The StringTie Approach to Reconstructing the Transcriptome from RNA-Seq Data

Sequencing of mRNA through RNA-seq has led to an explosion of projects to characterize transcribed genes in multiple cell types and conditions, and simultaneously to discover new genes and new isoforms of known genes.  These sequencing-based studies generate enormous amounts of data, which in turn require sophisticated, efficient, and innovative new algorithms to assemble these genomes and identify their gene content. Previous efforts to model genes de novo, via recognition of splice sites, coding regions, and other signals, have been superseded by more accurate methods based on RNA-seq data.

A typical RNA-seq run can generate over 200 million short reads, and most experiments include multiple replicates. Assembling these short reads into full transcripts is a complex task: not only do different transcripts have highly variable sequence coverage, but also alternative transcripts from the same locus typically share exons that are difficult to assemble unambiguously.  Although a growing number of methods have been developed to solve the transcript identification and quantification problems, much work remains to produce consistent, highly accurate solutions, especially as the quality of RNA-seq data is constantly changing.

Here we introduce a new transcript assembly algorithm, StringTie, which uses a combination of de novo assembly ideas and a novel application of a network flow algorithm, a method imported from other areas of computer science research.  As compared to Cufflinks, StringTie produces more complete reconstructions of genes and splice variants from RNA-seq data.  One key advantage of StringTie’s algorithm is that it assembles and quantifies gene transcripts simultaneously.