Salmon accepts either raw (green arrows) or aligned (gray arrow) reads as input. When processing quasi-mappings or aligned reads,Salmon executes an online inference algorithm. This ensures that transcript abundance estimates are available to estimate weights for the rich equivalence classes, and to consider the appropriate conditional probabilities when learning the experimental parameters and foreground bias models. After a fragment’s contributions to the online abundance estimates and bias models have been computed, the fragment is placed into an appropriate equivalence class (or one is created if it does not yet exist). Once all of the fragments have been observed, the initial abundances and fragment equivalence classes are passed to the offline inference module. The offline module learns the background bias models (based on initial abundance estimates) and then corrects the effective transcript lengths to account for the appropriate biases. Finally, the offline inference algorithm (EM or VBEM) is run over the reduced representation of the data until convergence. Once estimation is complete, posterior samples are generated via Gibbs sampling or a bootstrap procedure if the user has requested this.

The false discovery rate (FDR) vs. the sensitivity ofSalmon,Salmon (align),kallisto andeXpress onPolyester simulated RNA-seq data using empirically-derived fragment GC bias profiles. All methods were run with bias-correction enabled, but onlySalmon’s model incorporates corrections for fragment GC bias. This leads to a large improvement in sensitivity at almost every FDR value.

The log2 fold change between the estimated and true abundances as a function of the true abundance (measured in TPM), for all methods and for all replicates of both simulated “conditions” (each row displays points from all samples within a given condition). The top row corresponds to the 8 samples simulated from the data showing the weak fragment GC content bias, while the bottom row corresponds to the 8 samples simulated from the data showing the stronger fragment GC content bias. Points with an estimated log2 fold change of > 0.5 or < -0.5 are colored red. The fraction of red points appears in the upper right-hand corner of each plot.Salmon consistently demonstrates log fold changes closer to 0 than eitherkallisto oreXpress, across most of the range of expression.

The distribution of the mean absolute error of (inverse hyperbolic sine-transformed) TPMs between different replicates of data from the SEQC [12] study. The A sample corresponds to universal human reference tissue (UHRR) and the B sample corresponds to human brain tissue (HBRR). When comparing the replicates that were sequenced at different centers, the inter-replicate distances are larger. However, we observe thatSalmon’s bias correction methodology results in improved consistency (i.e., reduced distances) compared to the estimates produced by other methods, especially when comparing replicates sequenced at different centers, where we expect the effects of bias to be more pronounced.

Transcripts demonstrating dominant isoform switching that results from technical bias. In the quantification estimates computed usingkallisto andeXpress, these two-isoform genes show a change in the dominant isoform between conditions (an asterisk denotes a t-test on log2(TPM+1) with p < 1×10⁻⁶). However,Salmon directly corrects for technical biases that appear to underlie differences across sequencing center, revealing that the dominant isoform has not, in fact, switched across center.

The distribution of Spearman correlations over all 20 replicates of theRSEM-sim data forSalmon,kallisto andeXpress.Salmon andkallisto yield very similar distributions of correlations (no statistically significant difference), while both methods yield correlations greater than that ofeXpress (Mann-Whitney U test, p = 3.39780 × 10⁻⁸).

The effect of the number of conditional GC models used to account for correlation between fragment GC and sequence-specific bias. We choose the default to be 3 bins; the simplest model that demonstrates the majority of the benefit. Panels a, b and c show the result of varying the number of conditional GC models on an analysis of the GEUVADIS data for all genes, all transcripts, and genes with only two transcripts, respectively.

Supplementary Figures 1–7, Supplementary Tables 1–4, Supplementary Notes 1 and 2, and Supplementary Algorithms 1 (PDF 1950 kb)