StringTie enables improved reconstruction of a transcriptome from RNA-seq reads

By Mihaela Pertea, Geo Pertea, Corina Antonescu, Tsung-Cheng Chang, Joshua T. Mendell, Steven L. Salzberg. Nature Biotechnology, 2015

11,722 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d2fabce7e313869f39d4c6fff8bab6e463c21e9f

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Citations in the corpus

Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi (2017, methodology): “Interestingly, purely RNA-seq-based pipelines also yield the worst gene/transcript sensitivity and specificity for C. elegans.Comparing the results based on different transcript assemblers, we find that the results based on StringTie are better than those based on Cufflinks for A. thaliana and C. el”
Host-Viral Infection Maps Reveal Signatures of Severe COVID-19 Patients (2020, positive): “Due to the lack of high-quality viral genome annotations, Viral-Track includes de novo transcriptome assembly of the identified viruses using StringTie (Pertea et al., 2015).”
Improved transcriptome assembly using a hybrid of long and short reads with StringTie (2021, methodology): “As previously described, StringTie takes as input an alignment file of all reads from a sample in either SAM or BAM format [14], uses these alignments to create a splice graph, and then assembles transcripts by iterating through two steps: first, it identifies the heaviest path in the splicing graph”
Sequence Analysis (2020, neutral): “…(ONT), have made it possible to perform transcriptome analysis at the isoform level and accurately predict full-length novel transcripts (Au et al. 2013, Sharon et al. 2013, Tilgner et al. 2014, Pertea et al. 2015, Kuo et al. 2020, Nip et al. 2020, Tang et al. 2020, Prjibelski et al. 2023).”
A survey of best practices for RNA-seq data analysis (2016, methodology): “Several methods, such as Cufflinks [23], iReckon [24], SLIDE [25] and StringTie [26], incorporate existing annotations by adding them to the possible list of isoforms.”
rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data (2018, neutral): “While reference-based methods for RNA-sequencing (RNA-Seq) analysis [1–6] are widely used in transcriptome studies, they are subjected to the following constraints: (i) they are not applicable when the genome is unknown, (ii) their performance deterio-rates when the genome sequence or annotation is…”
A complete Cannabis chromosome assembly and adaptive admixture for elevated cannabidiol (CBD) content (2018, methodology): “Pairwise hits were locally realigned with AAT (43) and Exonerate protein2genome.”
Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis (2017, methodology): “Here we focused on two widely used alignment-based transcriptome discovery tools, namely, Cuf ﬂ inks 16 and StringTie 17 .”
FEELnc: a tool for long non-coding RNA annotation and its application to the dog transcriptome (2017, background): “Following RNA sequencing, the computational reconstruction of transcripts models either by genome-guided (11,12) or de novo assembly (13) usually produces tens of thousands of known and novel transcript models.”