ArticleBMC bioinformatics2010
Towards reliable isoform quantification using RNA-SEQ data.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Comparative transcriptome analysis of two contrasting resistant and susceptible Aegilops tauschii accessions to wheat leaf rust (Puccinia triticina) using RNA-sequencing.Scientific reports · 2022Article
- Anti-bias training for (sc)RNA-seq: experimental and computational approaches to improve precision.Briefings in bioinformatics · 2021Article
- Genetic basis of functional variability in adhesion G protein-coupled receptors.Scientific reports · 2019Article
- Modeling Enzyme Processivity Reveals that RNA-Seq Libraries Are Biased in Characteristic and Correctable Ways.Cell systems · 2016Article
- IUTA: a tool for effectively detecting differential isoform usage from RNA-Seq data.BMC genomics · 2014Article
- New levels of transcriptome complexity at upper thermal limits in wild Drosophila revealed by exon expression analysis.Genetics · 2013Article
- EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments.Bioinformatics (Oxford, England) · 2013Article
- Detecting miRNAs in deep-sequencing data: a software performance comparison and evaluation.Briefings in bioinformatics · 2013Article
- In the spotlight: Bioinformatics.IEEE reviews in biomedical engineering · 2013Review
- Identification of human HK genes and gene expression regulation study in cancer from transcriptomics data analysis.PloS one · 2013Article
- High-throughput RNA sequencing of pseudomonas-infected Arabidopsis reveals hidden transcriptome complexity and novel splice variants.PloS one · 2013Article
- Transcriptome assembly and isoform expression level estimation from biased RNA-Seq reads.Bioinformatics (Oxford, England) · 2012Article
- Analysis of protein isoforms: can we do it better?Proteomics · 2012Review
- Modeling RNA degradation for RNA-Seq with applications.Biostatistics (Oxford, England) · 2012Article
- A context-based approach to identify the most likely mapping for RNA-seq experiments.BMC bioinformatics · 2012Article
- Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM).Bioinformatics (Oxford, England) · 2011Article
- IsoformEx: isoform level gene expression estimation using weighted non-negative least squares from mRNA-Seq data.BMC bioinformatics · 2011Article
- Estimation of alternative splicing isoform frequencies from RNA-Seq data.Algorithms for molecular biology : AMB · 2011Article
- Using RNA-Seq for gene identification, polymorphism detection and transcript profiling in two alfalfa genotypes with divergent cell wall composition in stems.BMC genomics · 2011Article
- Accurate quantification of transcriptome from RNA-Seq data by effective length normalization.Nucleic acids research · 2011Article
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2 authors.
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Abstract
backgroundIn 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.
methodsOur 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.
resultsPreliminary 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.
conclusionsGiven 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.
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