Comprehensive benchmarking of RNA-Seq aligners

Alignment is the first step in most RNA-seq analysis pipelines, and the accuracy of downstream analyses depends heavily on it. Unlike most steps in the pipeline, alignment is particularly amenable to benchmarking with simulated data.

Researchers from the University of Padova and the University of Pennsylvania performed a comprehensive benchmarking of 14 common splice-aware aligners for base, read, and exon junction-level accuracy and compared default with optimized parameters. They also examine execution time and memory usage; differential behavior at canonical versus noncanonical junctions; the effect of untrimmed adapters; performance on indels, reads that map to multiple sites (multimappers); and other factors. They found that performance varied by genome complexity, and accuracy and popularity were poorly correlated. The most widely cited tool underperforms for most metrics, particularly when using default settings.

The effect of tuning parameters on the human-T3-data base-level statistics

rna-seq

For each tool, the figure shows the alignment statistics for the ‘default’ (d) and the ‘tuned’ (t) alignments.

Baruzzo G, Hayer KE, Kim EJ, Di Camillo B, FitzGerald GA, Grant GR. (2016) Simulation-based comprehensive benchmarking of RNA-seq aligners. Nat Methods [Epub ahead of print]. [abstract]

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