DNA Sequence Compression with Alignment Segmentation and Pointer Pairs
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Solution Overview
Problem
Existing data compression methods are inefficient in handling large files and biological sequences, particularly in terms of storage requirements and bandwidth usage, especially when dealing with repetitive patterns and sequences.
Innovation Solution
A compression engine that utilizes multiple sequence alignment to split files into aligned sequences, generating a consensus sequence and representing the original file as a list of pointer pairs, allowing for efficient compression and reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing data compression methods are used, then storage space and bandwidth are consumed, but compression efficiency is poor for large files and biological sequences with repetitive patterns
Solution Approach 1:
The input file is divided into multiple sequences of equal length, which are then aligned and processed independently. This segmentation allows the compression algorithm to identify and exploit repetitive patterns more effectively within each sequence while maintaining overall compression efficiency for large files.
Solution Approach 2:
The patent creates a consensus sequence that represents the aligned sequences, and then uses pointer pairs to reference subsequences within this consensus sequence. Instead of storing redundant data multiple times, the system creates a single copy (consensus sequence) with pointers that reference relevant portions, significantly reducing storage requirements while preserving all original information.
2Quantity of substance
If file size is reduced through compression, then storage requirements and bandwidth usage decrease, but computational resources are consumed during compression
Solution Approach 1:
By dividing the file into smaller sequences of equal length before alignment, the computational complexity of the alignment process is reduced. Each sequence can be processed more efficiently, and the segmented approach allows for parallel processing, thereby reducing overall computational resource consumption while achieving effective compression.
Solution Approach 2:
The patent transforms the original sequences by aligning them and generating a consensus sequence, changing the representation parameters from raw data to aligned positional information. This parameter transformation enables more efficient storage and processing, as the aligned structure reveals patterns that can be compressed more effectively with reduced computational overhead.
3Quantity of substance
If sequences are aligned to generate consensus sequence, then compression ratio improves, but device complexity increases
Solution Approach 1:
The alignment process is simplified by first segmenting the input into equal-length sequences, which makes the subsequent alignment operation more manageable. This segmentation reduces the complexity of the alignment mechanism by breaking down a complex global alignment problem into smaller, more tractable sub-problems that can be solved more efficiently.
Solution Approach 2:
The patent changes the representation parameters of the sequences through alignment, transforming them into a standardized format that facilitates consensus sequence generation. This parameter change simplifies the alignment mechanism by imposing a regular structure on the data, making the alignment process more systematic and less computationally intensive despite the increased compression ratio.
Data Source
AI summary
Compressing files is disclosed. An DNA sequence to be compressed is first aligned. Aligning the DNA sequence includes splitting the DNA sequences into smaller sequences or portions that can be aligned. After the DNA sequence is spilt one or more time and aligned, a compression matrix is generated. Each row of the compression matrix corresponds to part of the DNA sequence. A consensus sequence is determined from the compression matrix. Using the consensus sequence, pointer pairs are generated. Each pointer pair identifies a subsequence of the consensus matrix. The compressed file includes the pointer pairs and the consensus sequence.