ArticleGenome research2016
Integrative analysis with ChIP-seq advances the limits of transcript quantification from RNA-seq.
Article in Genome research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Oarfish: enhanced probabilistic modeling leads to improved accuracy in long read transcriptome quantification.Bioinformatics (Oxford, England) · 2025Article
- Quality assessment of long read data in multisample lrRNA-seq experiments using SQANTI-reads.Genome research · 2025Article
- SQANTI-reads: a tool for the quality assessment of long read data in multi-sample lrRNA-seq experiments.bioRxiv : the preprint server for biology · 2024Article
- Differential expression ofTranslational lung cancer research · 2024Article
- Oarfish: Enhanced probabilistic modeling leads to improved accuracy in long read transcriptome quantification.bioRxiv : the preprint server for biology · 2024Article
- Integrative genomic analyses reveal putative cell type-specific targets of the Drosophila ets transcription factor Pointed.BMC genomics · 2024Article
- Perplexity: evaluating transcript abundance estimation in the absence of ground truth.Algorithms for molecular biology : AMB · 2022Article
- Article
- Chromosome-Level Genome Assembly Reveals Significant Gene Expansion in the Toll and IMD Signaling Pathways ofFrontiers in genetics · 2021Article
- A Bayesian framework for inter-cellular information sharing improves dscRNA-seq quantification.Bioinformatics (Oxford, England) · 2020Article
- tappAS: a comprehensive computational framework for the analysis of the functional impact of differential splicing.Genome biology · 2020Article
- Event Analysis: Using Transcript Events To Improve Estimates of Abundance in RNA-seq Data.G3 (Bethesda, Md.) · 2018Article
- BRIE: transcriptome-wide splicing quantification in single cells.Genome biology · 2017Article
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Authors and funding
5 authors.
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Abstract
RNA-seq is currently the technology of choice for global measurement of transcript abundances in cells. Despite its successes, isoform-level quantification remains difficult because short RNA-seq reads are often compatible with multiple alternatively spliced isoforms. Existing methods rely heavily on uniquely mapping reads, which are not available for numerous isoforms that lack regions of unique sequence. To improve quantification accuracy in such difficult cases, we developed a novel computational method, prior-enhanced RSEM (pRSEM), which uses a complementary data type in addition to RNA-seq data. We found that ChIP-seq data of RNA polymerase II and histone modifications were particularly informative in this approach. In qRT-PCR validations, pRSEM was shown to be superior than competing methods in estimating relative isoform abundances within or across conditions. Data-driven simulations suggested that pRSEM has a greatly decreased false-positive rate at the expense of a small increase in false-negative rate. In aggregate, our study demonstrates that pRSEM transforms existing capacity to precisely estimate transcript abundances, especially at the isoform level.
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