ArticleBMC bioinformatics2015
EMSAR: estimation of transcript abundance from RNA-seq data by mappability-based segmentation and reclustering.
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 8 papers.
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Who cites it
8 citing papers in PubMed.
- Multidimensional landscape of non-alcoholic fatty liver disease-related disease spectrum uncovered by big omics data: Profiling evidence and new perspectives.Smart medicine · 2023Review
- Bioinformatics services for analyzing massive genomic datasets.Genomics & informatics · 2020Article
- Understanding the Role of the WRKY Gene Family under Stress Conditions in Pigeonpea (Plants (Basel, Switzerland) · 2019Article
- Further confirmation of second- and third-generation Eimeria necatrix merozoite DEGs using suppression subtractive hybridization.Parasitology research · 2019Article
- A junction coverage compatibility score to quantify the reliability of transcript abundance estimates and annotation catalogs.Life science alliance · 2019Article
- Event Analysis: Using Transcript Events To Improve Estimates of Abundance in RNA-seq Data.G3 (Bethesda, Md.) · 2018Article
- The genetic basis and evolution of red blood cell sickling in deer.Nature ecology & evolution · 2018Article
- Near-optimal probabilistic RNA-seq quantification.Nature biotechnology · 2016Article
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Authors and funding
5 authors.
Funding
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Abstract
backgroundRNA-seq has been widely used for genome-wide expression profiling. RNA-seq data typically consists of tens of millions of short sequenced reads from different transcripts. However, due to sequence similarity among genes and among isoforms, the source of a given read is often ambiguous. Existing approaches for estimating expression levels from RNA-seq reads tend to compromise between accuracy and computational cost.
resultsWe introduce a new approach for quantifying transcript abundance from RNA-seq data. EMSAR (Estimation by Mappability-based Segmentation And Reclustering) groups reads according to the set of transcripts to which they are mapped and finds maximum likelihood estimates using a joint Poisson model for each optimal set of segments of transcripts. The method uses nearly all mapped reads, including those mapped to multiple genes. With an efficient transcriptome indexing based on modified suffix arrays, EMSAR minimizes the use of CPU time and memory while achieving accuracy comparable to the best existing methods.
conclusionsEMSAR is a method for quantifying transcripts from RNA-seq data with high accuracy and low computational cost. EMSAR is available at https://github.com/parklab/emsar.
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