Background Tuberculosis (TB) and non-tuberculous mycobacteriosis are serious threats to health worldwide. A simple non-sequencing method is needed for rapid diagnosis, especially in less experienced hospitals, but there is no specific biomarker commonly used for all mycobacteria. The ku gene of the prokaryotic error-prone non-homologous end joining system (NHEJ) has the potential to be a highly specific detection biomarker for mycobacteria. Methods A total of 7294 mycobacterial genomes and 14 complete genomes of other families belonging to Corynebacteriales with Mycobacteriaceae were downloaded and analyzed for the existence and variation of the ku gene. Mycobacterium tuberculosis complex (MTBC) and non-tuberculosis mycobacteria (NTM)- specific primers were designed and the actual amplification and identification efficiencies were tested with 150 strains of 40 Mycobacterium species and 10 kinds of common respiratory pathogenic bacteria. Results The ku gene of the NHEJ system was ubiquitous in all genome sequenced Mycobacterium species and absent in other families of Corynebacteriales . On the one hand, as a single gene non-sequencing biomarker, its specific primers could effectively distinguish mycobacteria from other bacteria, MTBC from NTM, which would make the clinical detection of mycobacteria easy and have great clinical practical value. On the other hand, the sequence of ku gene can effectively distinguish NTM to species level with high resolution. Conclusion The Ku protein existed before the differentiation of Mycobacterium species, which was an important protein involved in maintaining of the genome’s integrity and related to the special growth stage of mycobacteria. It was rare in prokaryotes. These features made it a highly special differential biomarker for Mycobacterium .

Background Many miRNA-based diagnostic models have been constructed to distinguish diseased individuals. However, due to the inherent differences across different platforms or within multi-center data, the models usually fail in the generalization for medical application. Results Here, we proposed to use the within-sample expression ratios of related miRNA pairs as markers, by utilizing the internal miRNA: miRNA interactions. The ratio of the expression values between each miRNA pair turned out to be more stable cross multiple data source. Moreover, we adopted the genetic algorithm to solve the curse of dimensions when exploring the features. Conclusions The application results on three example datasets demonstrated that the expression ratio of interacting miRNA pair is a promising type of biomarker, which is insensitive to batch effects and has better performance in disease classifications.