Mapping RNA‐seq Reads with STAR

By Alexander Dobin, Thomas R. Gingeras. Current Protocols in Bioinformatics, 2015

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Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods (2019, background): “…that we developed, STAR-Fusion and TrinityFusion: STAR-Fusion leverages chimeric and discordant read alignments identified by the STAR aligner [18, 51, 52] to predict fusions and TrinityFusion leverages chimeric reads and Trinity de novo transcriptome assembly [48, 53] to reconstruct fusion…”
Expression Atlas: gene and protein expression across multiple studies and organisms (2017, methodology): “The tools employed for genome alignment, quantification and differential expression (Tophat2 (16)/Star (17), HTSeq (18), DESeq2 (19)) remain the same, as described in the previous update (1), although they have been updated to their latest versions.”
Single-Cell RNA-Seq Technologies and Related Computational Data Analysis (2019, methodology): “Currently popular aligners like TopHat2 (Kim et al., 2013), STAR (Dobin and Gingeras, 2015), and HISAT (Kim et al., 2015) perform well in mapping speed and accuracy, and they can eﬃciently map billions of reads to the reference genome or transcriptome ( Table 2 ).”