Arabidopsis RNA-Seq Differential Gene Expression profiling
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB19242 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR1811890_1.fastq.gz | Fastqsanger.gz | |||
| ERR1811890_2.fastq.gz | Fastqsanger.gz | |||
| ERR1811900_1.fastq.gz | Fastqsanger.gz | |||
| ERR1811900_2.fastq.gz | Fastqsanger.gz | |||
| ERR1811891_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 34 |
Similar Datasets
Project description:RNA-Seq data from 17 wild-type biological replicates of Arabidopsis thaliana used to explore read count measurements across replicates along with the False Discovery Rate of Differential Gene Expression tools. Although A. thaliana has a relatively small genome, its transcriptome is similar in scale and complexity to that of model mammal species and its genome is extensively annotated and the conclusions presented here provide useful guidance for work in other complex eukaryotes. The findings show that the negative binomial and log-normal distributions are both good choices as models for the cross-replicate variability of RNA-seq read counts. 6 of 9 DGE tools controlled their identification of false positives well even with only 3 replicates. Our results reinforce the conclusions reached by Schurch et. al. (2015 RNA) in yeast.
2019-02-08 | E-MTAB-5446 | biostudies-arrayexpress
Project description:Plants growing under high-density (HD) conditions experience increased competition for water, nutrients, and light, possibly leading to changes in size, biomass, morphology, and productivity. However, no research has focused on the relationship between whole-genome expression patterns and growth density. Here, we performed whole-genome RNA sequencing to examine the gene expression patterns in Arabidopsis grown under low and high densities. Of the 20,660 detected genes, the expression levels of 98 were enhanced and 107 were repressed under HD growth. Further analysis revealed that changes in density influenced metabolism- and stimulus-related genes the most. Furthermore, HD growth led to a shade avoidance phenotype, represented by upward growth and a reduction in rosette leaves. Moreover, a cluster of glutaredoxin genes, GRXS3, 4, 5, 7, and 8, were significantly down-regulated under high density, suggesting that high density affects plant growth mainly by nitrate limitation.
| S-EPMC5712407 | biostudies-literature
Project description:RNA-sequencing (RNA-seq) is a relatively new technology that lacks standardisation. RNA-seq can be used for Differential Gene Expression (DGE) analysis, however, no consensus exists as to which methodology ensures robust and reproducible results. Indeed, it is broadly acknowledged that DGE methods provide disparate results. Despite obstacles, RNA-seq assays are in advanced development for clinical use but further optimisation will be needed. Herein, five DGE models (DESeq2, voom + limma, edgeR, EBSeq, NOISeq) for gene-level detection were investigated for robustness to sequencing alterations using a controlled analysis of fixed count matrices. Two breast cancer datasets were analysed with full and reduced sample sizes. DGE model robustness was compared between filtering regimes and for different expression levels (high, low) using unbiased metrics. Test sensitivity estimated as relative False Discovery Rate (FDR), concordance between model outputs and comparisons of a 'population' of slopes of relative FDRs across different library sizes, generated using linear regressions, were examined. Patterns of relative DGE model robustness proved dataset-agnostic and reliable for drawing conclusions when sample sizes were sufficiently large. Overall, the non-parametric method NOISeq was the most robust followed by edgeR, voom, EBSeq and DESeq2. Our rigorous appraisal provides information for method selection for molecular diagnostics. Metrics may prove useful towards improving the standardisation of RNA-seq for precision medicine.
| S-EPMC8214188 | biostudies-literature
Project description:Here we walk through an end-to-end gene-level RNA-Seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were aligned to the reference genome, and prepare a count matrix which tallies the number of RNA-seq reads/fragments within each gene for each sample. We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results.
| S-EPMC4670015 | biostudies-literature
Project description:Transcriptomes are dynamic and unique, with each cell type/tissue, developmental stage and species expressing a different repertoire of RNA transcripts. Most mRNAs and well-characterized long noncoding RNAs are shaped with a 5' cap and 3' poly(A) tail, thus conventional transcriptome analyses typically start with the enrichment of poly(A)+ RNAs by oligo(dT) selection, followed by deep sequencing approaches. However, accumulated lines of evidence suggest that many RNA transcripts are processed by alternative mechanisms without 3' poly(A) tails and, therefore, fail to be enriched by oligo(dT) purification and are absent following deep sequencing analyses. We have described an enrichment strategy to purify non-polyadenylated (poly(A)-/ribo-) RNAs from human total RNAs by removal of both poly(A)+ RNA transcripts and ribosomal RNAs, which led to the identification of many novel RNA transcripts with non-canonical 3' ends in human. Here, we describe the application of non-polyadenylated RNA-sequencing in rhesus monkey and mouse cell lines/tissue, and further profile the transcription of non-polyadenylated RNAs across species, providing new resources for non-polyadenylated RNA identification and comparison across species.
| S-EPMC4535946 | biostudies-literature
Project description:BackgroundDifferential expression (DE) analysis of RNA-seq data typically depends on gene annotations. Different sets of gene annotations are available for the human genome and are continually updated-a process complicated with the development and application of high-throughput sequencing technologies. However, the impact of the complexity of gene annotations on DE analysis remains unclear.ResultsUsing "mappability", a metric of the complexity of gene annotation, we compared three distinct human gene annotations, GENCODE, RefSeq, and NONCODE, and evaluated how mappability affected DE analysis. We found that mappability was significantly different among the human gene annotations. We also found that increasing mappability improved the performance of DE analysis, and the impact of mappability mainly evident in the quantification step and propagated downstream of DE analysis systematically.ConclusionsWe assessed how the complexity of gene annotations affects DE analysis using mappability. Our findings indicate that the growth and complexity of gene annotations negatively impact the performance of DE analysis, suggesting that an approach that excludes unnecessary gene models from gene annotations improves the performance of DE analysis.
| S-EPMC8501603 | biostudies-literature
Project description:PurposeThe management of triple-negative breast cancer (TNBC) remains a significant clinical challenge due to the lack of effective targeted therapies. Inhibitors of the cyclin-dependent kinases 4 and 6 (CDK4/6) are emerging as promising therapeutic agents against TNBC; however, cells can rapidly acquire resistance through multiple mechanisms that are yet to be identified. Therefore, determining the mechanisms underlying resistance to CDK4/6 inhibition is crucial to develop combination therapies that can extend the efficacy of the CDK4/6 inhibitors or delay resistance. This study aims to identify differentially expressed genes (DEG) associated with acquired resistance to palbociclib in ER- breast cancer cells.MethodsWe performed next-generation transcriptomic sequencing (RNA-seq) and pathway analysis in ER- MDA-MB-231 palbociclib-sensitive (231/pS) and palbociclib-resistant (231/pR) cells.ResultsWe identified 2247 up-regulated and 1427 down-regulated transcripts in 231/pR compared to 231/pS cells. DEGs were subjected to functional analysis using Gene Ontology (GO) and the KEGG database which identified many transduction pathways associated with breast cancer, including the PI3K/AKT, PTEN and mTOR pathways. Additionally, Ingenuity Pathway Analysis (IPA) revealed that resistance to palbociclib is closely associated with altered cholesterol and fatty acid biosynthesis suggesting that resistance to palbociclib may be dependent on lipid metabolic reprograming.ConclusionThis study provides evidence that lipid metabolism is altered in TNBC with acquired resistance to palbociclib. Further studies are needed to determine if the observed lipid metabolic rewiring can be exploited to overcome therapy resistance in TNBC.
| S-EPMC8019424 | biostudies-literature
Project description:MOTIVATION:RNA-seq experiments are usually carried out in three or fewer replicates. In order to work well with so few samples, differential gene expression (DGE) tools typically assume the form of the underlying gene expression distribution. In this paper, the statistical properties of gene expression from RNA-seq are investigated in the complex eukaryote, Arabidopsis thaliana, extending and generalizing the results of previous work in the simple eukaryote Saccharomyces cerevisiae. RESULTS:We show that, consistent with the results in S.cerevisiae, more gene expression measurements in A.thaliana are consistent with being drawn from an underlying negative binomial distribution than either a log-normal distribution or a normal distribution, and that the size and complexity of the A.thaliana transcriptome does not influence the false positive rate performance of nine widely used DGE tools tested here. We therefore recommend the use of DGE tools that are based on the negative binomial distribution. AVAILABILITY AND IMPLEMENTATION:The raw data for the 17 WT Arabidopsis thaliana datasets is available from the European Nucleotide Archive (E-MTAB-5446). The processed and aligned data can be visualized in context using IGB (Freese et al., 2016), or downloaded directly, using our publicly available IGB quickload server at https://compbio.lifesci.dundee.ac.uk/arabidopsisQuickload/public_quickload/ under 'RNAseq>Froussios2019'. All scripts and commands are available from github at https://github.com/bartongroup/KF_arabidopsis-GRNA. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
| S-EPMC6748783 | biostudies-literature
Project description:A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data. We describe a comprehensive evaluation of common methods using the SEQC benchmark dataset and ENCODE data. We consider a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has no detectable expression. We find significant differences among the methods, but note that array-based methods adapted to RNA-seq data perform comparably to methods designed for RNA-seq. Our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth.
| S-EPMC4054597 | biostudies-literature
Project description:RNA-Seq is increasingly being used for gene expression profiling. In this approach, next-generation sequencing (NGS) platforms are used for sequencing. Due to highly parallel nature, millions of reads are generated in a short time and at low cost. Therefore analysis of the data is a major challenge and development of statistical and computational methods is essential for drawing meaningful conclusions from this huge data. In here, we assessed three different types of normalization (transcript parts per million, trimmed mean of M values, quantile normalization) and evaluated if normalized data reduces technical variability across replicates. In addition, we also proposed two novel methods for detecting differentially expressed genes between two biological conditions: (i) likelihood ratio method, and (ii) Bayesian method. Our proposed methods for finding differentially expressed genes were tested on three real datasets. Our methods performed at least as well as, and often better than, the existing methods for analysis of differential expression.
| S-EPMC4393055 | biostudies-literature