes are often able to rapidly adapt to changes in the environment, and that beneficial mutations are capable of sweeping large populations on time scales relevant to predictions of environmental dynamics in the coming decades. This project addresses two major areas of uncertainty for phytoplankton populations with the following questions: 1) What adaptive mutations to elevated CO2 are easily accessible to extant species, how often do they arise, and how large are their effects on fitness? 2) How will physical and ecological interactions affect the expansion of those mutations into standing populations? This study will address these questions by coupling experimental evolution with computational modeling of ocean biogeochemical cycles. First, cultured unicellular phytoplankton, representative of major functional groups (e.g. cyanobacteria, diatoms, coccolithophores), will be evolved under simulated year 2100 CO2 concentrations. From these experiments, estimates will be made of a) the rate of beneficial mutations, b) the magnitude of fitness gains conferred by these mutations, and c) secondary phenotypes (i.e., trade-offs) associated with these mutations, assayed using both physiological and genetic approaches. Second, an existing numerical model of the global ocean system will be modified to a) simulate the effects of changing atmospheric CO2 concentrations on ocean chemistry, and b) allow the introduction of CO2-specific adaptive mutants into the extant populations of virtual phytoplankton. The model will be used to explore the ecological and biogeochemical impacts of beneficial mutations in realistic environmental situations (e.g. resource availability, predation, etc.). Initially, the model will be applied to idealized sensitivity studies; then, as experimental results become available, the implications of the specific beneficial mutations observed in our experiments will be explored. This interdisciplinary study will provide novel, transformative understanding of the extent to which evolutionary processes influence phytoplankton diversity, physiological ecology, and carbon cycling in the near-future ocean. One of many important outcomes will be the development and testing of nearly-neutral genetic markers useful for competition studies in major phytoplankton functional groups, which has applications well beyond the current proposal. projects_0_end_date=2017-05 projects_0_geolocation=Experiment housed in laboratories at Michigan State University projects_0_name=Impacts of Evolution on the Response of Phytoplankton Populations to Rising CO2 projects_0_project_nid=2276 projects_0_start_date=2013-06 sourceUrl=(local

The spatial organization of genes within plant genomes can drive evolution of specialized metabolic pathways. In this study we investigated the origin and subsequent evolution of a diterpenoid biosynthetic gene cluster (BGC) present throughout the Lamiaceae (mint) family. Terpenoids are important specialized metabolites in plants with diverse adaptive functions that enable environmental interactions, such as chemical defense. Based on core genes found in the BGCs of all species examined across the Lamiaceae, we predict a simplified version of this cluster evolved in an early Lamiaceae ancestor. The current composition of the extant BGCs highlights the dynamic nature of its evolution. We elucidate the terpene backbones made by the Callicarpa americana BGC enzymes, including miltiradiene and the novel terpene (+)-kaurene, and show oxidization activities of BGC cytochrome P450s. Our work reveals the fluid nature of BGC assembly and the importance of genome structure in contributing to the ...

B) Short BDNF stimulation changes the levels of only a few microRNAs, such as miR-16-5p and miR-551b-5p. Volcano plot of small RNA sequencing data from BDNF- or control-treated cortical neurons (6DIV) was based on differential expression analysis using the edgeR package. Individual points represent the average fold change obtained in three independent experiments for each miRNA, and plotted against obtained p-values. MiRNAs above the red dashed line are significant (p<0.05, t-test type 2, n=3).. List of tagged entities: multiple components, Mir16 (ncbigene:100313997), Mir551b (ncbigene:100314268), Bdnf (uniprot:P23363), gene expression assay (bao:BAO_0002785),small RNA-seq (obi:OBI_0002112)

The top 10 genes that were retrieved by EdgeR using the R-Cloud on EBI for the LNCaP dataset. Expression ratio is (stimulated/un-stimulated). Here we can see that the same set of identifiers used in the sscMap from the DESeq analysis would have been attained by EdgeR with the exception of ENSG00000155368 which was ranked 22nd in DESeq analysis. Table S2 contains the full list of differentially expressed genes returned by the EdgeR analysis.