<p><em>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. To align our large (&gt;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 &gt;50 in mapping speed,  aligning to the human genome 550 million 2 &times; 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&ndash;90% success rate, corroborating  the                      high precision of the STAR mapping strategy.</em></p>