The article presents an efficient algorithm for global similarity searches of nucleic acid and protein sequences, which is significantly faster than previous methods while maintaining high sensitivity. The algorithm compares sequences by matching $k$-tuples of sequence elements, where $k$ is a fixed integer. This approach reduces computational time to the order of $N \times M$, where $N$ and $M$ are the lengths of the sequences being compared. The method has been adapted to produce rigorous sequence alignments and can search large data banks in a matter of minutes. The authors demonstrate the effectiveness of the algorithm through various test cases and comparisons with the Needleman-Wunsch method, showing that it can detect weak similarities and provide high-quality alignments. The algorithm's speed and accuracy make it a valuable tool for analyzing large sequence databases.The article presents an efficient algorithm for global similarity searches of nucleic acid and protein sequences, which is significantly faster than previous methods while maintaining high sensitivity. The algorithm compares sequences by matching $k$-tuples of sequence elements, where $k$ is a fixed integer. This approach reduces computational time to the order of $N \times M$, where $N$ and $M$ are the lengths of the sequences being compared. The method has been adapted to produce rigorous sequence alignments and can search large data banks in a matter of minutes. The authors demonstrate the effectiveness of the algorithm through various test cases and comparisons with the Needleman-Wunsch method, showing that it can detect weak similarities and provide high-quality alignments. The algorithm's speed and accuracy make it a valuable tool for analyzing large sequence databases.