Genomic Read Alignment via In-Memory Database Segmentation
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Solution Overview
Problem
Next-generation sequencing technologies produce vast amounts of data quickly and cheaply, but the computational alignment of short, imperfect genomic reads to a reference genome becomes a bottleneck, limiting the ability to fully exploit parallel processing and memory hierarchy due to complex index data structures and cache faults.
Innovation Solution
A computer-based system utilizing an in-memory database with a worker framework and alignment coordinator for parallel processing of genomic sequence data, optimizing alignment algorithms for in-memory use, and employing double indexing and early out heuristics to reduce processing time and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sophisticated index data structures such as suffix arrays and suffix trees are used for genomic read alignment, then alignment accuracy is improved, but processing time and memory access complexity increase due to many cache faults
Solution Approach 1:
The reference genome is divided into smaller segments or blocks, and an in-memory database is constructed for each block. This segmentation reduces the size of data structures that need to be kept in cache, thereby reducing cache faults while maintaining alignment accuracy through systematic processing of all blocks.
Solution Approach 2:
The in-memory database is pre-constructed from the reference genome before alignment processing begins. This preliminary action organizes the reference data in an optimized format that minimizes cache access during the actual alignment operation, reducing processing time while maintaining accuracy.
2Measurement precision
If sophisticated index data structures are used for genomic read alignment, then alignment accuracy is improved, but device complexity increases due to difficulty in parallel construction and querying
Solution Approach 1:
The reference genome is divided into smaller segments or blocks, and an in-memory database is constructed for each block. This segmentation reduces the size of data structures that need to be kept in cache, thereby reducing cache faults while maintaining alignment accuracy through systematic processing of all blocks.
Solution Approach 2:
The in-memory database is pre-constructed from the reference genome before alignment processing begins. This preliminary action organizes the reference data in an optimized format that minimizes cache access during the actual alignment operation, reducing processing time while maintaining accuracy.
3Quantity of substance
If traditional file system operations are used for genomic data processing, then data storage capacity is improved, but processing speed deteriorates due to extensive file system operations
Solution Approach 1:
The patent replaces traditional file system operations with an in-memory database system. By loading the reference genome and intermediate results into RAM, the system eliminates slow disk I/O operations while maintaining the ability to store and process large genomic datasets, thereby dramatically improving processing speed.
4Productivity
If parallel processing is attempted for genomic alignment, then processing throughput is improved, but efficiency decreases due to inability to fully exploit parallel processing and memory hierarchy
Solution Approach 1:
The reference genome is divided into smaller segments or blocks, and an in-memory database is constructed for each block. This segmentation reduces the size of data structures that need to be kept in cache, thereby reducing cache faults while maintaining alignment accuracy through systematic processing of all blocks.
Solution Approach 2:
The in-memory database is pre-constructed from the reference genome before alignment processing begins. This preliminary action organizes the reference data in an optimized format that minimizes cache access during the actual alignment operation, reducing processing time while maintaining accuracy.
Data Source
AI summary
A high performance, low-cost, gapped read alignment algorithm is disclosed that produces high quality alignments of a complete human genome in a few minutes. Additionally, the algorithm is more than an order of magnitude faster than previous approaches using a low-cost workstation. The results are obtained via careful algorithm engineering of the seeding based approach. The use of non-hashed seeds in combination with techniques from search engine ranking achieves fast cache-efficient processing. The algorithm can also be efficiently parallelized. Integration into an in-memory database infrastructure (IMDB) leads to low overhead for data management and further analysis.