DNA Read Clustering Using Edit Distance and Hash Approximation
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Solution Overview
Problem
Polynucleotide sequencers produce error-prone DNA reads, making it challenging to accurately cluster DNA sequences due to the high volume and noise in the data, which complicates error correction and requires computationally efficient methods to group reads representing the same original DNA strand.
Innovation Solution
The approach involves using edit distances and hash values to cluster DNA reads, where reads with similar edit distances or hash values are grouped together, and an iterative process is used to reduce computational expense by designating representative reads and omitting others from subsequent iterations, employing techniques like locality-sensitive hashing and binary signatures to approximate edit distances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional clustering methods are used on large volumes of noisy DNA reads, then comprehensive error correction is achieved, but computational complexity and processing time become prohibitive
Solution Approach 1:
The patent segments the clustering process into multiple iterative passes. In each pass, only a subset of reads (those not yet assigned to any cluster) is processed against existing cluster representatives. This divides the computationally intensive task of comparing all reads against all potential clusters into manageable segments across multiple iterations, reducing per-iteration complexity while maintaining comprehensive error correction across the entire dataset.
Solution Approach 2:
The patent performs preliminary actions by first computing hash values for all reads and organizing them into buckets before the iterative clustering begins. This preliminary organization based on hash similarity allows the algorithm to quickly identify candidate clusters without computing full edit distances for all read pairs, significantly reducing the computational burden of the subsequent iterative clustering process.
2Measurement precision
If edit distance calculations are performed for all DNA read pairs to ensure accurate clustering, then clustering precision is improved, but processing speed decreases significantly
Solution Approach 1:
The patent segments the population of DNA reads into multiple batches or iterations. In each iteration, only a portion of reads (specifically, those not yet assigned to clusters) undergo edit distance calculations against cluster representatives. This segmentation reduces the number of edit distance computations required in each processing step, thereby improving processing speed while still achieving precise clustering across the entire dataset through multiple passes.
Solution Approach 2:
The patent introduces hash values as an intermediary mechanism to pre-filter and organize reads before edit distance calculation. By computing hash values for all reads and grouping them into buckets, the system creates an intermediate organizational structure that allows rapid identification of candidate clusters. This intermediary step eliminates the need for exhaustive pairwise edit distance calculations, maintaining clustering precision while dramatically improving processing speed.
3Productivity
If hash values are used to approximate edit distance for faster clustering, then processing speed is improved, but measurement accuracy may be compromised
Solution Approach 1:
The patent employs periodic action by alternating between hash-based approximate distance calculations and more precise edit distance measurements across multiple iterative passes. In early iterations, hash values provide fast approximate filtering to quickly identify candidate clusters. As iterations progress and clusters become more refined, the system periodically applies precise edit distance calculations to verify and refine cluster assignments, ensuring measurement accuracy while maintaining overall processing efficiency through the periodic alternation between fast approximation and precise measurement.
Data Source
AI summary
A technique for clustering DNA reads from polynucleotide sequencing is described. DNA reads with a level of difference that is likely caused by errors in sequencing are grouped together in the same cluster. DNA reads that represent reads of different DNA molecules are placed in different clusters. The clusters are based on edit distance, which is the number of changes necessary to convert a given DNA read into another. The process of forming clusters may be performed iteratively and may use other types of distance that serve as an approximation for edit distance. Well clustered DNA reads provide a starting point for further analysis.