RNA-seq could detect single nucleotide polymorphism in coding genes

RNA-sequencing (RNA-seq) is perhaps the most popular and accessible method for probing the transcriptome of cells under different environmental and nutritional conditions. In a typical RNA-seq workflow, sample preparation and sequencing are intimately tied to data processing. Typically, RNA-seq data that comprise short reads are aligned to a reference genome to obtain relative gene expression pattern of the cell at a global level. Although conceptually simple and easy to implement, aligning short reads to a genome would result in a read inflation problem given a single mRNA transcript would engender multiple short reads during shotgun sequencing. This issue has been solved by an alternative approach of assembling short reads into full length transcripts whose abundance could subsequently be used to inform relative expression of different genes. Using this approach, one could gain a more accurate view of differential gene expression in a cell. However, there is an alternative use of this transcript assembly approach. Specifically, the full-length transcript could be aligned against a reference genome to identify single nucleotide polymorphisms in a particular gene. Repeating this at the global level could uncover a significant ensemble of single nucleotide polymorphisms in different genes. While only a subset of genes could be probed by RNA-seq experiments given that cells do not express all genes at the same time, the new view of RNA-seq data at the full-transcript level nevertheless represents an alternative use of hard-earned data. Overall, RNA-seq data represents a rich resource of data ready to be tapped for different uses. Besides informing the relative expression levels of different genes, assembly of short reads into full-length transcripts followed by subsequent alignment with coding genes in a reference genome might be able to uncover unique single nucleotide polymorphism in different genes.