Abstract
Genomic experiments produce large sets of data, many of which are publicly available. Investigating these datasets using bioinformatics data mining techniques may reveal novel biological knowledge. We developed a bioinformatics pipeline to investigate Chip-seq DNA binding proteins datasets for HepG2 liver cancer cell line downloaded from ENCODE project. Of 276 datasets, 175 passed our proposed quantity control testing. A pair-wise DNA co-location analysis tool developed by us revealed a cluster of 19 proteins significantly collocating on DNA binding regions. The results were confirmed by tools from other labs. Narrowing down our bioinformatics analysis showed a strong enrichment of DNA-binding protein SIN3A to activator (H3K79me2) and repressor (H3K27me3) indicating SIN3A plays has an important regulatory role in vital liver functions. Whether increased enrichment varies in liver infection we compared histone modification between HepG2 and HepG2.2.15 cells (HepG2 derived hepatitis B virus (HBV) expressing stable cells) and observed an increase SIN3A enrichment in promoter regions (H3K4me3) confirming a known biological phenotype. The mechanistic role of SIN3A protein in case of liver injury or insult during liver infection warrants further dry and wet lab investigations.
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Khushi, M., Naseem, U., Du, J., Khan, A., Poon, S.K. (2020). Data Mining ENCODE Data Predicts a Significant Role of SINA3 in Human Liver Cancer. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12534. Springer, Cham. https://doi.org/10.1007/978-3-030-63836-8_2
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