TopHat

TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. TopHat is a popular spliced aligner for RNA-sequence (RNA-seq) experiments. In this paper, we describe TopHat2, which incorporates many significant enhancements to TopHat. TopHat2 can align reads of various lengths produced by the latest sequencing technologies, while allowing for variable-length indels with respect to the reference genome. In addition to de novo spliced alignment, TopHat2 can align reads across fusion breaks, which can occur after genomic translocations. TopHat2 combines the ability to identify novel splice sites with direct mapping to known transcripts, producing sensitive and accurate alignments, even for highly repetitive genomes or in the presence of pseudogenes. TopHat2 is available at http://ccb.jhu.edu/software/tophat.


References in zbMATH (referenced in 20 articles )

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  1. 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
  2. Li, Wei Vivian; Zhao, Anqi; Zhang, Shihua; Li, Jingyi Jessica: MSIQ: joint modeling of multiple RNA-seq samples for accurate isoform quantification (2018)
  3. Song, Wei; Liu, Huaping; Wang, Jiajia; Kong, Yan; Yin, Xia; Zang, Weidong: MATHT: a web server for comprehensive transcriptome data analysis (2018)
  4. Teixeira, Andreia Sofia; Fernandes, Francisco; Francisco, Alexandre P.: SpliceTAPyR -- an efficient method for transcriptome alignment (2018)
  5. von Stechow, Louise (ed.); Delgado, Alberto Santos (ed.): Computational cell biology. Methods and protocols (2018)
  6. Wolff, Alexander: Analysis of expression profile and gene variation via development of methods for next generation sequencing data (2018)
  7. Yuan, Fei; Lu, Lin; Zhang, YuHang; Wang, ShaoPeng; Cai, Yu-Dong: Data mining of the cancer-related lncRNAs GO terms and KEGG pathways by using mRMR method (2018)
  8. Zhao, Lili; Wu, Weisheng; Feng, Dai; Jiang, Hui; Nguyen, Xuanlong: Bayesian analysis of RNA-Seq data using a family of negative binomial models (2018)
  9. Keith, Jonathan M. (ed.): Bioinformatics. Volume II: structure, function, and applications (2017)
  10. Papastamoulis, Panagiotis; Rattray, Magnus: Bayesian estimation of differential transcript usage from RNA-seq data (2017)
  11. Mathé, Ewy (ed.); Davis, Sean (ed.): Statistical genomics. Methods and protocols (2016)
  12. Pietrzak, Maciej; Rempała, Grzegorz A.; Seweryn, Michał; Wesołowski, Jacek: Limit theorems for empirical Rényi entropy and divergence with applications to molecular diversity analysis (2016)
  13. Ruddy, Sean; Johnson, Marla; Purdom, Elizabeth: Shrinkage of dispersion parameters in the binomial family, with application to differential exon skipping (2016)
  14. Axelson-Fisk, Marina: Comparative gene finding. Models, algorithms and implementation (2015)
  15. Picardi, Ernesto (ed.): RNA bioinformatics (2015)
  16. Papastamoulis, Panagiotis; Hensman, James; Glaus, Peter; Rattray, Magnus: Improved variational Bayes inference for transcript expression estimation (2014)
  17. Rossell, David; Attolini, Camille Stephan-Otto; Kroiss, Manuel; Stöcker, Almond: Quantifying alternative splicing from paired-end RNA-sequencing data (2014)
  18. Cox, Anthony J.; Jakobi, Tobias; Rosone, Giovanna; Schulz-Trieglaff, Ole B.: Comparing DNA sequence collections by direct comparison of compressed text indexes (2012)
  19. Salzman, Julia; Jiang, Hui; Wong, Wing Hung: Statistical modeling of RNA-Seq data (2011)
  20. Trapnell, Cole; Pachter, Lior; Salzberg, Steven L.: Tophat: discovering splice junctions with RNA-seq (2009) ioport