Sungkyunkwan University, School of Medicine. A study on distribution of recurrent mutation across different cancer types within regulatory DNA motifs/segments of human genome. Feb. 9 (Thu) 15:00 / Room 476, PBC Ambarnil Ghosh Sungkyunkwan University, School of Medicine. Tremendous advancement in DNA sequencing technology has enabled large scale identification of mutations specific to a particular disease or patient in TCGA pan-cancer analyses era. Up to date, most of the significant mutations are counted on the basis of protein coding regions and its consequent potential to manipulate open reading frame. Deciphering the functional role of the mutations occurred within noncoding regions remains a monumental challenge. Several studies were published to focus noncoding mutations within the genome but prioritizing important target region is a challenging task. Currently, in our computational biology based data mining work, a thorough mapping of the mutations in the regulatory region of the genome is under progress from more than 11,000 cancer patient genome. Altogether, 33 types of TCGA cancer types and their GDC server analyzed data are considered for this analyses. The goal of this study is to identify the mutations in the non-coding regulatory regions in the human genome associated with specific cancer. Genome wide mapping of regulatory regions are performed by prediction of the regulatory signatures like: transcription factor binding motifs, G-quadruplex motif, Z-DNA motif, Cruciform motifs, I-Motifs, super enhancer regions, high-density ChIP-Seq regions, etc. The regulatory region mapping is followed by the intersection with recurrent mutation maps to obtain our final result. General concept of mutational-effect lies in the disruption of very important regulatory signature. Beside disruption, we are also considering regulatory signature forming pattern or more specifically motif-forming recurrent mutations. We generally tag a gene or a regulatory region to a particular mutation: by data mining the link to a cellular event or by tagging a gene as a promoter or by tagging a super-enhance regions, etc. Therefore, the analyses of these recurrent mutations those are occurring in the non-protein coding regulatory regions can enhance our understanding of the unusual transcriptional regulatory networks of the cancer genome. This study will also increase the possibility of finding new candidates involving in cancer progression. This pipeline of genome-wide analysis of mutations can be applied separately to different disease to decipher the role of novel recurrent mutations in the non-coding regions. Further analysis would lead to the identification of clinically significant mutations that can be used as novel drug-targets for therapeutic purpose. Inquiry: Dr. Dipayan Rudra (279-9865) or AIM Administrative Team (Tel.279-8628, E-mail: varsha@ibs.re.kr)