suitable aligner for human RNA-seq

[yueli]

Hello,

Human RNA-seq dataset was generated from Illumina HiSeq 3000 with 2X100 cycles run.

The first step is making alignment of the reads to the human genome. These are many aligner, such as: Bowtie, GASSST, PASS, SOAP, BOAT. Each aligners has different performs in different kinds of data.

Which is the best suitable aligner for RNA-seq data?

Thank you in advance for great help!

Sincerely,

Yue

[colindaven]

STAR or Hisat2, and NOT Tophat2

[ashishbansal]

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.