Genome Sequence Compression Using GoB Partitioning and Hybrid Encoding
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Solution Overview
Problem
The rapid growth in genome data storage requires more efficient methods for compressing and transmitting genome sequences to reduce storage costs and improve transmission efficiency.
Innovation Solution
A method and system for compressing genome sequences by partitioning them into Groups of Bases (GoBs), using a combination of Markov models and Long and Short-Term Memory (LSTM)-based neural networks for encoding and decoding, with arithmetic encoding and decoding processes to achieve efficient compression and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional storage methods are used for genome sequences, then data can be stored, but storage costs increase rapidly and storage capacity is strained
Solution Approach 1:
The patent transforms the genome sequence from its original textual format into a compressed binary representation by changing the parameter of data encoding. The arithmetic encoder modifies the probability parameters dynamically during encoding, achieving high compression ratios that reduce storage requirements while maintaining data integrity
Solution Approach 2:
The patent creates a compressed copy of the genome sequence data through arithmetic encoding. The encoded bit stream represents a compact copy of the original genome data, which can be stored more efficiently and then decoded back to the original sequence when needed
2Quantity of substance
If genome sequences are compressed using existing methods, then storage space is reduced, but transmission efficiency and decoding speed are insufficient
Solution Approach 1:
The patent segments the genome sequence into multiple blocks that can be processed independently and in parallel. The arithmetic encoder processes each block separately, allowing for parallel computation that accelerates both encoding and decoding operations while maintaining overall compression efficiency
Solution Approach 2:
The patent replaces traditional mechanical or algorithmic compression methods with a mathematically optimized arithmetic encoding system. The use of probability models and arithmetic coding operations provides both high compression ratios and computational efficiency, achieving faster processing speeds compared to conventional compression algorithms
3Loss of energy
If compression ratio is increased to reduce storage costs, then transmission efficiency improves, but encoding complexity increases
Solution Approach 1:
The arithmetic encoder performs self-optimization by dynamically adjusting probability parameters during the encoding process. The model adapts to the specific characteristics of the genome sequence being encoded, automatically optimizing the compression ratio without requiring external intervention or complex configuration
Solution Approach 2:
The patent implements a universal arithmetic encoding framework that can handle genome sequences of varying lengths and compositions. The same encoding algorithm and probability models work effectively across different genome types, providing a versatile solution that achieves high compression ratios without requiring separate complex systems for different data types
Data Source
AI summary
Systems and methods for genome sequence compression and decompression are provided. The method for compression encoding of a genome sequence includes partitioning a genome sequence into a plurality of Group of Bases (GoBs) and processing each of the plurality of GoBs independently to encode the genome sequence into a bit stream. Processing each of the plurality of GoBs includes dividing each of the plurality of GOBs into a first part and a second part, the first part including an initial context part and the second part including a learning-based inference part. The processing each of the plurality of GoBs further includes encoding the first part in accordance with a Markov model, encoding the second part in accordance with a learning-based model, and encoding the encoded first part and the encoded second part into the bit stream with an arithmetic encoder. The learning-based model may include Long and Short-Term Memory (LSTM)-based neural networks.