Sequence-to-expression approach to identify etiological non-coding DNA variations in P53 and cMYC-driven diseases [RNA-seq]
Most DNA variants associated with common complex diseases fall outside the protein-coding regions of the genome, making them hard to detect and relate to a function. Although many computational tools are available for prioritizing functional disease risk variants outside the protein-coding regions of the genome, the precision of prediction of these tools is mostly unreliable and hence not close to cancer risk prediction. This study brings to light a novel way to improve prediction accuracy of publicly available tools by integrating the impact of cis-overlapping binding sites of opposing cancer proteins, such as P53 and cMYC, in their analysis to filter out deleterious DNA variants outside the protein-coding regions of the human genome. Using a biology-based statistical approach, DNA variants within cis-overlapping motifs impacting the binding affinity of opposing transcription factors can significantly alter the expression of target genes and regulatory networks. This study brings us closer to developing a generally applicable approach capable of filtering etiological non-coding variations in co-occupied genomic regions of P53 and cMYC family members to improve disease risk assessment. Overall design: RNA-Seq analysis was conducted on untreated and treated Raji and U2OS cells with doxorubicin to identify differentially expressed genes after treatment. The total RNA samples extracted from cancer cells were tested for RNA quality and concentration. Illumina HiSeq2500 platform in a 2x100bp paired-end configuration was used to obtain 35 million reads on average for each library sample.