Browse
Submit Data
Databases
API
Help

Dataset Information

16 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Poly(A) inclusive RNA isoform sequencing (PAIso-seq) reveals wide-spread non-adenosine residues within RNA poly(A) tails.

ABSTRACT: Message RNA poly(A) tails are vital for their function and regulation. However, the full-length sequence of mRNA isoforms with their poly(A) tails remains undetermined. Here, we develop a method at single-cell level sensitivity that enables quantification of poly(A) tails along with the full-length cDNA while reading non-adenosine residues within poly(A) tails precisely, which we name poly(A) inclusive RNA isoform sequencing (PAIso-seq). Using this method, we can quantify isoform specific poly(A) tail length. More interestingly, we find that 17% of the mRNAs harbor non-A residues within the body of poly(A) tails in mouse GV oocytes. We show that PAIso-seq is sensitive enough to analyze single GV oocytes. These findings will not only provide an accurate and sensitive tool in studying poly(A) tails, but also open a door for the function and regulation of non-adenosine modifications within the body of poly(A) tails.

SUBMITTER: Liu Y

PROVIDER: S-EPMC6876564 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Poly(A) inclusive RNA isoform sequencing (PAIso-seq) reveals wide-spread non-adenosine residues within RNA poly(A) tails.

Liu Yusheng Y Nie Hu H Liu Hongxiang H Lu Falong F

Nature communications 20191122 1

Message RNA poly(A) tails are vital for their function and regulation. However, the full-length sequence of mRNA isoforms with their poly(A) tails remains undetermined. Here, we develop a method at single-cell level sensitivity that enables quantification of poly(A) tails along with the full-length cDNA while reading non-adenosine residues within poly(A) tails precisely, which we name poly(A) inclusive RNA isoform sequencing (PAIso-seq). Using this method, we can quantify isoform specific poly(A ...[more]

PMID: 31757970

Similar Datasets

Poly(A)-seq: A method for direct sequencing and analysis of the transcriptomic poly(A)-tails.

Project description:Poly(A) tails at the 3' end of eukaryotic messenger RNAs control mRNA stability and translation efficiency. Facilitated by various NGS methods, alternative polyadenylation sites determining the 3'-UTR length of gene transcripts have been extensively studied. However, poly(A) lengths demonstrating dynamic and developmental regulation remain largely unexplored. The recently developed NGS-based methods for genome-wide poly(A) profiling have promoted the study of genom-wide poly(A) dynamics. Here we present a straight forward NGS-method for poly(A) profiling, which applies a direct 3'-end adaptor ligation and the template switching for 5'-end adaptor ligation for cDNA library construction. Poly(A) lengths are directly calculated from base call data using a self-developed pipeline pA-finder. The libraries were directly sequenced from the 3'-UTR regions into the followed poly(A) tails, firstly on NextSeq 500 to produce single-end 300-nt reads, demonstrating the method feasibility and that optimization of the fragmented RNA size for cDNA library construction could detecting longer poly (A) tails. We next applied Poly(A)-seq cDNA libraries containing 40-nt and 120-nt poly(A) tail spike-in RNAs on HiSeq X-ten and NovaSeq 6000 to obtain 150-nt and 250-nt pair-end reads. The sequencing profiles of the spike-in RNAs demonstrated both high accuracy and high quality score in reading poly(A) tails. The poly(A) signal bleeding into the 3' adaptor sequence and a sharp decreased quality score at the junction were observed, allowing the modification of pA-finder to remove homopolymeric signal bleeding. We hope that wide applications of Poly(A)-seq help facilitate the study of the development- and disease-related poly(A) dynamics and regulation, and of the recent emerging mixed tailing regulation.

| S-EPMC7297374 | biostudies-literature

Simultaneous isoform discovery and quantification from RNA-seq.

Project description:RNA sequencing is a recent technology which has seen an explosion of methods addressing all levels of analysis, from read mapping to transcript assembly to differential expression modeling. In particular the discovery of isoforms at the transcript assembly stage is a complex problem and current approaches suffer from various limitations. For instance, many approaches use graphs to construct a minimal set of isoforms which covers the observed reads, then perform a separate algorithm to quantify the isoforms, which can result in a loss of power. Current methods also use ad-hoc solutions to deal with the vast number of possible isoforms which can be constructed from a given set of reads. Finally, while the need of taking into account features such as read pairing and sampling rate of reads has been acknowledged, most existing methods do not seamlessly integrate these features as part of the model. We present Montebello, an integrated statistical approach which performs simultaneous isoform discovery and quantification by using a Monte Carlo simulation to find the most likely isoform composition leading to a set of observed reads. We compare Montebello to Cufflinks, a popular isoform discovery approach, on a simulated data set and on 46.3 million brain reads from an Illumina tissue panel. On this data set Montebello appears to offer a modest improvement over Cufflinks when considering discovery and parsimony metrics. In addition Montebello mitigates specific difficulties inherent in the Cufflinks approach. Finally, Montebello can be fine-tuned depending on the type of solution desired.

| S-EPMC3718502 | biostudies-literature

Towards reliable isoform quantification using RNA-SEQ data.

Project description:BACKGROUND: In eukaryotes, alternative splicing often generates multiple splice variants from a single gene. Here we explore the use of RNA sequencing (RNA-Seq) datasets to address the isoform quantification problem. Given a set of known splice variants, the goal is to estimate the relative abundance of the individual variants. METHODS: Our method employs a linear models framework to estimate the ratios of known isoforms in a sample. A key feature of our method is that it takes into account the non-uniformity of RNA-Seq read positions along the targeted transcripts. RESULTS: Preliminary tests indicate that the model performs well on both simulated and real data. In two publicly available RNA-Seq datasets, we identified several alternatively-spliced genes with switch-like, on/off expression properties, as well as a number of other genes that varied more subtly in isoform expression. In many cases, genes exhibiting differential expression of alternatively spliced transcripts were not differentially expressed at the gene level. CONCLUSIONS: Given that changes in isoform expression level frequently involve a continuum of isoform ratios, rather than all-or-nothing expression, and that they are often independent of general gene expression changes, we anticipate that our research will contribute to revealing a so far uninvestigated layer of the transcriptome. We believe that, in the future, researchers will prioritize genes for functional analysis based not only on observed changes in gene expression levels, but also on changes in alternative splicing.

| S-EPMC2863065 | biostudies-literature

Recognition of adenosine residues by the active site of poly(A)-specific ribonuclease.

Project description:Poly(A)-specific ribonuclease (PARN) is a mammalian 3'-exoribonuclease that degrades poly(A) with high specificity. To reveal mechanisms by which poly(A) is recognized by the active site of PARN, we have performed a kinetic analysis using a large repertoire of trinucleotide substrates. Our analysis demonstrated that PARN harbors specificity for adenosine recognition in its active site and that the nucleotides surrounding the scissile bond are critical for adenosine recognition. We propose that two binding pockets, which interact with the nucleotides surrounding the scissile bond, play a pivotal role in providing specificity for the recognition of adenosine residues by the active site of PARN. In addition, we show that PARN, besides poly(A), also quite efficiently degrades poly(U), approximately 10-fold less efficiently than poly(A). The poly(U)-degrading property of PARN could be of biological significance as oligo(U) tails recently have been proposed to play a role in RNA stabilization and destabilization.

| S-EPMC2804161 | biostudies-literature

Isoform-level microRNA-155 target prediction using RNA-seq.

Project description:Computational prediction of microRNA targets remains a challenging problem. The existing rule-based, data-driven and expression profiling approaches to target prediction are mostly approached from the gene-level. The increasing availability of RNA-seq data provides a new perspective for microRNA target prediction on the isoform-level. We hypothesize that the splicing isoform is the ultimate effector in microRNA targeting and that the proposed isoform-level approach is capable of predicting non-dominant isoform targets as well as their targeting regions that are otherwise invisible to many existing approaches. To test the hypothesis, we used an iterative expectation maximization (EM) algorithm to quantify transcriptomes at the isoform-level. The performance of the EM algorithm in transcriptome quantification was examined in simulation studies using FluxSimulator. We used joint evidence from isoform-level down-regulation and seed enrichment to predict microRNA-155 targets. We validated our computational approach using results from 149 in-house performed in vitro 3'-UTR assays. We also augmented the splicing database using exon-exon junction evidence, and applied the EM algorithm to predict and quantify 1572 cell line specific novel isoforms. Combined with seed enrichment analysis, we predicted 51 novel microRNA-155 isoform targets. Our work is among the first computational studies advocating the isoform-level microRNA target prediction.

| S-EPMC3089486 | biostudies-literature

PDE12 removes mitochondrial RNA poly(A) tails and controls translation in human mitochondria.

Project description:Polyadenylation of mRNA in human mitochondria is crucial for gene expression and perturbation of poly(A) tail length has been linked to a human neurodegenerative disease. Here we show that 2'-phosphodiesterase (2'-PDE), (hereafter PDE12), is a mitochondrial protein that specifically removes poly(A) extensions from mitochondrial mRNAs both in vitro and in mitochondria of cultured cells. In eukaryotes, poly(A) tails generally stabilize mature mRNAs, whereas in bacteria they increase mRNA turnover. In human mitochondria, the effects of increased PDE12 expression were transcript dependent. An excess of PDE12 led to an increase in the level of three mt-mRNAs (ND1, ND2 and CytB) and two (CO1 and CO2) were less abundant than in mitochondria of control cells and there was no appreciable effect on the steady-state level of the remainder of the mitochondrial transcripts. The alterations in poly(A) tail length accompanying elevated PDE12 expression were associated with severe inhibition of mitochondrial protein synthesis, and consequently respiratory incompetence. Therefore, we propose that mRNA poly(A) tails are important in regulating protein synthesis in human mitochondria, as it is the case for nuclear-encoded eukaryotic mRNA.

| S-EPMC3177208 | biostudies-literature

Addition of poly(A) and poly(A)-rich tails during RNA degradation in the cytoplasm of human cells.

Project description:Polyadenylation of RNA is a posttranscriptional modification that can play two somewhat opposite roles: stable polyadenylation of RNA encoded in the nuclear genomes of eukaryote cells contributes to nuclear export, translation initiation, and possibly transcript longevity as well. Conversely, transient polyadenylation targets RNA molecules to rapid exonucleolytic degradation. The latter role has been shown to take place in prokaryotes and organelles, as well as the nucleus of eukaryotic cells. Here we present evidence of hetero- and homopolymeric adenylation of truncated RNA molecules within the cytoplasm of human cells. RNAi-mediated silencing of the major RNA decay machinery of the cell resulted in the accumulation of these polyadenylated RNA fragments, indicating that they are degradation intermediates. Together, these results suggest that a mechanism of RNA decay, involving transient polyadenylation, is present in the cytoplasm of human cells.

| S-EPMC2867691 | biostudies-literature

Comparative evaluation of full-length isoform quantification from RNA-Seq.

Project description:BackgroundFull-length isoform quantification from RNA-Seq is a key goal in transcriptomics analyses and has been an area of active development since the beginning. The fundamental difficulty stems from the fact that RNA transcripts are long, while RNA-Seq reads are short.ResultsHere we use simulated benchmarking data that reflects many properties of real data, including polymorphisms, intron signal and non-uniform coverage, allowing for systematic comparative analyses of isoform quantification accuracy and its impact on differential expression analysis. Genome, transcriptome and pseudo alignment-based methods are included; and a simple approach is included as a baseline control.ConclusionsSalmon, kallisto, RSEM, and Cufflinks exhibit the highest accuracy on idealized data, while on more realistic data they do not perform dramatically better than the simple approach. We determine the structural parameters with the greatest impact on quantification accuracy to be length and sequence compression complexity and not so much the number of isoforms. The effect of incomplete annotation on performance is also investigated. Overall, the tested methods show sufficient divergence from the truth to suggest that full-length isoform quantification and isoform level DE should still be employed selectively.

| S-EPMC8145802 | biostudies-literature

Global view on the metabolism of RNA poly(A) tails in yeast Saccharomyces cerevisiae.

Project description:The polyadenosine tail (poly[A]-tail) is a universal modification of eukaryotic messenger RNAs (mRNAs) and non-coding RNAs (ncRNAs). In budding yeast, Pap1-synthesized mRNA poly(A) tails enhance export and translation, whereas Trf4/5-mediated polyadenylation of ncRNAs facilitates degradation by the exosome. Using direct RNA sequencing, we decipher the extent of poly(A) tail dynamics in yeast defective in all relevant exonucleases, deadenylases, and poly(A) polymerases. Predominantly ncRNA poly(A) tails are 20-60 adenosines long. Poly(A) tails of newly transcribed mRNAs are 50 adenosine long on average, with an upper limit of 200. Exonucleolysis by Trf5-assisted nuclear exosome and cytoplasmic deadenylases trim the tails to 40 adenosines on average. Surprisingly, PAN2/3 and CCR4-NOT deadenylase complexes have a large pool of non-overlapping substrates mainly defined by expression level. Finally, we demonstrate that mRNA poly(A) tail length strongly responds to growth conditions, such as heat and nutrient deprivation.

| S-EPMC8367983 | biostudies-literature

Efficient RNA isoform identification and quantification from RNA-Seq data with network flows.

Project description:MotivationSeveral state-of-the-art methods for isoform identification and quantification are based on [Formula: see text]-regularized regression, such as the Lasso. However, explicitly listing the-possibly exponentially-large set of candidate transcripts is intractable for genes with many exons. For this reason, existing approaches using the [Formula: see text]-penalty are either restricted to genes with few exons or only run the regression algorithm on a small set of preselected isoforms.ResultsWe introduce a new technique called FlipFlop, which can efficiently tackle the sparse estimation problem on the full set of candidate isoforms by using network flow optimization. Our technique removes the need of a preselection step, leading to better isoform identification while keeping a low computational cost. Experiments with synthetic and real RNA-Seq data confirm that our approach is more accurate than alternative methods and one of the fastest available.Availability and implementationSource code is freely available as an R package from the Bioconductor Web site (http://www.bioconductor.org/), and more information is available at http://cbio.ensmp.fr/flipflop.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC4147886 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data