Compressed Genomic Sequence Search Using Reference-Variant Indexing
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Solution Overview
Problem
Current genomic data analysis techniques face computational bottlenecks due to the exponential growth of genomic data, with existing compression methods not effectively mitigating the need for full data reconstruction during analysis, and existing search algorithms being inefficient for inexact searches in large genomic libraries.
Innovation Solution
The approach involves compressing genomic data to preserve structural similarities and differences, allowing direct computation on the compressed data using 'compressive' algorithms, which include pre-processing to create a compressed database and a links table, and a two-step search process using BLAST or BLAT, enabling efficient analysis proportional to the compressed data size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If genomic data is compressed to reduce storage space, then storage efficiency is improved, but computational analysis requires full decompression which negates the time savings
Solution Approach 1:
The patent segments the compressed genomic data into a core reference sequence and multiple variant calls. During analysis, only the relevant variant calls need to be applied to the reference sequence rather than fully decompressing all data, enabling partial reconstruction that significantly reduces computational time while maintaining storage efficiency.
Solution Approach 2:
The patent performs preliminary organization of genomic variants during the compression phase, structuring the data so that during analysis, only the necessary variant information needs to be retrieved and applied to the reference sequence. This preliminary structuring eliminates the need for full decompression while preserving fast access to relevant genetic information.
2Reliability
If traditional search algorithms are used on full genomic libraries, then search completeness is maintained, but computational time scales linearly with library size
Solution Approach 1:
The patent extracts only the essential variant information from complete genomic sequences, storing differences relative to a reference rather than all sequence data. During search operations, queries are performed on this extracted variant information combined with the reference sequence, maintaining search completeness while reducing the data volume to be processed by a significant factor.
3Quantity of substance
If compression schemes are used that do not respect sequence similarity metrics, then storage efficiency is improved, but search accuracy deteriorates
Solution Approach 1:
The patent changes the representation parameters from raw sequence bases to structured variant calls with explicit positional and typological information (insertions, deletions, substitutions). This parameter transformation maintains storage efficiency through compression while preserving the ability to accurately measure sequence similarity by applying variants to the reference sequence during analysis.
Data Source
AI summary
The redundancy in genomic sequence data is exploited by compressing sequence data in such a way as to allow direct computation on the compressed data using methods that are referred to herein as “compressive” algorithms. This approach reduces the task of computing on many similar genomes to only slightly more than that of operating on just one. In this approach, the redundancy among genomes is translated into computational acceleration by storing genomes in a compressed format that respects the structure of similarities and differences important to analysis. Specifically, these differences are the nucleotide substitutions, insertions, deletions, and rearrangements introduced by evolution. Once such a compressed library has been created, analysis is performed on it in time proportional to its compressed size, rather than having to reconstruct the full data set every time one wishes to query it.