Presentation Information
[PE2-11]ATAC-seq shape information clustering unfolds biological insight
○Jianhao Cheng, Cheng Zheng, Ryo Yamada, Daigo Okada (Center for Genomic Medicine Graduate School of Medicine, Kyoto University Kyoto Japan)
Backgrounds: Epigenomic status is believed to characterize cellular conditions including their differentiation stages and their adaptations to the macro or micro environment. The next generation sequencer-based information of epigenetic features the depth of reads in the form of function along the genome, that represents the degree of openness of the chromatin structure accessibility of molecules to DNA sequence by the transcriptional machinery molecules and their regulatory factors. Although there are multiple analytical methods available for it so far, the majority methods only focus on the intensity value and detect the enriched region (peaks) in the genome. Other features of sequencing shape are often neglected by the researchers.Methods and Results: To clarify the meaning of ATAC-seq sequencing shape, we applied the proposed method to public ATAC-seq dataset. We hypothesize that significant difference in ATAC-seq shape may have a functional role and some biological meaning. Hence, we proposed a method to quantify the dissimilarity of the sequencing shapes of genome-wide genes, then embed them into a low dimensional space and extract the feature values from them. After that we employed clustering algorithm to classified genes to several groups based on their shape information. Then within each group, we performed gene set analysis and identified the associated biological function.Conclusions: In summary, we have shown that ATAC-seq shape information provides new insights into transcription process.Keywords: ATAC-seq, shape information.