News.hust.edu.cn (Correspondent: Tan Chongyang)  On October 5, a research paper about Strain-GeMS (https://github.com/HUST-NingKang-Lab/straingems), a microbial subspecies identification method invented by Ning Kang, a professor from the College of Life Science & Technology, and his team, was published online in the Oxford University Press publication Bioinformatics (2017 impact factor of 5.481).

Professor Ning Kang is the corresponding author. Tan Chongyang, a graduate student of the College of Life Science & Technology, and Cui Wei, a lecturer from Nankai University, are the co-first authors. Based on ConStrains, an established subspecies identification method, Ning’s team provides a more accurate microbial subspecies identification method by integrating Multi-GeMS with SNP-flow, PAM, etc. They compared the simulation data of Strain-GeMS with those of other existing methods like ConStrains, Midas and Sigma to verify its accuracy in subspecies identification. Meanwhile, Strain-GeMS was applied to the data of the intestinal microorganisms carried by the Hadza people, revealing the seasonal variations of the emergence of subspecies.

It is known that many human diseases are related to a single microorganism or a group of microorganisms, such as type 2 diabetes (T2D), obesity, cancer and some immune-related diseases. Different strains in the same species may have completely different effects on human health. For example, Escherichia coli O157: H7 is a highly virulent strain, while most other strains of Escherichia coli are not pathogenic. Applying subspecies identification in the analysis of human intestinal tracts and various environmental microbial communities will give us a new understanding of the existence of subspecies in different environments and their geographical distribution, dominance and sustainable emergence. Through identifying some specific pathogenic strains, we will be also able to apply subspecies identification in epidemiological surveillance and medical diagnosis to improve human health.

Link to article:

https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/bty844/5116146?searchresult=1