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Background  The recent advance of high-throughput sequencing makes it feasible to study entire transcriptomes through the application of  de novo  sequence assembly algorithms. While a popular strategy is to first construct an intermediate de Bruijn graph structure to represent the transcriptome, an additional step is needed to construct predicted transcripts from the graph.    Results  Since the de Bruijn graph contains all branching possibilities, we develop a memory-efficient algorithm to recover alternative splicing information and library-specific expression information directly from the graph without prior genomic knowledge. We implement the algorithm as a postprocessing module of the Velvet assembler. We validate our algorithm by simulating the transcriptome assembly of  Drosophila  using its known genome, and by performing  Drosophila  transcriptome assembly using publicly available RNA-Seq libraries. Under a range of conditions, our algorithm recovers sequences and alternative splicing junctions with higher specificity than Oases or Trans-ABySS.    Conclusions  Since our postprocessing algorithm does not consume as much memory as Velvet and is less memory-intensive than Oases, it allows biologists to assemble large libraries with limited computational resources. Our algorithm has been applied to perform transcriptome assembly of the non-model blow fly  Lucilia sericata  that was reported in a previous article, which shows that the assembly is of high quality and it facilitates comparison of the  Lucilia sericata  transcriptome to  Drosophila  and two mosquitoes, prediction and experimental validation of alternative splicing, investigation of differential expression among various developmental stages, and identification of transposable elements.

A memory-efficient algorithm to obtain splicing graphs and de novoexpression estimates from de Bruijn graphs of RNA-Seq data