Multi-Pass Genomic Read Mapping With Early k-Mer Termination
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Solution Overview
Problem
Existing genomic read mapping methods are inefficient and time-consuming, particularly in the evaluation of k-mer seeds against candidate alignments, leading to prolonged runtime in aligning genomic data to a reference genome.
Innovation Solution
A multi-pass software-accelerated genomic read mapping engine that iteratively evaluates k-mer seeds using a hash table, allowing early termination of the mapping process and reducing runtime by filtering and aligning genomic data more efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional genomic read mapping methods are used, then mapping accuracy is maintained, but runtime is prolonged and processing efficiency is low
Solution Approach 1:
The mapping process is divided into multiple passes, where each pass processes a subset of k-mer seeds. This segmentation allows the system to process data in manageable chunks, improving overall efficiency while maintaining accuracy through iterative refinement across passes.
Solution Approach 2:
The system performs preliminary filtering and evaluation of k-mer seeds before final alignment. By pre-processing and filtering candidate alignments in earlier passes, the system reduces the computational burden in subsequent passes, thereby decreasing total runtime while maintaining mapping accuracy.
2Measurement precision
If more k-mer seeds are evaluated against candidate alignments, then mapping accuracy improves, but processing time increases
Solution Approach 1:
The system performs periodic evaluation of k-mer seeds across multiple passes, where each pass evaluates a subset of seeds. This periodic action allows the system to progressively improve accuracy by re-evaluating candidates with refined criteria, reducing total evaluation time compared to a single comprehensive pass.
Solution Approach 2:
The system uses feedback from previous passes to refine alignment criteria and prioritize high-confidence candidates. By incorporating feedback mechanisms that adjust evaluation criteria based on previous results, the system maintains high accuracy while reducing the number of required evaluations in subsequent passes.
3Quantity of substance
If a larger hash table is used to store more k-mer seeds, then mapping completeness improves, but memory requirements and processing overhead increase
Solution Approach 1:
The hash table is segmented across multiple passes, where each pass processes a subset of k-mer seeds. This allows the system to maintain a manageable hash table size at any given time while still processing a comprehensive set of seeds across multiple passes, reducing memory requirements and processing overhead.
Solution Approach 2:
The system dynamically adjusts the hash table size and processing parameters based on the current pass and available computational resources. This dynamic adaptation allows the system to optimize between memory usage and processing capacity, enabling comprehensive k-mer seed processing without requiring a permanently large hash table.
Data Source
AI summary
Software-accelerated genomic data read mapping includes commencing iterative performance of operations for the software accelerated genomic data read mapping to map a genomic data read to a reference genome. The operations include obtaining a next k-mer seed from a genomic data read, generating a genomic signature based on the next k-mer seed, determining a reference sequence location using a hash data structure, determining a number of mismatches of the next k-mer seed, based on determining the number of mismatches satisfies a mismatch threshold, terminating the iterative performance of the operations, and selecting an actual alignment for the genomic data read based on the obtained next k-mer seed.