Presumptive identifcation of different Enterobaeteriaeeae species is routinely achieved based on biochemical properties. Traditional practice includes manual comparison of each biochem- ical property of the unknown sa...Presumptive identifcation of different Enterobaeteriaeeae species is routinely achieved based on biochemical properties. Traditional practice includes manual comparison of each biochem- ical property of the unknown sample with known reference samples and inference of its identity based on the maximum similarity pattern with the known samples. This process is labor- intensive, time-consuming, error-prone, and subjective. Therefore, automation of sorting and sim- ilarity in calculation would be advantageous. Here we present a MATLAB-based graphical user interface （GUI） tool named BioCluster. This tool was designed for automated clustering and iden- tification of Enterobacteriaceae based on biochemical test results. In this tool, we used two types of algorithms, i.e., traditional hierarchical clustering （HC） and the Improved Hierarchical Clustering （IHC）, a modified algorithm that was developed specifically for the clustering and identification of within this species. IHC takes into account the variability in result of 1-47 biochemical tests family. This tool also provides different options to optimize the clus- tering in a user-friendly way. Using computer-generated synthetic data and some real data, we have demonstrated that BioCluster has high accuracy in clustering and identifying enterobacterial species based on biochemical test data. This tool can be freely downloaded at http：//microbialgen.du.ac.bd/ biocluster/.展开更多
基金supported by the grants from the Ministry of Science and Technology (S&T) of Bangladesh (Grant No.HEQEP CP236)the University Grants Commission (UGC).
文摘Presumptive identifcation of different Enterobaeteriaeeae species is routinely achieved based on biochemical properties. Traditional practice includes manual comparison of each biochem- ical property of the unknown sample with known reference samples and inference of its identity based on the maximum similarity pattern with the known samples. This process is labor- intensive, time-consuming, error-prone, and subjective. Therefore, automation of sorting and sim- ilarity in calculation would be advantageous. Here we present a MATLAB-based graphical user interface （GUI） tool named BioCluster. This tool was designed for automated clustering and iden- tification of Enterobacteriaceae based on biochemical test results. In this tool, we used two types of algorithms, i.e., traditional hierarchical clustering （HC） and the Improved Hierarchical Clustering （IHC）, a modified algorithm that was developed specifically for the clustering and identification of within this species. IHC takes into account the variability in result of 1-47 biochemical tests family. This tool also provides different options to optimize the clus- tering in a user-friendly way. Using computer-generated synthetic data and some real data, we have demonstrated that BioCluster has high accuracy in clustering and identifying enterobacterial species based on biochemical test data. This tool can be freely downloaded at http：//microbialgen.du.ac.bd/ biocluster/.
