ArticleBioinformatics (Oxford, England)2012
Transcriptome assembly and isoform expression level estimation from biased RNA-Seq reads.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Comprehensive assessment of transcriptome assembly quality using CATS.Nature communications · 2026Article
- Bayesian identification of differentially expressed isoforms using a novel joint model of RNA-seq data.PLoS computational biology · 2025Article
- sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data.Nature methods · 2024Article
- Long noncoding RNA study: Genome-wide approaches.Genes & diseases · 2023Review
- Tiglon enables accurate transcriptome assembly via integrating mappings of different aligners.iScience · 2022Article
- Anti-bias training for (sc)RNA-seq: experimental and computational approaches to improve precision.Briefings in bioinformatics · 2021Article
- LIQA: long-read isoform quantification and analysis.Genome biology · 2021Article
- IntAPT: integrated assembly of phenotype-specific transcripts from multiple RNA-seq profiles.Bioinformatics (Oxford, England) · 2021Article
- Transcriptome profiling reveals the effects of drought tolerance in Giant Juncao.BMC plant biology · 2021Article
- TransBorrow: genome-guided transcriptome assembly by borrowing assemblies from different assemblers.Genome research · 2020Article
- Event Analysis: Using Transcript Events To Improve Estimates of Abundance in RNA-seq Data.G3 (Bethesda, Md.) · 2018Article
- TAPAS: tool for alternative polyadenylation site analysis.Bioinformatics (Oxford, England) · 2018Article
- Article
- Bayesian nonparametric discovery of isoforms and individual specific quantification.Nature communications · 2018Article
- FreePSI: an alignment-free approach to estimating exon-inclusion ratios without a reference transcriptome.Nucleic acids research · 2018Article
- SparseIso: a novel Bayesian approach to identify alternatively spliced isoforms from RNA-seq data.Bioinformatics (Oxford, England) · 2018Article
- CORNAS: coverage-dependent RNA-Seq analysis of gene expression data without biological replicates.BMC bioinformatics · 2017Article
- Genome-wide identification and differential analysis of translational initiation.Nature communications · 2017Article
- Information transduction capacity reduces the uncertainties in annotation-free isoform discovery and quantification.Nucleic acids research · 2017Article
- DeepBound: accurate identification of transcript boundaries via deep convolutional neural fields.Bioinformatics (Oxford, England) · 2017Article
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Authors and funding
2 authors.
Funding
Abstract
motivationRNA-Seq uses the high-throughput sequencing technology to identify and quantify transcriptome at an unprecedented high resolution and low cost. However, RNA-Seq reads are usually not uniformly distributed and biases in RNA-Seq data post great challenges in many applications including transcriptome assembly and the expression level estimation of genes or isoforms. Much effort has been made in the literature to calibrate the expression level estimation from biased RNA-Seq data, but the effect of biases on transcriptome assembly remains largely unexplored.
resultsHere, we propose a statistical framework for both transcriptome assembly and isoform expression level estimation from biased RNA-Seq data. Using a quasi-multinomial distribution model, our method is able to capture various types of RNA-Seq biases, including positional, sequencing and mappability biases. Our experimental results on simulated and real RNA-Seq datasets exhibit interesting effects of RNA-Seq biases on both transcriptome assembly and isoform expression level estimation. The advantage of our method is clearly shown in the experimental analysis by its high sensitivity and precision in transcriptome assembly and the high concordance of its estimated expression levels with quantitative reverse transcription-polymerase chain reaction data. AVAILABILITY: CEM is freely available at http://www.cs.ucr.edu/~liw/cem.html. CONTACT: liw@cs.ucr.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.