MOTIVATION: RNA-Seq technology is promising to uncover many novel alternative 
splicing events, gene fusions and other variations in RNA transcripts. For an 
accurate detection and quantification of transcripts, it is important to resolve 
the mapping ambiguity for those RNA-Seq reads that can be mapped to multiple 
loci: >17% of the reads from mouse RNA-Seq data and 50% of the reads from some 
plant RNA-Seq data have multiple mapping loci. In this study, we show how to 
resolve the mapping ambiguity in the presence of novel transcriptomic events 
such as exon skipping and novel indels towards accurate downstream analysis. We 
introduce ORMAN ( O ptimal R esolution of M ultimapping A mbiguity of R N A-Seq 
Reads), which aims to compute the minimum number of potential transcript 
products for each gene and to assign each multimapping read to one of these 
transcripts based on the estimated distribution of the region covering the read. 
ORMAN achieves this objective through a combinatorial optimization formulation, 
which is solved through well-known approximation algorithms, integer linear 
programs and heuristics.
RESULTS: On a simulated RNA-Seq dataset including a random subset of transcripts 
from the UCSC database, the performance of several state-of-the-art methods for 
identifying and quantifying novel transcripts, such as Cufflinks, IsoLasso and 
CLIIQ, is significantly improved through the use of ORMAN. Furthermore, in an 
experiment using real RNA-Seq reads, we show that ORMAN is able to resolve 
multimapping to produce coverage values that are similar to the original 
distribution, even in genes with highly non-uniform coverage.
AVAILABILITY: ORMAN is available at http://orman.sf.net