With thousands of sequenced 16 S rRNA genes available,and advancements in oligonucleotide microarray technology,the detection of microorganisms in microbial communities consisting of hundreds of species may be possible.The existing algorithms developed for sequence-specific probe design are not suitable for applications in large-scale bacteria detection due to the lack of coverage,flexibility and efficiency.Many other strategies developed for group-specific probe design focus on how to find a unique group-specific probe that can specifically detect all target sequences of a group.Unique group-specific probe for each group can not always be found.Hence,it is necessary to design non-unique probes.Each probe can specifically detect target sequences of a different subgroup.Combination of multiple probes can achieve higher coverage.However,it is a time-consuming task to evaluate all possible combinations.A feasible algorithm using relative entropy and genetic algorithm (GA) to design group-specific non-unique probes was presented.

Interpreting genes of interest is essential for identifying molecular mechanisms, but acquiring such information typically involves tedious manual retrieval. To streamline this process, the fanyi package offers tools to retrieve gene information from sources like National Center for Biotechnology Information (NCBI), significantly enhancing accessibility. Additionally, understanding the latest research advancements and sharing achievements are crucial for junior researchers. However, language barriers often restrict knowledge absorption and career development. To address these challenges, we developed the fanyi package, which leverages artificial intelligence (AI)-driven online translation services to accurately translate among multiple languages. This dual functionality allows researchers to quickly capture and comprehend information, promotes a multilingual environment, and fosters innovation in academic community. Meanwhile, the translation functions are versatile and applicable beyond biomedicine research to other domains as well. The fanyi package is freely available at https://github.com/YuLab-SMU/fanyi.