Improved transcriptome assembly using a hybrid of long and short reads with StringTie

• Published in: PLoS computational biology Vol. 18; no. 6; p. e1009730
• Main Authors: Shumate, Alaina, Wong, Brandon, Pertea, Geo, Pertea, Mihaela
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.06.2022; Public Library of Science (PLoS)
• Subjects: Algorithms; Animals; Biology and Life Sciences; Earth Sciences; Exons; High-Throughput Nucleotide Sequencing; Humans; Mice; Research and Analysis Methods; Sequence Analysis, DNA; Sequence Analysis, RNA; Software; Transcriptome - genetics
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1009730

Short-read RNA sequencing and long-read RNA sequencing each have their strengths and weaknesses for transcriptome assembly. While short reads are highly accurate, they are rarely able to span multiple exons. Long-read technology can capture full-length transcripts, but its relatively high error rate often leads to mis-identified splice sites. Here we present a new release of StringTie that performs hybrid-read assembly. By taking advantage of the strengths of both long and short reads, hybrid-read assembly with StringTie is more accurate than long-read only or short-read only assembly, and on some datasets it can more than double the number of correctly assembled transcripts, while obtaining substantially higher precision than the long-read data assembly alone. Here we demonstrate the improved accuracy on simulated data and real data from Arabidopsis thaliana , Mus musculus , and human. We also show that hybrid-read assembly is more accurate than correcting long reads prior to assembly while also being substantially faster. StringTie is freely available as open source software at https://github.com/gpertea/stringtie .