Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification.
Ontology highlight
ABSTRACT:
SUBMITTER: Love MI
PROVIDER: S-EPMC6178912 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Studies of differential gene expression have identified several molecular signatures and pathways associated with Parkinson's disease (PD). The role of isoform switches and differential transcript usage (DTU) remains, however, unexplored. Here, we report the first genome-wide study of DTU in PD. We performed RNA sequencing following ribosomal RNA depletion in prefrontal cortex samples of 49 individuals from two independent case-control cohorts. DTU was assessed using two transcript-count based approaches, implemented in the DRIMSeq and DEXSeq tools. Multiple PD-associated DTU events were detected in each cohort, of which 23 DTU events in 19 genes replicated across both patient cohorts. For several of these, including THEM5, SLC16A1 and BCHE, DTU was predicted to have substantial functional consequences, such as altered subcellular localization or switching to non-protein coding isoforms. Furthermore, genes with PD-associated DTU were enriched in functional pathways previously linked to PD, including reactive oxygen species generation and protein homeostasis. Importantly, the vast majority of genes exhibiting DTU were not differentially expressed at the gene-level and were therefore not identified by conventional differential gene expression analysis. Our findings provide the first insight into the DTU landscape of PD and identify novel disease-associated genes. Moreover, we show that DTU may have important functional consequences in the PD brain, since it is predicted to alter the functional composition of the proteome. Based on these results, we propose that DTU analysis is an essential complement to differential gene expression studies in order to provide a more accurate and complete picture of disease-associated transcriptomic alterations.
| S-EPMC7660910 | biostudies-literature
Project description:We introduce Salmon, a lightweight method for quantifying transcript abundance from RNA-seq reads. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure. It is the first transcriptome-wide quantifier to correct for fragment GC-content bias, which, as we demonstrate here, substantially improves the accuracy of abundance estimates and the sensitivity of subsequent differential expression analysis.
| S-EPMC5600148 | biostudies-literature
Project description:RNA-seq has been a boon to the quantitative analysis of transcriptomes. A notable application is the detection of changes in transcript usage between experimental conditions. For example, discovery of pathological alternative splicing may allow the development of new treatments or better management of patients. From an analysis perspective, there are several ways to approach RNA-seq data to unravel differential transcript usage, such as annotation-based exon-level counting, differential analysis of the percentage spliced in, or quantitative analysis of assembled transcripts. The goal of this research is to compare and contrast current state-of-the-art methods, and to suggest improvements to commonly used work flows.We assess the performance of representative work flows using synthetic data and explore the effect of using non-standard counting bin definitions as input to DEXSeq, a state-of-the-art inference engine. Although the canonical counting provided the best results overall, several non-canonical approaches were as good or better in specific aspects and most counting approaches outperformed the evaluated event- and assembly-based methods. We show that an incomplete annotation catalog can have a detrimental effect on the ability to detect differential transcript usage in transcriptomes with few isoforms per gene and that isoform-level prefiltering can considerably improve false discovery rate control.Count-based methods generally perform well in the detection of differential transcript usage. Controlling the false discovery rate at the imposed threshold is difficult, particularly in complex organisms, but can be improved by prefiltering the annotation catalog.
| S-EPMC4729156 | biostudies-literature
Project description:MotivationEach year, the number of published bulk and single-cell RNA-seq data sets is growing exponentially. Studies analyzing such data are commonly looking at gene-level differences, while the collected RNA-seq data inherently represents reads of transcript isoform sequences. Utilizing transcriptomic quantifiers, RNA-seq reads can be attributed to specific isoforms, allowing for analysis of transcript-level differences. A differential transcript usage (DTU) analysis is testing for proportional differences in a gene's transcript composition, and has been of rising interest for many research questions, such as analysis of differential splicing or cell type identification.ResultsWe present the R package DTUrtle, the first DTU analysis workflow for both bulk and single-cell RNA-seq data sets, and the first package to conduct a 'classical' DTU analysis in a single-cell context. DTUrtle extends established statistical frameworks, offers various result aggregation and visualization options and a novel detection probability score for tagged-end data. It has been successfully applied to bulk and single-cell RNA-seq data of human and mouse, confirming and extending key results. Additionally, we present novel potential DTU applications like the identification of cell type specific transcript isoforms as biomarkers.AvailabilityThe R package DTUrtle is available at https://github.com/TobiTekath/DTUrtle with extensive vignettes and documentation at https://tobitekath.github.io/DTUrtle/.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8570804 | biostudies-literature
Project description:BACKGROUND: Zebrafish embryos are transcriptionally silent until activation of the zygotic genome during the 10th cell cycle. Onset of transcription is followed by cellular and morphological changes involving cell speciation and gastrulation. Previous genome-wide surveys of transcriptional changes only assessed gene expression levels; however, recent studies have shown the necessity to map isoform-specific transcriptional changes. Here, we perform isoform discovery and quantification on transcriptome sequences from before and after zebrafish zygotic genome activation (ZGA). RESULTS: We identify novel isoforms and isoform switches during ZGA for genes related to cell adhesion, pluripotency and DNA methylation. Isoform switching events include alternative splicing and changes in transcriptional start sites and in 3' untranslated regions. New isoforms are identified even for well-characterized genes such as pou5f1, sall4 and dnmt1. Genes involved in cell-cell interactions such as f11r and magi1 display isoform switches with alterations of coding sequences. We also detect over 1000 transcripts that acquire a longer 3' terminal exon when transcribed by the zygote compared to their maternal transcript counterparts. ChIP-sequencing data mapped onto skipped exon events reveal a correlation between histone H3K36 trimethylation peaks and skipped exons, suggesting epigenetic marks being part of alternative splicing regulation. CONCLUSIONS: The novel isoforms and isoform switches reported here include regulators of transcriptional, cellular and morphological changes taking place around ZGA. Our data display an array of isoform-related functional changes and represent a valuable resource complementary to existing early embryo transcriptomes.
| S-EPMC3747860 | biostudies-literature
Project description:Alzheimer's disease (AD) is the leading cause of dementia in aging individuals. Yet, the pathophysiological processes involved in AD onset and progression are still poorly understood. Among numerous strategies, a comprehensive overview of gene expression alterations in the diseased brain could contribute for a better understanding of the AD pathology. In this work, we probed the differential expression of genes in different brain regions of healthy and AD adult subjects using data from three large transcriptomic studies: Mayo Clinic, Mount Sinai Brain Bank (MSBB), and ROSMAP. Using a combination of differential expression of gene and isoform switch analyses, we provide a detailed landscape of gene expression alterations in the temporal and frontal lobes, harboring brain areas affected at early and late stages of the AD pathology, respectively. Next, we took advantage of an indirect approach to assign the complex gene expression changes revealed in bulk RNAseq to individual cell types/subtypes of the adult brain. This strategy allowed us to identify previously overlooked gene expression changes in the brain of AD patients. Among these alterations, we show isoform switches in the AD causal gene amyloid-beta precursor protein (APP) and the risk gene bridging integrator 1 (BIN1), which could have important functional consequences in neuronal cells. Altogether, our work proposes a novel integrative strategy to analyze RNAseq data in AD and other neurodegenerative diseases based on both gene/transcript expression and regional/cell-type specificities.
| S-EPMC7782705 | biostudies-literature
Project description:Background: RNA sequencing has enabled high-throughput and fine-grained quantitative analyses of the transcriptome. While differential gene expression is the most widely used application of this technology, RNA-seq data also has the resolution to infer differential transcript usage (DTU), which can elucidate the role of different transcript isoforms between experimental conditions, cell types or tissues. DTU has typically been inferred from exon-count data, which has issues with assigning reads unambiguously to counting bins, and requires alignment of reads to the genome. Recently, approaches have emerged that use transcript quantification estimates directly for DTU. Transcript counts can be inferred from 'pseudo' or lightweight aligners, which are significantly faster than traditional genome alignment. However, recent evaluations show lower sensitivity in DTU analysis compared to exon-level analysis. Transcript abundances are estimated from equivalence classes (ECs), which determine the transcripts that any given read is compatible with. Recent work has proposed performing a variety of RNA-seq analysis directly on equivalence class counts (ECCs). Methods: Here we demonstrate that ECCs can be used effectively with existing count-based methods for detecting DTU. We evaluate this approach on simulated human and drosophila data, as well as on a real dataset through subset testing. Results: We find that ECCs have similar sensitivity and false discovery rates as exon-level counts but can be generated in a fraction of the time through the use of pseudo-aligners. Conclusions: We posit that equivalence class read counts are a natural unit on which to perform differential transcript usage analysis.
| S-EPMC6524746 | biostudies-literature
Project description:Nitrogen (N) is crucial for plant growth and development; however, excessive use of N fertilizers cause many problems including environmental damage, degradation of soil fertility, and high cost to the farmers. Therefore, immediate implementation is required to develop N efficient crop varieties. Rice being low nitrogen use efficiency (NUE) and a high demand staple food across the world has become a favorite crop to study the NUE trait. In the current study, we used the publicly available transcriptome data generated from the root and shoot tissues of two rice genotypes IR-64 and Nagina-22 (N-22) under optimum N supply (N+) and chronic N-starvation (N-). A stringent pipeline was applied to detect differentially expressed genes (DEGs), alternatively spliced (DAS) genes, differentially expressed transcripts (DETs) and differential transcript usage (DTU) transcripts in both the varieties and tissues under N+ and N- conditions. The DAS genes and DTU transcripts identified in the study were found to be involved in several metabolic and biosynthesis processes. We suggest alternative splicing (AS) plays an important role in fine-tuning the regulation of metabolic pathways related genes in genotype, tissue, and condition-dependent manner. The current study will help in understanding the transcriptional dynamics of NUE traits in the future.
| S-EPMC8066416 | biostudies-literature
Project description:High-throughput single-cell RNA-seq (scRNA-seq) is a powerful tool for studying gene expression in single cells. Most current scRNA-seq bioinformatics tools focus on analysing overall expression levels, largely ignoring alternative mRNA isoform expression. We present a computational pipeline, Sierra, that readily detects differential transcript usage from data generated by commonly used polyA-captured scRNA-seq technology. We validate Sierra by comparing cardiac scRNA-seq cell types to bulk RNA-seq of matched populations, finding significant overlap in differential transcripts. Sierra detects differential transcript usage across human peripheral blood mononuclear cells and the Tabula Muris, and 3 'UTR shortening in cardiac fibroblasts. Sierra is available at https://github.com/VCCRI/Sierra .
| S-EPMC7341584 | biostudies-literature
Project description:RNA sequencing studies with complex designs and transcript-resolution analyses involve multiple hypotheses per gene; however, conventional approaches fail to control the false discovery rate (FDR) at gene level. We propose stageR, a two-stage testing paradigm that leverages the increased power of aggregated gene-level tests and allows post hoc assessment for significant genes. This method provides gene-level FDR control and boosts power for testing interaction effects. In transcript-level analysis, it provides a framework that performs powerful gene-level tests while maintaining biological interpretation at transcript-level resolution. The procedure is applicable whenever individual hypotheses can be aggregated, providing a unified framework for complex high-throughput experiments.
| S-EPMC5547545 | biostudies-literature