As next generation sequencing technologies are getting more efficient and less 
expensive, RNA-Seq is becoming a widely used technique for transcriptome 
studies. Computational analysis of RNA-Seq data often starts with the mapping of 
millions of short reads back to the genome or transcriptome, a process in which 
some reads are found to map equally well to multiple genomic locations 
(multimapping reads). We have developed the Minimum Unique Length Tool (MULTo), 
a framework for efficient and comprehensive representation of mappability 
information, through identification of the shortest possible length required for 
each genomic coordinate to become unique in the genome and transcriptome. Using 
the minimum unique length information, we have compared different uniqueness 
compensation approaches for transcript expression level quantification and 
demonstrate that the best compensation is achieved by discarding multimapping 
reads and correctly adjusting gene model lengths. We have also explored 
uniqueness within specific regions of the mouse genome and enhancer mapping 
experiments. Finally, by making MULTo available to the community we hope to 
facilitate the use of uniqueness compensation in RNA-Seq analysis and to 
eliminate the need to make additional mappability files.