Abstract: The invention provides a method and file format for semantically preserving compression of any file type and/or data using a multi-level charterization process and optimized lookup tables. By leveraging advanced pattern matching and efficient data structures, the method achieves higher compression ratios while maintaining the original data's meaning and functionality. The compressed data is stored in a new file format with a unique extension and can be accessed via a virtual service over an API, facilitating widespread integration and use.

Abstract: This disclosure is various methods and systems that utilize deep learning, specifically convolutional neural networks and recurrent neural networks to enable bacterial identification and classification by analyzing raw genomic sequences, such as the 16S rRNA gene and other preserved regions. The system involves multiple convolutional layers to extract and generalize features, correlate their presence, and ultimately classify the sequences into genera or species. RNNs, such as LSTMs, are used when the order of features matters, particularly in cases with padded regions or separators between gene segments.