BACKGROUND: Through transcription and alternative splicing, a gene can be 
transcribed into different RNA sequences (isoforms), depending on the 
individual, on the tissue the cell is in, or in response to some stimuli. Recent 
RNA-Seq technology allows for new high-throughput ways for isoform 
identification and quantification based on short reads, and various methods have 
been put forward for this non-trivial problem.
RESULTS: In this paper we propose a novel radically different method based on 
minimum-cost network flows. This has a two-fold advantage: on the one hand, it 
translates the problem as an established one in the field of network flows, 
which can be solved in polynomial time, with different existing solvers; on the 
other hand, it is general enough to encompass many of the previous proposals 
under the least sum of squares model. Our method works as follows: in order to 
find the transcripts which best explain, under a given fitness model, a splicing 
graph resulting from an RNA-Seq experiment, we find a min-cost flow in an offset 
flow network, under an equivalent cost model. Under very weak assumptions on the 
fitness model, the optimal flow can be computed in polynomial time. 
Parsimoniously splitting the flow back into few path transcripts can be done 
with any of the heuristics and approximations available from the theory of 
network flows. In the present implementation, we choose the simple strategy of 
repeatedly removing the heaviest path.
CONCLUSIONS: We proposed a new very general method based on network flows for a 
multiassembly problem arising from isoform identification and quantification 
with RNA-Seq. Experimental results on prediction accuracy show that our method 
is very competitive with popular tools such as Cufflinks and IsoLasso. Our tool, 
called Traph (Transcrips in gRAPHs), is available at: 
http://www.cs.helsinki.fi/gsa/traph/.