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. 2013 Jan 1;29(1):15-21.
doi: 10.1093/bioinformatics/bts635. Epub 2012 Oct 25.

STAR: ultrafast universal RNA-seq aligner

Alexander Dobin  1 , Carrie A Davis, Felix Schlesinger, Jorg Drenkow, Chris Zaleski, Sonali Jha, Philippe Batut, Mark Chaisson, Thomas R Gingeras
Affiliations

STAR: ultrafast universal RNA-seq aligner

Alexander Dobin et al. Bioinformatics. .

Abstract

Motivation: Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases.

Results: To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy.

Availability and implementation: STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

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Figures

Fig. 1.
Fig. 1.
Schematic representation of the Maximum Mappable Prefix search in the STAR algorithm for detecting (a) splice junctions, (b) mismatches and (c) tails
Fig. 2.
Fig. 2.
True-positive rate versus false-positive rate (ROC-curve) for simulated RNA-seq data for STAR, TopHat2, GSNAP, RUM and MapSplice
Fig. 3.
Fig. 3.
Various accuracy metrics for splice junction detection in the experimental RNA-seq data. The color-coding scheme for mappers is the same in all plots. X-axis in plots (a), (b), (d) and (e) is the detection threshold defined as the number of reads mapped across each junction, i.e. each point with the X-value of N represents all junctions that are supported by at least N reads mapped by a given aligner. (a) Total number of detected junctions, annotated (solid lines) and unannotated (dashed lines); (b) percentage of detected junctions that are annotated; (c) pseudo-ROC curve: percentage of all annotated junctions that are detected versus percentage of detected junctions that are unannotated; (d) number of unannotated junctions detected by at least two mappers (solid lines) and number of unannotated junctions detected exclusively by only one mapper (dashed lines); (e) percentage of detected unannotated junctions that are detected exclusively by only one mapper and (f) pseudo-ROC curve: percentage of unannotated junctions that are detected by at least two mappers versus percentage of detected unannotated junctions that are detected exclusively by only one mapper
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