Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

The identification of switch-like alternative splicing exons among multiple samples with RNA-Seq data.

ABSTRACT: Alternative splicing is an ubiquitous phenomenon in most human genes and has important functions. The switch-like exon is the type of exon that has a high level of usage in some tissues, but has a low level of usage in the other tissues. They usually undergo strong tissue-specific regulations. There is still a lack a systematic method to identify switch-like exons from multiple RNA-seq samples. We proposed a novel method called iterative Tertile Absolute Deviation around the mode (iTAD) to profile the distribution of exon relative usages among multiple samples and to identify switch-like exons and other types of exons using a robust statistic estimator. We validated the method with simulation data, and applied it on RNA-seq data of 16 human body tissues and detected 3,100 switch-like exons. We found that switch-like exons tend to be more associated with Alu elements in their flanking intron regions than other types of exons.

SUBMITTER: Qin Z

PROVIDER: S-EPMC5444801 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

The identification of switch-like alternative splicing exons among multiple samples with RNA-Seq data.

Qin Zhiyi Z Zhang Xuegong X

PloS one 20170525 5

Alternative splicing is an ubiquitous phenomenon in most human genes and has important functions. The switch-like exon is the type of exon that has a high level of usage in some tissues, but has a low level of usage in the other tissues. They usually undergo strong tissue-specific regulations. There is still a lack a systematic method to identify switch-like exons from multiple RNA-seq samples. We proposed a novel method called iterative Tertile Absolute Deviation around the mode (iTAD) to profi ...[more]

PMID: 28542625

Similar Datasets

SplAdder: identification, quantification and testing of alternative splicing events from RNA-Seq data.

Project description:MotivationUnderstanding the occurrence and regulation of alternative splicing (AS) is a key task towards explaining the regulatory processes that shape the complex transcriptomes of higher eukaryotes. With the advent of high-throughput sequencing of RNA (RNA-Seq), the diversity of AS transcripts could be measured at an unprecedented depth. Although the catalog of known AS events has grown ever since, novel transcripts are commonly observed when working with less well annotated organisms, in the context of disease, or within large populations. Whereas an identification of complete transcripts is technically challenging and computationally expensive, focusing on single splicing events as a proxy for transcriptome characteristics is fruitful and sufficient for a wide range of analyses.ResultsWe present SplAdder, an alternative splicing toolbox, that takes RNA-Seq alignments and an annotation file as input to (i) augment the annotation based on RNA-Seq evidence, (ii) identify alternative splicing events present in the augmented annotation graph, (iii) quantify and confirm these events based on the RNA-Seq data and (iv) test for significant quantitative differences between samples. Thereby, our main focus lies on performance, accuracy and usability.AvailabilitySource code and documentation are available for download at http://github.com/ratschlab/spladder Example data, introductory information and a small tutorial are accessible via http://bioweb.me/spladderContacts: andre.kahles@ratschlab.org or gunnar.ratsch@ratschlab.orgSupplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC4908322 | biostudies-literature

ARH-seq: identification of differential splicing in RNA-seq data.

Project description:The computational prediction of alternative splicing from high-throughput sequencing data is inherently difficult and necessitates robust statistical measures because the differential splicing signal is overlaid by influencing factors such as gene expression differences and simultaneous expression of multiple isoforms amongst others. In this work we describe ARH-seq, a discovery tool for differential splicing in case-control studies that is based on the information-theoretic concept of entropy. ARH-seq works on high-throughput sequencing data and is an extension of the ARH method that was originally developed for exon microarrays. We show that the method has inherent features, such as independence of transcript exon number and independence of differential expression, what makes it particularly suited for detecting alternative splicing events from sequencing data. In order to test and validate our workflow we challenged it with publicly available sequencing data derived from human tissues and conducted a comparison with eight alternative computational methods. In order to judge the performance of the different methods we constructed a benchmark data set of true positive splicing events across different tissues agglomerated from public databases and show that ARH-seq is an accurate, computationally fast and high-performing method for detecting differential splicing events.

| S-EPMC4132698 | biostudies-literature

KISSPLICE: de-novo calling alternative splicing events from RNA-seq data.

Project description:BACKGROUND: In this paper, we address the problem of identifying and quantifying polymorphisms in RNA-seq data when no reference genome is available, without assembling the full transcripts. Based on the fundamental idea that each polymorphism corresponds to a recognisable pattern in a De Bruijn graph constructed from the RNA-seq reads, we propose a general model for all polymorphisms in such graphs. We then introduce an exact algorithm, called KISSPLICE, to extract alternative splicing events. RESULTS: We show that KISSPLICE enables to identify more correct events than general purpose transcriptome assemblers. Additionally, on a 71 M reads dataset from human brain and liver tissues, KISSPLICE identified 3497 alternative splicing events, out of which 56% are not present in the annotations, which confirms recent estimates showing that the complexity of alternative splicing has been largely underestimated so far. CONCLUSIONS: We propose new models and algorithms for the detection of polymorphism in RNA-seq data. This opens the way to a new kind of studies on large HTS RNA-seq datasets, where the focus is not the global reconstruction of full-length transcripts, but local assembly of polymorphic regions. KISSPLICE is available for download at http://alcovna.genouest.org/kissplice/.

| S-EPMC3358658 | biostudies-literature

Detecting Allele-Specific Alternative Splicing from Population-Scale RNA-Seq Data.

Project description:RNA sequencing (RNA-seq) is a powerful technology for studying human transcriptome variation. We introduce PAIRADISE (Paired Replicate Analysis of Allelic Differential Splicing Events), a method for detecting allele-specific alternative splicing (ASAS) from RNA-seq data. Unlike conventional approaches that detect ASAS events one sample at a time, PAIRADISE aggregates ASAS signals across multiple individuals in a population. By treating the two alleles of an individual as paired, and multiple individuals sharing a heterozygous SNP as replicates, we formulate ASAS detection using PAIRADISE as a statistical problem for identifying differential alternative splicing from RNA-seq data with paired replicates. PAIRADISE outperforms alternative statistical models in simulation studies. Applying PAIRADISE to replicate RNA-seq data of a single individual and to population-scale RNA-seq data across many individuals, we detect ASAS events associated with genome-wide association study (GWAS) signals of complex traits or diseases. Additionally, PAIRADISE ASAS analysis detects the effects of rare variants on alternative splicing. PAIRADISE provides a useful computational tool for elucidating the genetic variation and phenotypic association of alternative splicing in populations.

| S-EPMC7477012 | biostudies-literature

ASNEO: Identification of personalized alternative splicing based neoantigens with RNA-seq.

Project description:Cancer neoantigens have shown great potential in immunotherapy, while current software focuses on identifying neoantigens which are derived from SNVs, indels or gene fusions. Alternative splicing widely occurs in tumor samples and it has been proven to contribute to the generation of candidate neoantigens. Here we present ASNEO, which is an integrated computational pipeline for the identification of personalized Alternative Splicing based NEOantigens with RNA-seq. Our analyses showed that ASNEO could identify neopeptides which are presented by MHC I complex through mass spectrometry data validation. When ASNEO was applied to two immunotherapy-treated cohorts, we found that alternative splicing based neopeptides generally have a higher immune score than that of somatic neopeptides and alternative splicing based neopeptides could be a marker to predict patient survival pattern. Our identification of alternative splicing derived neopeptides would contribute to a more complete understanding of the tumor immune landscape. Prediction of patient-specific alternative splicing neopeptides has the potential to contribute to the development of personalized cancer vaccines.

| S-EPMC7425491 | biostudies-literature

Identification of alternative splicing regulatory patterns and characteristic splicing factors in heart failure using RNA-seq data and machine learning

Project description: Not available

| S-EPMC11336631 | biostudies-literature

Genome-wide Identification and Analysis of Splicing QTLs in Multiple Sclerosis by RNA-Seq Data.

Project description:Multiple sclerosis (MS) is an autoimmune disease characterized by inflammatory demyelinating lesions in the central nervous system. Recently, the dysregulation of alternative splicing (AS) in the brain has been found to significantly influence the progression of MS. Moreover, previous studies demonstrate that many MS-related variants in the genome act as the important regulation factors of AS events and contribute to the pathogenesis of MS. However, by far, no genome-wide research about the effect of genomic variants on AS events in MS has been reported. Here, we first implemented a strategy to obtain genomic variant genotype and AS isoform average percentage spliced-in values from RNA-seq data of 142 individuals (51 MS patients and 91 controls). Then, combing the two sets of data, we performed a cis-splicing quantitative trait loci (sQTLs) analysis to identify the cis-acting loci and the affected differential AS events in MS and further explored the characteristics of these cis-sQTLs. Finally, the weighted gene coexpression network and gene set enrichment analyses were used to investigate gene interaction pattern and functions of the affected AS events in MS. In total, we identified 5835 variants affecting 672 differential AS events. The cis-sQTLs tend to be distributed in proximity of the gene transcription initiation site, and the intronic variants of them are more capable of regulating AS events. The retained intron AS events are more susceptible to influence of genome variants, and their functions are involved in protein kinase and phosphorylation modification. In summary, these findings provide an insight into the mechanism of MS.

| S-EPMC8633104 | biostudies-literature

Inference of alternative splicing from RNA-Seq data with probabilistic splice graphs.

Project description:MotivationAlternative splicing and other processes that allow for different transcripts to be derived from the same gene are significant forces in the eukaryotic cell. RNA-Seq is a promising technology for analyzing alternative transcripts, as it does not require prior knowledge of transcript structures or genome sequences. However, analysis of RNA-Seq data in the presence of genes with large numbers of alternative transcripts is currently challenging due to efficiency, identifiability and representation issues.ResultsWe present RNA-Seq models and associated inference algorithms based on the concept of probabilistic splice graphs, which alleviate these issues. We prove that our models are often identifiable and demonstrate that our inference methods for quantification and differential processing detection are efficient and accurate.AvailabilitySoftware implementing our methods is available at http://deweylab.biostat.wisc.edu/psginfer.Contactcdewey@biostat.wisc.eduSupplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC3753571 | biostudies-literature

ASGAL: aligning RNA-Seq data to a splicing graph to detect novel alternative splicing events.

Project description:BACKGROUND:While the reconstruction of transcripts from a sample of RNA-Seq data is a computationally expensive and complicated task, the detection of splicing events from RNA-Seq data and a gene annotation is computationally feasible. This latter task, which is adequate for many transcriptome analyses, is usually achieved by aligning the reads to a reference genome, followed by comparing the alignments with a gene annotation, often implicitly represented by a graph: the splicing graph. RESULTS:We present ASGAL (Alternative Splicing Graph ALigner): a tool for mapping RNA-Seq data to the splicing graph, with the specific goal of detecting novel splicing events, involving either annotated or unannotated splice sites. ASGAL takes as input the annotated transcripts of a gene and a RNA-Seq sample, and computes (1) the spliced alignments of each read in input, and (2) a list of novel events with respect to the gene annotation. CONCLUSIONS:An experimental analysis shows that ASGAL allows to enrich the annotation with novel alternative splicing events even when genes in an experiment express at most one isoform. Compared with other tools which use the spliced alignment of reads against a reference genome for differential analysis, ASGAL better predicts events that use splice sites which are novel with respect to a splicing graph, showing a higher accuracy. To the best of our knowledge, ASGAL is the first tool that detects novel alternative splicing events by directly aligning reads to a splicing graph. AVAILABILITY:Source code, documentation, and data are available for download at http://asgal.algolab.eu .

| S-EPMC6247705 | biostudies-literature

Estimation of data-specific constitutive exons with RNA-Seq data.

Project description:BackgroundRNA-Seq has the potential to answer many diverse and interesting questions about the inner workings of cells. Estimating changes in the overall transcription of a gene is not straightforward. Changes in overall gene transcription can easily be confounded with changes in exon usage which alter the lengths of transcripts produced by a gene. Measuring the expression of constitutive exons--xons which are consistently conserved after splicing--ffers an unbiased estimation of the overall transcription of a gene.ResultsWe propose a clustering-based method, exClust, for estimating the exons that are consistently conserved after splicing in a given data set. These are considered as the exons which are "constitutive" in this data. The method utilises information from both annotation and the dataset of interest. The method is implemented in an openly available R function package, sydSeq.ConclusionWhen used on two real datasets exClust includes more than three times as many reads as the standard UI method, and improves concordance with qRT-PCR data. When compared to other methods, our method is shown to produce robust estimates of overall gene transcription.

| S-EPMC3656776 | 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