GPB Gao Ge's research group has made progress in the integration and analysis of non-coding variants related to human regulation

Although 97% of the regions in the human genome do not encode proteins, they still have functions that cannot be ignored.

Through systematic mining and integration of published literature, the research team extracted and collected more than 11.

On this basis, the research team further evaluated 7 mainstream non-coding mutation prediction tools based on high-quality mutation data sets, and found that the sensitivity of the corresponding tools still needs to be improved.

Figure 1: Overview of the structure of the REVA database and partial results of the tool evaluation

Currently, the REVA database has been officially released online (http://reva.

The REVA database will continue to be updated.

Doctoral students Wang Yu and Shi Fangyuan from the School of Life Sciences of Peking University are the co-first authors of the paper, researcher Gao Ge is the corresponding author, and Liang Yu, a master of the School of Life Sciences of Nanchang University, provided strong support in data collection

Link to the paper: https://　　Recommended translation of magazine articles (Chinese):

　　REVA: A high-quality database of non-coding variants related to human expression regulation