Haplotype Mosaic Compression for Genomic Variant Call Data
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Solution Overview
Problem
Current data compression methods for genomic data, such as VCF files, are inefficient and require significant storage and computational resources, especially in environments with limited resources like laptops and smartphones, due to high redundancy and lack of effective utilization of haplotype structure.
Innovation Solution
A method that uses phased haplotype information from a reference database to identify mosaic segments matching an individual's genome, encoding these segments and deviations to achieve higher compression efficiency, leveraging the positional Burrows-Wheeler transform to optimize the compression process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If standard compression tools (bgzip, M3VCF) are used on VCF files, then the file size is reduced to 100-500 MB, but the compression ratio is insufficient for environments with limited storage and connectivity resources
Solution Approach 1:
The genome is divided into two separate haplotype sequences (maternal and paternal chromosomes), each processed independently through the compression algorithm. This segmentation allows the method to exploit the structure of individual haplotypes more effectively than treating the diploid genome as a single unit, leading to better compression ratios while maintaining the ability to reconstruct the original genotype data.
Solution Approach 2:
The method creates a compressed representation by copying and encoding haplotype segments from reference panels. Instead of storing all raw genotype data, it stores references to segments in the reference panel along with deviation information, significantly reducing the amount of data that needs to be stored and transmitted while preserving all necessary genetic information.
2Measurement precision
If detailed variant information is retained in VCF files to maintain accuracy, then measurement precision is improved, but file size increases making it difficult to use in resource-limited environments
Solution Approach 1:
The method extracts and separates the essential genetic information (haplotype segments from reference panels) from the redundant representation (full VCF files with all variant calls). By extracting only the necessary segment information and storing references rather than complete variant data, it maintains measurement precision while dramatically reducing file size to suit resource-limited environments.
Solution Approach 2:
The compression approach changes the representation parameters from storing complete genotype calls at every position to storing segmented haplotype references with deviation markers. This parameter transformation allows the system to maintain accurate variant call information where needed while using a more compact representation for the majority of the genome where haplotype segments match reference panels.
Data Source
AI summary
Methods and apparatus for compressing and decompressing genetic information from an individual. In one arrangement, a data compression method generates a compressed representation of at least a portion of an individual's genome by receiving an input file having a representation of the genome as a sequence of variants defined relative to a reference genome. A reference database having a plurality of reference lists of genetic variants from other individuals is accessed. Each reference list has a sequence of genetic variants from a single, phased haplotype. Two mosaics of segments from the reference lists are identified which match the genome to within a threshold accuracy. Each mosaic represents a single one of the two haplotypes of the individual's genome and includes a portion of the sequence of genetic variants from one of the reference lists. The compressed representation is generated by encoding the two mosaics and deviations from the two mosaics.