ArticleBMC bioinformatics2015
A convex formulation for joint RNA isoform detection and quantification from multiple RNA-seq samples.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Jumper enables discontinuous transcript assembly in coronaviruses.Nature communications · 2021Article
- STRONG: metagenomics strain resolution on assembly graphs.Genome biology · 2021Article
- IntAPT: integrated assembly of phenotype-specific transcripts from multiple RNA-seq profiles.Bioinformatics (Oxford, England) · 2021Article
- Event Analysis: Using Transcript Events To Improve Estimates of Abundance in RNA-seq Data.G3 (Bethesda, Md.) · 2018Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundDetecting and quantifying isoforms from RNA-seq data is an important but challenging task. The problem is often ill-posed, particularly at low coverage. One promising direction is to exploit several samples simultaneously.
resultsWe propose a new method for solving the isoform deconvolution problem jointly across several samples. We formulate a convex optimization problem that allows to share information between samples and that we solve efficiently. We demonstrate the benefits of combining several samples on simulated and real data, and show that our approach outperforms pooling strategies and methods based on integer programming.
conclusionOur convex formulation to jointly detect and quantify isoforms from RNA-seq data of multiple related samples is a computationally efficient approach to leverage the hypotheses that some isoforms are likely to be present in several samples. The software and source code are available at http://cbio.ensmp.fr/flipflop.
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