EMSAR: estimation of transcript abundance from RNA-seq data by mappability-based segmentation and reclustering

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 Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0704-z) contains supplementary material, which is available to authorized users.

A junction coverage compatibility score to quantify the reliability of transcript abundance estimates and annotation catalogs

“…Even for studies where the main aim is to detect differential expression at the gene level, incorporating transcript abundances can in some cases improve the inference (Wang et al, 2010; Trapnell et al, 2013; Soneson et al, 2015). As methods for transcript abundance estimation are improving, both in accuracy and speed, it has become increasingly common to estimate abundances of individual isoforms rather than of the gene as a whole, and today a plethora of transcript abundance estimation methods based on various underlying algorithms are available (e.g., Trapnell et al, 2010; Li & Dewey, 2011; Glaus et al, 2012; Roberts & Pachter, 2013; Patro et al, 2014; Lee et al, 2015; Pertea et al, 2015; Bray et al, 2016; Liu & Dickerson, 2017; Patro et al, 2017). Most evaluations of the ability of these methods to accurately estimate transcript abundances have been performed using simulated data, where reads are generated from a known transcriptome (Kanitz et al, 2015; Soneson et al, 2015), or using artificial spike-in sequences (Leshkowitz et al, 2016).…”

The genetic basis and evolution of red blood cell sickling in deer

“…S3b). We used emsar ( 61 ) with default parameters to assess transcript abundances. The three most abundant reconstructed transcripts correspond to full or partial α- and β-globin transcripts, including one transcript, highlighted above, that encompasses the entire adult β-globin CDS.…”

Bioinformatics services for analyzing massive genomic datasets

“…Finally, voom robustly estimates the mean-variance relationship and generates a precision weight for each individual normalized observation, which can be used to calculate differentially expressed genes from transcript ex- pression levels. Several other pipelines for RNA-Seq data analysis are available at Bio-Express, including MapSplice2-RSEM [15], Bowtie-EMSAR [16], STAR-HTSeq [17], and STAR-RSEM [18].…”