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Geo Pertea edited this page Jul 27, 2026 · 6 revisions

StringTie
Transcript assembly and quantification for RNA-Seq

Overview

StringTie is a fast and highly efficient assembler of RNA-Seq alignments into potential transcripts. It uses a novel network flow algorithm as well as an optional de novo assembly step to assemble and quantitate full-length transcripts representing multiple splice variants for each gene locus. Its input can include not only alignments of short reads that can also be used by other transcript assemblers, but also alignments of longer sequences that have been assembled from those reads. In order to identify differentially expressed genes between experiments, StringTie's output can be processed by specialized software like Ballgown, Cuffdiff or other programs (DESeq2, edgeR, etc.).

Obtaining and installing StringTie

The current version of StringTie can be downloaded from GitHub:

git clone https://github.com/gpertea/stringtie
cd stringtie
make release

For evaluating and further processing the GTF output of StringTie, utilities like gffcompare or gffread can be used.

Licensing and Contact Information

StringTie is free, open source software released under an MIT License.

For technical issues, bug reports and code contributions please use StringTie's GitHub repository.

Publications

Shumate A, Wong B, Pertea G, Pertea M Improved transcriptome assembly using a hybrid of long and short reads with StringTie, PLOS Computational Biology 18, 6 (2022), doi.org/10.1371/journal.pcbi.1009730

Kovaka S, Zimin AV, Pertea GM, Razaghi R, Salzberg SL, Pertea M Transcriptome assembly from long-read RNA-seq alignments with StringTie2, Genome Biology 20, 278 (2019), doi:10.1186/s13059-019-1910-1

Pertea M, Kim D, Pertea GM, Leek JT, Salzberg SL Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown, Nature Protocols 11, 1650-1667 (2016), doi:10.1038/nprot.2016.095

Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT & Salzberg SL. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads Nature Biotechnology 2015, doi:10.1038/nbt.3122

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