Reference-Free Sequencing Read Collapsing with Locality-Sensitive Hashing
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Solution Overview
Problem
Conventional read collapsing methods in nucleotide sequencing require alignment and sorting of reads, leading to super-linear computational complexity and limited applicability, especially in cases where high-quality reference sequences are not available, and they are unable to efficiently group similar reads without exhaustive pairwise comparisons.
Innovation Solution
The use of locality sensitive hashing (LSH) for read collapsing, which groups similar nucleotide sequencing reads based on virtual universal molecular indices (vUMIs) without the need for alignment, allowing for O(n) runtime complexity and enabling efficient grouping of reads from any sample, regardless of the organism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional alignment and sorting methods are used for read collapsing, then read grouping accuracy is improved, but computational complexity becomes super-linear and processing time increases
Solution Approach 1:
The patent replaces the mechanical alignment and sorting process with locality-sensitive hashing (LSH), which uses mathematical hash functions to directly group similar reads without requiring alignment to reference sequences. This substitution eliminates the super-linear computational complexity while maintaining grouping accuracy through probabilistic hashing techniques.
Solution Approach 2:
The patent changes the parameter of sequence representation from aligned sequences to k-mer hash signatures. By transforming reads into hash-based representations based on their k-mer content, the system achieves O(n) processing complexity while maintaining the ability to accurately group reads from the same original molecule.
2Reliability
If alignment-based methods are used, then read collapsing reliability is improved, but adaptability to samples without high-quality reference sequences deteriorates
Solution Approach 1:
The patent extracts the reference sequence dependency from the read collapsing process by using LSH on k-mers. This extraction allows the method to function independently of reference sequences, enabling reliable read grouping for any sample organism regardless of whether high-quality references are available.
Solution Approach 2:
The patent creates a universal read collapsing method that works across all organisms and sample types by using LSH on k-mer compositions. This universal approach eliminates the need for organism-specific reference alignment, making the same algorithm applicable to bacteria, viruses, fungi, and other diverse biological samples.
3Measurement precision
If exhaustive pairwise comparisons are performed to group similar reads, then grouping completeness is improved, but computational resources and time consumption increase
Solution Approach 1:
The patent performs preliminary hashing of k-mers from reads before any grouping decision is made. By pre-computing hash signatures for all reads and storing them in a data structure, the system enables O(1) lookup during grouping, completely eliminating the need for time-consuming exhaustive pairwise comparisons while maintaining complete grouping accuracy.
Data Source
AI summary
Disclosed herein are systems and methods for collapsing sequencing reads and identifying similar sequencing reads. In one example, a method includes generating a plurality of first identifier subsequences from a first identifier sequence of each nucleotide sequencing read and generating a first signature for the nucleotide sequencing read by applying hashing to the plurality of first identifier subsequences. The method may include assigning the nucleotide sequencing read to a first particular bin of a first data structure based on the first signature and determining a nucleotide sequence for each first particular bin of the first data structure with one or more nucleotide sequencing reads assigned.