title: StringTie enables improved reconstruction of a transcriptome from RNA-seq reads.
abstract: Methods used to sequence the transcriptome often produce more than 200 million short sequences. We introduce StringTie, a computational method that applies a network flow algorithm originally developed in optimization theory, together with optional de novo assembly, to assemble these complex data sets into transcripts. When used to analyze both simulated and real data sets, StringTie produces more complete and accurate reconstructions of genes and better estimates of expression levels, compared with other leading transcript assembly programs including Cufflinks, IsoLasso, Scripture and Traph. For example, on 90 million reads from human blood, StringTie correctly assembled 10,990 transcripts, whereas the next best assembly was of 7,187 transcripts by Cufflinks, which is a 53% increase in transcripts assembled. On a simulated data set, StringTie correctly assembled 7,559 transcripts, which is 20% more than the 6,310 assembled by Cufflinks. As well as producing a more complete transcriptome assembly, StringTie runs faster on all data sets tested to date compared with other assembly software, including Cufflinks.
authors.affiliation: 1] Center for Computational Biology, Johns Hopkins University, Baltimore, Maryland, USA. [2] McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
authors.forename: Mihaela
authors.initials: M
authors.lastname: Pertea
authors.affiliation: 1] Center for Computational Biology, Johns Hopkins University, Baltimore, Maryland, USA. [2] McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
authors.forename: Geo M
authors.initials: GM
authors.lastname: Pertea
authors.affiliation: 1] Center for Computational Biology, Johns Hopkins University, Baltimore, Maryland, USA. [2] McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
authors.forename: Corina M
authors.initials: CM
authors.lastname: Antonescu
authors.affiliation: 1] Department of Molecular Biology, The University of Texas Southwestern Medical Center, Dallas, Texas, USA. [2] Center for Regenerative Science and Medicine, The University of Texas Southwestern Medical Center, Dallas, Texas, USA.
authors.forename: Tsung-Cheng
authors.initials: TC
authors.lastname: Chang
authors.affiliation: 1] Department of Molecular Biology, The University of Texas Southwestern Medical Center, Dallas, Texas, USA. [2] Center for Regenerative Science and Medicine, The University of Texas Southwestern Medical Center, Dallas, Texas, USA. [3] Simmons Cancer Center, The University of Texas Southwestern Medical Center, Dallas, Texas, USA.
authors.forename: Joshua T
authors.initials: JT
authors.lastname: Mendell
authors.affiliation: 1] Center for Computational Biology, Johns Hopkins University, Baltimore, Maryland, USA. [2] McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, USA. [3] Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA. [4] Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA.
authors.forename: Steven L
authors.identifier: 0000000288597432
authors.initials: SL
authors.lastname: Salzberg
mesh_terms: D000465:Algorithms; D057809:HEK293 Cells; D006801:Humans; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software; D059467:Transcriptome / Q000235:genetics*
chemical_list: D012333:RNA, Messenger