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.

Figure 1

Transcript assembly pipelines for StringTie, Cufflinks and Traph. (a) Overview of the flow of the StringTie algorithm, compared to Cufflinks and Traph. All methods begin with a set of RNA-seq reads that have been mapped to the genome. An optional secondary input to StringTie is a set of pre-assembled super-reads, designated as StringTie+SR. StringTie iteratively extracts the heaviest path from a splice graph, constructs a flow network, computes maximum flow to estimate abundance, and then updates the splice graph by removing reads that were assigned by the flow algorithm. This process repeats until all reads have been assigned. (b) Annotated transcript T for which read data covers only the fragments F1 and F2. An assembler is given credit for a correct reconstruction of T if it correctly assembles F1 and F2.

Figure 2

Transcriptome assemblers’ accuracies in detecting expressed transcripts from two simulated RNA-seq data sets. (a) Transcriptome assemblers’ accuracies in detecting expressed transcripts from two simulated RNA-seq data sets. In data set Sim-I (left), the fragment sizes follow an empirical distribution based on Illumina sequences, and in Sim-II (right) the fragment sizes follow a parameterized normal distribution. StringTie+SR pre-assembles the reads into super-reads when possible. (b) Accuracy of transcriptome assemblers on gene loci from the same two data sets, considering only those transcripts that were completely covered by input reads. Scripture’s precision on Sim-I was 17.7%, below the 20% minimum shown here.

Figure 3

Accuracy of transcript assemblers at assembling known genes, measured on real data sets from four different tissues. Known genes are defined as those annotated in either the RefSeq, UCSC or Ensembl human gene databases. Gene level sensitivity (y axis) measures the percentage of genes for which a program got at least one isoform correct, whereas transcript sensitivity measures the percentage of known transcripts that were correctly assembled. Precision (x axis) is measured as the percentage of all predicted genes (transcripts) that match an annotated gene (transcript).