Multi-Pass Genomic Read Mapping With Hash-Table Early 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 selecting candidate alignments based on mismatch thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional genomic read mapping methods are used to evaluate all k-mer seeds against candidate alignments, then mapping accuracy is maintained, but runtime becomes excessively long
Solution Approach 1:
The patent applies preliminary action by using a hash table to pre-store candidate alignment locations for k-mer seeds before the main mapping process. This allows the system to quickly retrieve and evaluate only the most promising alignments without exhaustively checking all possible candidates, thereby reducing runtime while maintaining accuracy through targeted evaluation of pre-identified candidates.
Solution Approach 2:
The patent segments the mapping process into multiple passes, where each pass evaluates a subset of k-mer seeds against candidate alignments. The first pass uses a relaxed threshold to quickly identify potential alignments, and subsequent passes apply stricter criteria. This segmentation allows the system to achieve both speed (by stopping early when sufficient alignments are found) and accuracy (by iteratively refining results).
2Productivity
If iterative multi-pass evaluation of k-mer seeds is implemented, then mapping speed increases, but process complexity increases
Solution Approach 1:
The hash table is constructed in advance during a preprocessing stage, storing candidate alignment locations for various k-mer seeds. This preliminary action eliminates the need for complex real-time alignment evaluation during the main mapping process, as the system simply needs to query the pre-built hash table and evaluate a limited set of candidates, thereby achieving high speed with manageable complexity.
Solution Approach 2:
The hash table serves as an intermediary data structure that mediates between the genomic read data and the reference genome. It pre-computes and stores candidate alignment locations, allowing the main mapping process to simply query and evaluate these pre-identified candidates rather than performing complex alignment calculations in real-time, thus simplifying the overall processing architecture while maintaining high productivity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for software-accelerated genomic data read mapping are described. In some implementations, software-accelerated genomic data read mapping includes obtaining a first k-mer seed from a genomic data read; generating a genomic signature based on a first k-mer seed; determining a reference sequence location using a hash data structure based on the genomic signature; determining a number of mismatches; based on determining the number of mismatches includes one or more mismatches, obtaining, by the one or more computers, a set of k-mer seeds from the genomic data read; and based on the set of k-mer seeds from the genomic data read, selecting, by the one or more computers, an actual alignment for the genomic data read.