Development of Wildlife Identification System Based on Local Alignment Algorithm

Wildlife crimes seriously threaten biodiversity and public health security. Identifying the species of wild animals and their products involved in such cases is the key to handling them. This study aimed to establish a wildlife DNA identification system based on a local alignment algorithm, thereby providing reliable technical support for public security organizations with high-precision and high-efficiency species identification techniques. The system adopted the Smith–Waterman local alignment algorithm, recognized the local similarity of DNA sequences with dynamic programming, and solved the problem of heavy reliance on complete sequences when using traditional global alignment. The parameters were optimized in combination with the Basic Local Alignment Search Tool, and matrix calculation was accelerated with C-Extensions for Python, thus significantly enhancing the processing efficiency for large-scale data. The database was based on self-dedicated identification data from the College of Criminal Science and Technology, Nanjing Police University. Experts reviewed the data, and the accuracy and safety of data were ensured to avoid the risk of species classification errors in open-source databases. The system innovatively integrated the Streamlit framework to develop the visualization platform and user-friendly interface. The grassroots police officers obtained species-matching results and the related species information by inputting DNA sequences, thereby facilitating case handling. The system provides an efficient technical tool for combating wildlife crimes and maintaining biodiversity, with significant practical value and immense promotion potential.