Multi-Modal Genomic Compression Using Cross-Platform Quality Fusion
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Solution Overview
Problem
Current genomic data compression systems fail to effectively handle heterogeneous data from multiple sequencing platforms, leading to suboptimal compression efficiency and loss of cross-platform relationships, and do not adapt compression strategies based on region-specific importance across platforms.
Innovation Solution
A system and method for multi-modal genomic data fusion with adaptive quality-driven compression that harmonizes heterogeneous data formats, evaluates genomic region importance by analyzing cross-platform correlations, and uses a neural network to recover lost information while maintaining biological significance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current compression systems treat data from different sequencing platforms independently, then device complexity is reduced, but compression efficiency deteriorates and cross-platform relationships are lost
Solution Approach 1:
The patent merges data from multiple sequencing platforms into a unified multi-modal dataset, treating them as interconnected rather than independent. The system integrates Illumina, PacBio, Oxford Nanopore, and 10× Genomics data together, using cross-platform correlations to improve compression efficiency while preserving biological relationships through a unified processing framework
Solution Approach 2:
The compression system is designed with multi-functionality to handle diverse data types from different sequencing platforms simultaneously. It implements universal processing capabilities that can adapt to various data formats and characteristics while maintaining a single integrated compression pipeline that exploits correlations across all platforms
2Manufacturing precision
If existing genomic compression methods are optimized for single-platform data, then manufacturing precision is improved for that platform, but adaptability to heterogeneous multi-platform data deteriorates
Solution Approach 1:
The system implements dynamic adaptation mechanisms that adjust compression parameters and strategies based on the specific characteristics of each sequencing platform and the data being processed. The compression algorithm dynamically selects and applies appropriate techniques for different data types while maintaining overall system coherence and exploiting cross-platform correlations
Solution Approach 2:
The patent applies local quality principles by treating different genomic regions and different platforms with customized compression strategies. Important genomic regions receive higher preservation priority, while less critical regions undergo more aggressive compression. Each platform's data characteristics are considered locally to optimize compression while maintaining overall data integrity
3Device complexity
If compression rates are applied uniformly across all genomic regions, then device complexity is reduced, but loss of information increases in biologically important regions
Solution Approach 1:
The system implements region-specific compression strategies that evaluate the biological importance of different genomic regions and apply appropriate compression rates. Critical regions such as coding sequences, regulatory elements, and areas with high variant density receive lower compression rates to preserve information, while less important regions undergo higher compression
Solution Approach 2:
The compression system incorporates feedback mechanisms that continuously assess the importance of genomic regions based on multi-platform data correlations and adjust compression rates accordingly. The system uses quality metrics and biological annotations to feedback into the compression decision process, ensuring that biologically important regions are preserved while achieving overall compression efficiency
Data Source
AI summary
A system for multi-modal genomic data fusion with adaptive quality driven compression processes genomic data from multiple sequencing platforms. The system harmonizes heterogeneous data formats from different platforms into a unified representation, then evaluates genomic region importance by analyzing cross-platform correlations. A multi-modal quality assessor generates consensus quality scores across platforms using weighted voting algorithms, while a multi-modal rate control engine determines optimal compression rates based on quality scores and platform-specific characteristics. The system compresses genomic data while maintaining cross-platform relationships, then recovers lost information using a neural network comprising recurrent layers and channel-wise transformers that leverage cross-platform correlations. The neural network integrates complementary information from multiple sequencing technologies to reconstruct genomic data with improved quality compared to single-platform approaches, enabling efficient storage and analysis of multi-modal genomic datasets while preserving critical biological relationships.