Salmon provides accurate, fast, and bias-aware transcript expression estimates using dual-phase inference

Abstract

Existing methods for quantifying transcript abundance require a fundamental compromise: either use high quality read alignments and experiment-specific models or sacrifice them for speed. We introduce Salmon, a quantification method that overcomes this restriction by combining a novel 'lightweight' alignment procedure with a streaming parallel inference algorithm and a feature-rich bias model. These innovations yield both exceptional accuracy and order-of-magnitude speed benefits over traditional alignment-based methods.Estimating transcript abundance across cell types, species, and conditions is a fundamental task in genomics. For example, these estimates are used for the classification of diseases and their subtypes [1], for understanding expression changes during development [2], and tracking the progression of cancer [3]. Efficient quantification of transcript abundance from RNA-seq data is an especially pressing problem due to the exponentially increasing number of experiments and the * rob.growing adoption of expression data for medical diagnosis [4]. However, various methods that address this problem achieve accurate results at the cost of requiring significant computational resources and do not scale well with the rate at which data is produced [5]. The recently developed quantification tool Sailfish [6] achieves an order of magnitude speed improvement over previous approaches, but Sailfish can sometimes produce slightly less accurate estimates for paired-end data or for stranded protocols and does not take advantage of high quality alignment information and experiment-specific models.We introduce a quantification procedure, called Salmon ( Supplementary Fig. 1), that achieves best-in-class accuracy, takes advantage of high quality alignment information and experiment-specific models and provides the same order-of-magnitude speed benefits as Sailfish.Using synthetic data from both the RSEM simulator [7] and the Flux Simulator [8] as well as experimental quantitative PCR data [9], we show that Salmon generally outperforms Sailfish and eXpress [10] with respect to accuracy ( Fig. 1a-b,e; Supplementary Tables 1&2) and is also faster than Sailfish (Fig. 1c). The transcript abundance estimation problem is particularly difficult for genes with many isoforms since reads derived from these genes can map to many more transcripts, and we find that Salmon is also generally more accurate in this case (Fig. 1d). Salmon is designed to run in parallel so that the procedure scales better with the number of reads in an experiment. Salmon can quantify abundance either via a lightweight alignment procedure (Online methods, Lightweight alignment and Supplementary Fig. 2), or using pre-computed alignments provided in SAM or BAM format -we find that the quantification accuracy is robust to this choice of input ( Supplementary Fig. 3). Salmon is also typically more accurate than a recent unpublished procedure Kallisto ( Supplementary Figs. 4&5, Supplementary Table 1).An innovation contributing to Salmon's speed and accuracy is ...

Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences

“…To evaluate the accuracy of abundance estimation with transcript and gene resolution, we used Salmon 7 (v0.5.1) to estimate TPM values for each transcript in each of the data sets. Gene-level TPM estimates, representing the overall transcriptional output of each gene, were obtained by summing the corresponding transcript-level TPM estimates.…”

“…Several software packages have been developed for performing such “simple” counting (e.g., featureCounts 1 and HTSeq-count 2 ). More recently, the field has seen a surge in methods aimed at quantifying the abundances of individual transcripts (e.g., Cufflinks 3 , RSEM 4 , BitSeq 5 , kallisto 6 and Salmon 7 ). These methods provide higher resolution than simple counting, and by circumventing the computationally costly read alignment step, some are considerably faster.…”

An Evolutionarily Conserved Transcriptional Activator-Repressor Module Controls Expression of Genes for D-Galacturonic Acid Utilization inAspergillus niger

“…The RNA-seq reads were cleaned by correcting sequencing errors with Rcorrector (Song and Florea 2015), trimming sequencing adapters and low quality sequences with Skewer (Jiang et al 2014), and removing ribosomal RNA with SortMeRNA (Kopylova et al 2012). The cleaned reads were mapped to NRRL3 transcripts and counted with Salmon (Patro et al 2016), and the read counts were analyzed for differences in transcript expression between genotypes with DESeq2 (Love et al 2014).…”

“…RNA-seq reads were mapped to the NRRL3/N400 genome as this is the parent of the laboratory strain N402 and derivatives used in this study. TPM values were calculated using Salmon (Patro et al 2016) (Table S3). Analysis of differential gene expression, based on a stringent false discovery rate (FDR) ,0.001 and a fold change (FC) .4.0, identified 37 upregulated genes (Table 3).…”

Characterizing the reproductive transcriptomic correlates of acute dehydration in males in the desert-adapted rodent,Peromyscus eremicus

“…We then evaluated the assembly's structural integrity with Transrate and assessed completeness using the vertebrata database in BUSCO version 1.1b1 [64]. We quasimapped the raw reads to the assembly with Salmon version 0.7.2 [65] to confirm that mapping rates were high. Finally, the assembly was also annotated in dammit version 0.3.2, which finds open reading frames with TransDecoder and uses five databases (Rfam, Pfam, OrthoDB, BUSCO, and Uniref90) to thoroughly annotate transcripts (https://github.com/ camillescott/dammit).…”