A Novel Approach for Mapping Ambiguous Sequences of Transcriptomes

An organism’s transcriptome is the set of all transcripts within a cell at a certain time. We often analyze the transcriptome by quantifying gene expression and performing subsequent analyses such as a differential expression or a network analysis. Such analysis helps us in understanding and interpreting the functional elements of the genome. Many challenges limit the accuracy and ability to map all the RNA-Seq correctly into its genome sequence. Some of these challenges are exemplified when mapping sequences fall at exon junctions, sequences containing polymorphisms, multiple insertions or deletions, and reads falling partially or wholly within introns. One of the most significant problems is the loss of data occurring from the inability to map sequences when they align to multiple genomic locations, sometimes called ambiguous sequence mappings. In this paper, we present a novel method to increase the accuracy of gene expression estimation by relying on a statistical approach to increase the accuracy of mapping the ambiguous reads to their proper locations within the genome. This approach allows us to better identify significantly expressed genomic locations so we can accurately map ambiguous reads to their most likely accurate genomic locations and to define more precisely which genes are expressed throughout the genome. Due to its statical nature the approach can be easily combined with other existing mapping tools and mechanisms as well.