Sequence-to-expression approach to identify etiological non-coding DNA variations in P53 and cMYC-driven diseases [ChIP-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: ChIP-Seq analysis was conducted on untreated and treated Raji and U2OS cells with doxorubicin to identify genomic binding after treatment. Illumina HiSeq2500 platform in a 2x100bp paired-end configuration was used to obtain 15 million reads on average for each library sample.