An Algorithm for the Selection of Probes for Specific Detection of Human Disease Pathogens Using the DNA Microarray Technology

The aim of the study was to develop an algorithm for the selection of discriminating probes to identify a wide range of causative agents of human infectious diseases. MATERIALS AND METHODS: The algorithm for selecting the probes was implemented in the form of the disprose (DIScrimination PRObe SElection) computer program written in the R language. Additionally, third-party software was used: the BLAST+ and ViennaRNA Package programs. The developed algorithm was tested by selecting specific probes for detecting Chlamydophila (Chlamydia) pneumoniae — an atypical bacterial pathogen causing community-acquired pneumonia (CAP). Nucleotide sequences for analysis were downloaded from the NCBI databank. RESULTS: An algorithm for the selection of specific probes capable of detecting human infectious pathogens has been developed. The algorithm is implemented in the form of the disprose modular program, which allows for performing all stages of the probe selection process: loading the nucleotide sequences and their metadata from available databanks, creating local databases, forming a pool of probes, calculating their physicochemical parameters, aligning the probes and sequences contained in local databases, processing and evaluating the alignment results. The algorithm was successfully tested and its performance was confirmed by selecting a set of probes for the specific detection of Chlamydophila pneumoniae. The specificity of the selected probes calculated in silico indicated a low risk of their nonspecific binding and a high potential of using them as molecular genetic diagnostic tools (DNA microarrays, PCR). CONCLUSION: An algorithm for the selection of specific probes detecting a wide range of human pathogens in clinical biomaterial has been developed and implemented in the form of the disprose modular program. The probes selected using this program can serve as the functional basis of DNA-oriented microarrays able to identify causative agents of polyetiological diseases, such as CAP. Due to the flexibility and openness of the program, the scope of its application can be expanded.