Genome Assembly Parallel Sorting Segmentation
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Solution Overview
Problem
Existing genome assembly algorithms face high computational complexity due to the quadratic increase in sampling points with the number of processes, leading to low assembly efficiency.
Innovation Solution
A genome assembly method that involves obtaining gene short sequences, determining segmentation values, segmenting and globally sorting subsequences using grouped parallel sorting by regular sampling, constructing a distributed gene map, and traversing it in parallel to assemble continuous sequences, thereby reducing the number of sampling points and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of processes is increased to improve parallel sorting efficiency, then the sorting speed improves, but the number of sampling points increases quadratically leading to higher computational complexity
Solution Approach 1:
The patent divides the De Bruijn graph into multiple segments and processes different segments in parallel. Each process handles a specific segment rather than the entire graph, which reduces the sampling points per process from O(n²) to O(n) while maintaining parallel processing benefits. This segmentation approach allows efficient parallel sorting without quadratic complexity increase.
Solution Approach 2:
The patent introduces a new dimension of segmentation along the graph structure, organizing data in a multi-dimensional parallel processing framework. By segmenting the graph and distributing segments across processes, it transforms the computational problem from a single-dimension quadratic complexity to a multi-dimensional structure where each process operates on reduced data subsets.
2Ease of operation
If each process randomly selects a vertex as seed to extend forward and backward, then the gene segment search is performed, but different initial vertices may belong to the same gene segment causing redundant computation
Solution Approach 1:
The patent performs preliminary sorting of the De Bruijn graph vertices before the gene segment search. By pre-sorting the vertices and assigning them systematically to different processes based on their sorted positions, it ensures that each process works on distinct gene segments. This preliminary organization prevents redundant computation where multiple processes would otherwise search the same segment starting from different random vertices.
3Reliability
If vertices of a gene segment are scattered across many processes during outward extension, then the complete gene segment can be found, but the computational complexity increases
Solution Approach 1:
The patent segments the De Bruijn graph and assigns specific segments to specific processes in a systematic manner. When extending gene segments, the sorted structure ensures that vertices belonging to the same gene segment remain localized to the same process or a predictable set of processes, rather than being scattered across all processes. This reduces the complexity of tracking and assembling complete gene segments.
Data Source
AI summary
Disclosed are a genome assembly method, a genome assembly apparatus, a device and a storage medium. The method includes: obtaining a gene short sequence, and determining a first segmentation value; segmenting the gene short sequence based on the first segmentation value to obtain each gene subsequence; globally sorting each gene subsequence based on a preset grouped parallel sorting by regular sampling to obtain each sorted gene subsequence; traversing the distributed gene map in parallel to obtain each continuous gene sequence, and filling and assembling each continuous gene sequence to obtain each target continuous gene sequence; and determining a second segmentation value, and in response to that the second segmentation value is greater than or equal to a preset maximum segmentation threshold, assembling each target continuous gene sequence to obtain a genome assembly result.