Parallel Genome Analysis Using Overlapping Subregional Processing
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Solution Overview
Problem
Current genome sequencing data analysis methods are computationally intensive and take days to complete, unable to efficiently distribute the workload across multiple compute nodes, which is inadequate for population-scale genome sequencing workflows.
Innovation Solution
A parallel processing system that divides the genome into evenly sized subregions, using both artificial chromosomes and overlapping subregional boundaries to maintain data integrity, enabling simultaneous processing of alignment, deduplication, recalibration, and genotyping steps across multiple processors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If sequential data analysis process is used, then data integrity is maintained, but processing time becomes excessively long (days to complete)
Solution Approach 1:
The genome is divided into multiple subregions, with each hardware processor assigned to process a specific subregion independently. This segmentation enables parallel processing of alignment, deduplication, recalibration, and genotyping steps across multiple processors simultaneously, reducing overall processing time from days to hours while maintaining data integrity through coordinated processing of overlapping regions.
2Productivity
If workload is distributed across multiple compute nodes, then processing speed increases, but data integrity and reproducibility become difficult to maintain
Solution Approach 1:
Overlapping subregional boundaries are implemented where adjacent subregions share common genomic regions. This local quality approach ensures that data processed in parallel across multiple compute nodes can be cross-validated and merged consistently, maintaining reproducibility and data integrity while enabling speedup through parallel processing.
3Adaptability or versatility
If population-scale genome sequencing is performed, then scientific discovery capability increases, but computational resource requirements become unmanageable
Solution Approach 1:
The computational workload for population-scale sequencing is segmented both by genome subregions and by processing steps (alignment, deduplication, recalibration, genotyping). This multi-level segmentation allows efficient distribution across hardware processors, enabling population-scale analysis with manageable computational resources while maintaining the capability for comprehensive scientific discovery.
4Measurement precision
If more processing steps are performed (alignment, deduplication, recalibration, genotyping), then analysis accuracy improves, but processing time increases
Solution Approach 1:
Multiple processing steps (alignment, deduplication, recalibration, and genotyping) are executed in continuous parallel operation across different hardware processors rather than sequentially. This continuity maintains comprehensive analysis accuracy while dramatically reducing total processing time through simultaneous execution of all necessary steps.
Data Source
AI summary
An apparatus includes a set of hardware processors and a memory configured to store a sequence. The sequence includes a set of elements. Hardware processors are configured to implement a segment processing module, and an assignment module. The assignment module receives the sequence and assigns each element to at least one segment from a set of segments, including, when an element maps to at least a first segment and a second segment, assigning the element to both the first segment and the second segment. A segment processing module is configured to substantially simultaneously, for each segment from a set of segments specific to that one or more hardware processors, remove at least a portion of duplicate elements to generate a deduplicated segment. The segment processing module reorders the elements in the deduplicated segment to generate a realigned segment that has a reduced alignment errors.