STAR: ultrafast universal RNA-seq aligner

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/.

Figures

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.

True-positive rate versus false-positive rate (ROC-curve) for simulated RNA-seq data for STAR, TopHat2, GSNAP, RUM and MapSplice

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