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Using Big Data to Discover and Predict Disease Genes

Yonsei Biotechnology Professor In Suk Lee and his team are using big data to more effectively identify and predict disease genes

Yonsei Biotechnology Professor In Suk Lee and his research team are using big data to more effectively identify and predict disease genes. To this end, they have developed a new co-expression database called COEXPEDIA (www.coexpedia.org), which enables the identification of diseases and drugs previously unknown to be related to a gene or a gene group of interest. Currently, COEXPEDIA contains nearly eight million co-expressions inferred from both humans and mice, and it is open to researchers throughout the world. The research team has also developed a genome-scale co-functional network of zebrafish genes, DanioNet (www.inetbio.org/danionet). DanioNet allows for function-driven disease gene discovery in zebrafish, which offers a promising human disease model due to the fish’s high anatomical and genomic similarity to humans. In using rigorous statistical assessment, the team was able to confirm the high prediction capacity of DanioNet for a wide variety of human diseases; more specifically, the team demonstrated the feasibility of function-driven disease gene discovery using DanioNet by predicting genes for ciliopathies and performing experimental validation for eight candidate genes. This article, “Function-driven discovery of disease genes in zebrafish using an integrated genomics big data resource,” was published November 16 of 2016, also in Nucleic Acids Research.

Updated in Feb 2017

Reference Hongseok Shim, Ji Hyun Kim, Chan Yeong Kim, Sohyun Hwang, Hyojin Kim, Sunmo Yang, Ji Eun Lee, Insuk Lee, “Function-driven discovery of disease genes in zebrafish using an integrated genomics big data resource” Nucleic Acids Research, doi: https://doi.org/10.1093/nar/gkw897