Contact Matrix Codec for Genomic Data Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current compression algorithms for genomic annotation data lack efficiency due to the lack of a unified format, leading to suboptimum performance in data storage and processing, as they do not exploit the different statistical characteristics of multiple fields within the data, resulting in inefficient random access and extraction of specific fields.
Innovation Solution
A unified data format, referred to as the contact matrix codec (CMC) data structure, which includes a header with interval information, chromosome details, normalization methods, and matrix payloads, enables efficient encoding and decoding of contact matrices by transforming sparse matrices to dense, applying diagonal transformations, and using entropy coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If current compression algorithms are used for genomic annotation data, then data can be stored, but storage efficiency and processing performance are suboptimal due to lack of unified format and failure to exploit statistical characteristics of different fields
Solution Approach 1:
The contact matrix data is segmented into multiple fields with different statistical characteristics (e.g., chromosome identifiers, start positions, end positions, contact frequencies). Each field is encoded using algorithms optimized for its specific statistical properties, allowing efficient compression while maintaining fast random access to individual fields without processing the entire dataset.
Solution Approach 2:
Different compression and encoding strategies are applied to different fields within the contact matrix data based on their local statistical characteristics. For example, chromosome identifiers may use dictionary encoding, positional data may use differential encoding, and frequency data may use entropy coding. This localized optimization improves both storage efficiency and processing speed for specific data types.
2Productivity
If a unified data format is implemented to exploit statistical characteristics of multiple fields, then storage efficiency improves, but implementation complexity increases
Solution Approach 1:
The CMC data structure provides a universal format that handles multiple types of genomic annotation data (contact matrices, coverage data, peak calls) through a consistent framework. The format includes standardized field definitions, encoding schemes, and access methods that work across different data types, reducing the need for multiple specialized formats while maintaining efficiency for each specific data type.
3Loss of energy
If sparse matrices are transformed to dense format with diagonal transformations and entropy coding, then compression efficiency improves, but encoding/decoding time increases
Solution Approach 1:
The contact matrix data undergoes preliminary transformations (sparse to dense conversion, diagonal transformations) during the encoding phase to optimize compression. These transformations are performed once during data preparation, and the transformed data is then efficiently compressed using entropy coding. The transformed format is stored and can be quickly decoded without repeating the computationally intensive transformations, reducing overall processing time for subsequent operations.
Data Source
AI summary
A system and method relate to encoding and decoding a contact matrix data structure. A system includes a processor and a computer-readable storage device storing a contact matrix data structure. The contact matrix data structure includes a header containing an interval of a contact matrix, a list of interval multipliers, a tile size, a list of chromosomes with a corresponding identifier and length, a list of sample identifiers, zero or more names of methods of normalization performed on the contact matrix tiles; zero or more bin payload having an interval multiplier; at least one parameter set; and at least one matrix payloads.