ASpli: integrative analysis of splicing landscapes through RNA-Seq assays
TL;DR: Mancini et al. as discussed by the authors proposed a framework for the regulation of bioquimicas based on the Centro de Regulacion Genomica (CGRG) and the Consejo Nacional de Investigaciones Cientificas y Tecnicas.
Abstract: Fil: Mancini, Estefania. Centro de Regulacion Genomica; Espana. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Parque Centenario. Instituto de Investigaciones Bioquimicas de Buenos Aires. Fundacion Instituto Leloir. Instituto de Investigaciones Bioquimicas de Buenos Aires; Argentina
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TL;DR: A review of the emerging bulk RNA-Seq-based analyses, emphasizing less familiar and underused applications, is presented in this paper, highlighting the power of bulk RNASeq in providing biological insights.
Abstract: Significant innovations in next-generation sequencing techniques and bioinformatics tools have impacted our appreciation and understanding of RNA. Practical RNA sequencing (RNA-Seq) applications have evolved in conjunction with sequence technology and bioinformatic tools advances. In most projects, bulk RNA-Seq data is used to measure gene expression patterns, isoform expression, alternative splicing and single-nucleotide polymorphisms. However, RNA-Seq holds far more hidden biological information including details of copy number alteration, microbial contamination, transposable elements, cell type (deconvolution) and the presence of neoantigens. Recent novel and advanced bioinformatic algorithms developed the capacity to retrieve this information from bulk RNA-Seq data, thus broadening its scope. The focus of this review is to comprehend the emerging bulk RNA-Seq-based analyses, emphasizing less familiar and underused applications. In doing so, we highlight the power of bulk RNA-Seq in providing biological insights.
15 citations
TL;DR: It is shown that SAS restricts leaf blade size through two distinct cellular strategies, early SAS induction limits cell division, while later exposure limits cell expansion, which enables phytochromes to maintain control of leaf size through the proliferative and expansion phases of leaf growth.
Abstract: Plants are plastic organisms that optimize growth in response to a changing environment. This adaptive capability is regulated by external cues, including light, which provides vital information about the habitat. Phytochrome photoreceptors detect far-red light, indicative of nearby vegetation, and elicit the adaptive shade-avoidance syndrome (SAS), which is critical for plant survival. Plants exhibiting SAS are typically more elongated, with distinctive, small, narrow leaf blades. By applying SAS-inducing end-of-day far-red (EoD FR) treatments at different times during Arabidopsis (Arabidopsis thaliana) leaf 3 development, we have shown that SAS restricts leaf blade size through two distinct cellular strategies. Early SAS induction limits cell division, while later exposure limits cell expansion. This flexible strategy enables phytochromes to maintain control of leaf size through the proliferative and expansion phases of leaf growth. mRNAseq time course data, accessible through a community resource, coupled to a bioinformatics pipeline, identified pathways that underlie these dramatic changes in leaf growth. Phytochrome regulates a suite of major development pathways that control cell division, expansion, and cell fate. Further, phytochromes control cell proliferation through synchronous regulation of the cell cycle, DNA replication, DNA repair, and cytokinesis, and play an important role in sustaining ribosome biogenesis and translation throughout leaf development.
14 citations
TL;DR: It is shown that, although TFIIS seems unnecessary under optimal conditions in Arabidopsis, its absence renders plants supersensitive to heat; tfIIs mutants die even when exposed to sublethal high temperature, suggesting that the vital role of TFIis in stress adaptation of plants is conserved.
Abstract: Abstract Elongation factor TFIIS (transcription factor IIS) is structurally and biochemically probably the best characterized elongation cofactor of RNA polymerase II. However, little is known about TFIIS regulation or its roles during stress responses. Here, we show that, although TFIIS seems unnecessary under optimal conditions in Arabidopsis, its absence renders plants supersensitive to heat; tfIIs mutants die even when exposed to sublethal high temperature. TFIIS activity is required for thermal adaptation throughout the whole life cycle of plants, ensuring both survival and reproductive success. By employing a transcriptome analysis, we unravel that the absence of TFIIS makes transcriptional reprogramming sluggish, and affects expression and alternative splicing pattern of hundreds of heat-regulated transcripts. Transcriptome changes indirectly cause proteotoxic stress and deterioration of cellular pathways, including photosynthesis, which finally leads to lethality. Contrary to expectations of being constantly present to support transcription, we show that TFIIS is dynamically regulated. TFIIS accumulation during heat occurs in evolutionary distant species, including the unicellular alga Chlamydomonas reinhardtii, dicot Brassica napus and monocot Hordeum vulgare, suggesting that the vital role of TFIIS in stress adaptation of plants is conserved.
9 citations
TL;DR: In this article , the effects of all mutations in the region comprising CD19 exons 1-3 are quantitatively disentangled and the authors identify ~200 single point mutations that alter CD19 splicing and thus could predispose B-ALL patients to developing CART-19 resistance.
Abstract: Abstract Following CART-19 immunotherapy for B-cell acute lymphoblastic leukaemia (B-ALL), many patients relapse due to loss of the cognate CD19 epitope. Since epitope loss can be caused by aberrant CD19 exon 2 processing, we herein investigate the regulatory code that controls CD19 splicing. We combine high-throughput mutagenesis with mathematical modelling to quantitatively disentangle the effects of all mutations in the region comprising CD19 exons 1-3. Thereupon, we identify ~200 single point mutations that alter CD19 splicing and thus could predispose B-ALL patients to developing CART-19 resistance. Furthermore, we report almost 100 previously unknown splice isoforms that emerge from cryptic splice sites and likely encode non-functional CD19 proteins. We further identify cis -regulatory elements and trans -acting RNA-binding proteins that control CD19 splicing (e.g., PTBP1 and SF3B4) and validate that loss of these factors leads to pervasive CD19 mis-splicing. Our dataset represents a comprehensive resource for identifying predictive biomarkers for CART-19 therapy.
7 citations
TL;DR: The regulation of SCF components involved in hormonal perception by S-nitrosation may represent a key strategy to determine the precise time and site-dependent activation of each hormonal signaling pathway and highlights NO as a pivotal molecular player in these scenarios.
Abstract: E3 ubiquitin ligases mediate the last step of the ubiquitination pathway in the ubiquitin-proteasome system (UPS). By targeting transcriptional regulators for their turnover, E3s play a crucial role in every aspect of plant biology. In plants, SKP1/CULLIN1/F-BOX PROTEIN (SCF)-type E3 ubiquitin ligases are essential for the perception and signaling of several key hormones including auxins and jasmonates (JAs). F-box proteins, TRANSPORT INHIBITOR RESPONSE 1 (TIR1) and CORONATINE INSENSITIVE 1 (COI1), bind directly transcriptional repressors AUXIN/INDOLE-3-ACETIC ACID (AUX/IAA) and JASMONATE ZIM-DOMAIN (JAZ) in auxin- and JAs-depending manner, respectively, which permits the perception of the hormones and transcriptional activation of signaling pathways. Redox modification of proteins mainly by S-nitrosation of cysteines (Cys) residues via nitric oxide (NO) has emerged as a valued regulatory mechanism in physiological processes requiring its rapid and versatile integration. Previously, we demonstrated that TIR1 and Arabidopsis thaliana SKP1 (ASK1) are targets of S-nitrosation, and these NO-dependent posttranslational modifications enhance protein-protein interactions and positively regulate SCFTIR1 complex assembly and expression of auxin response genes. In this work, we confirmed S-nitrosation of Cys140 in TIR1, which was associated in planta to auxin-dependent developmental and stress-associated responses. In addition, we provide evidence on the modulation of the SCFCOI1 complex by different S-nitrosation events. We demonstrated that S-nitrosation of ASK1 Cys118 enhanced ASK1-COI1 protein-protein interaction. Overexpression of non-nitrosable ask1 mutant protein impaired the activation of JA-responsive genes mediated by SCFCOI1 illustrating the functional relevance of this redox-mediated regulation in planta. In silico analysis positions COI1 as a promising S-nitrosation target, and demonstrated that plants treated with methyl JA (MeJA) or S-nitrosocysteine (NO-Cys, S-nitrosation agent) develop shared responses at a genome-wide level. The regulation of SCF components involved in hormonal perception by S-nitrosation may represent a key strategy to determine the precise time and site-dependent activation of each hormonal signaling pathway and highlights NO as a pivotal molecular player in these scenarios.
5 citations
References
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TL;DR: EdgeR as mentioned in this paper is a Bioconductor software package for examining differential expression of replicated count data, which uses an overdispersed Poisson model to account for both biological and technical variability and empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference.
Abstract: Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).
29,413 citations
TL;DR: The RNA-Seq approach to transcriptome profiling that uses deep-sequencing technologies provides a far more precise measurement of levels of transcripts and their isoforms than other methods.
Abstract: RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. Studies using this method have already altered our view of the extent and complexity of eukaryotic transcriptomes. RNA-Seq also provides a far more precise measurement of levels of transcripts and their isoforms than other methods. This article describes the RNA-Seq approach, the challenges associated with its application, and the advances made so far in characterizing several eukaryote transcriptomes.
11,528 citations
TL;DR: This protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results, which takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.
Abstract: Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.
10,913 citations
TL;DR: Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases, which removes a major computational bottleneck in RNA-seq analysis.
Abstract: We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in <10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.
6,468 citations
TL;DR: Salmon is the first transcriptome-wide quantifier to correct for fragment GC-content bias, which substantially improves the accuracy of abundance estimates and the sensitivity of subsequent differential expression analysis.
Abstract: 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.
6,095 citations