Author(s): Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C
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/.
Author(s): Lacalle RA, Pulido D, Vara J, Zaiacaín M, Jiménez A
Author(s): Lanza AM, Kim DS, Alper HS
Author(s): Lee YT, de Vasconcellos JF, Yuan J, Byrnes C, Noh S, et al.
Author(s): Ali N, Karlsson C, Aspling M, Hu G, Hacohen N, et al.
Author(s): Sims D, Mendes-Pereira AM, Frankum J, Burgess D, Cerone MA
Author(s): de Vasconcellos JF, Lee YT, Byrnes C, Tumburu L, Miller JL
Author(s): Lee YT, de Vasconcellos JF, Byrnes C, Kaushal M, Miller JL
Author(s): Moran DM, Shen H, Maki CG