STAR

STAR: ultrafast universal RNA-seq aligner. 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/.


References in zbMATH (referenced in 11 articles )

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  1. Acuña, V.; Grossi, R.; Italiano, G. F.; Lima, L.; Rizzi, R.; Sacomoto, G.; Sagot, M.-F.; Sinaimeri, B.: On bubble generators in directed graphs (2020)
  2. Kuan-Hao Chao, Yi-Wen Hsiao, Yi-Fang Lee, Chien-Yueh Lee, Liang-Chuan Lai, Mong-Hsun Tsai, Tzu-Pin Lu, Eric Y. Chuang: RNASeqR: an R package for automated two-group RNA-Seq analysis workflow (2019) arXiv
  3. Teixeira, Andreia Sofia; Fernandes, Francisco; Francisco, Alexandre P.: SpliceTAPyR -- an efficient method for transcriptome alignment (2018)
  4. Wolff, Alexander: Analysis of expression profile and gene variation via development of methods for next generation sequencing data (2018)
  5. Faisal, Shahla; Tutz, Gerhard: Missing value imputation for gene expression data by tailored nearest neighbors (2017)
  6. Fu, Rong; Wang, Pei; Ma, Weiping; Taguchi, Ayumu; Wong, Chee-Hong; Zhang, Qing; Gazdar, Adi; Hanash, Samir M.; Zhou, Qinghua; Zhong, Hua; Feng, Ziding: A statistical method for detecting differentially expressed SNVs based on next-generation RNA-seq data (2017)
  7. Mao, Shunfu; Mohajer, Soheil; Ramachandran, Kannan; Tse, David; Kannan, Sreeram: abSNP: RNA-Seq SNP calling in repetitive regions via abundance estimation (2017)
  8. Shomroni, Orr: Development of algorithms and next-generation sequencing data workflows for the analysis of gene regulatory networks (2017)
  9. Carugo, Oliviero (ed.); Eisenhaber, Frank (ed.): Data mining techniques for the life sciences (2016)
  10. Mathé, Ewy (ed.); Davis, Sean (ed.): Statistical genomics. Methods and protocols (2016)
  11. Picardi, Ernesto (ed.): RNA bioinformatics (2015)