  Transcriptome assembly from RNA-Seq reads is an active area of bioinformatics
research. The ever-declining cost and the increasing depth of RNA-Seq have
provided unprecedented opportunities to better identify expressed transcripts.
However, the nonlinear transcript structures and the ultra-high throughput of
RNA-Seq reads pose significant algorithmic and computational challenges to the
existing transcriptome assembly approaches, either reference-guided or de novo.
While reference-guided approaches offer good sensitivity, they rely on
alignment results of the splice-aware aligners and are thus unsuitable for
species with incomplete reference genomes. In contrast, de novo approaches do
not depend on the reference genome but face a computational daunting task
derived from the complexity of the graph built for the whole transcriptome. In
response to these challenges, we present a hybrid approach to exploit an
incomplete reference genome without relying on splice-aware aligners. We have
designed a split-and-align procedure to efficiently localize the reads to
individual genomic loci, which is followed by an accurate de novo assembly to
assemble reads falling into each locus. Using extensive simulation data, we
demonstrate a high accuracy and precision in transcriptome reconstruction by
comparing to selected transcriptome assembly tools. Our method is implemented
in assemblySAM, a GUI software freely available at
http://sammate.sourceforge.net.
