Target-Enriched Sequence Read Assignment via Capture Hybridization
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Solution Overview
Problem
General-purpose aligners like BWA do not integrate target enrichment parameters, leading to longer processing times and reduced accuracy when assigning sequence reads to genomic locations in Next Generation Sequencing workflows, especially for samples enriched using target enrichment panels.
Innovation Solution
A computer-implemented method that accesses sequence reads from nucleic acid samples enriched by hybridization to capture sequences, identifies matches by calculating sequence similarity scores, and assigns reads to genomic locations based on these scores, using a threshold to determine accurate alignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If general-purpose aligners like BWA are used to assign sequence reads to genomic locations, then the alignment can be performed across the whole genome, but the processing time increases and accuracy decreases for target-enriched samples
Solution Approach 1:
The patent extracts and utilizes the target enrichment information (capture sequences) from the sequencing data to create a specialized alignment approach. By taking out this specific information that general-purpose aligners ignore, the method achieves both faster processing and higher accuracy for target-enriched samples
Solution Approach 2:
The patent applies local quality by using different alignment strategies for different types of sequencing data. For target-enriched samples, it uses a specialized method that incorporates capture sequence information, while general-purpose aligners remain appropriate for whole-genome sequencing. This localized optimization improves performance where needed without affecting other applications
2Adaptability or versatility
If general-purpose aligners are used, then the algorithm can process any sequencing data, but it does not integrate target enrichment parameters resulting in reduced accuracy
Solution Approach 1:
The patent makes the alignment algorithm dynamic by allowing it to adapt its behavior based on the type of sequencing data being processed. The method can switch between using general-purpose alignment and specialized target-enriched alignment approaches, optimizing performance for each data type while maintaining versatility across different sequencing workflows
3Productivity
If general-purpose aligners are used, then the whole genome reference data can be utilized, but the processing efficiency decreases for target-enriched samples
Solution Approach 1:
The patent performs preliminary action by extracting and storing capture sequence information before the alignment process. This pre-processing step creates an index or reference structure that allows the alignment algorithm to quickly utilize target enrichment information during processing, improving efficiency without losing valuable enrichment data
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method significantly reduces processing time and improves accuracy by leveraging target enrichment information, allowing for efficient assignment of sequence reads to specific genomic locations, thereby enhancing the efficiency of Next Generation Sequencing workflows.
Implementation Method 1
a file containing a sequence read, wherein the sequence read is obtained from a nucleic acid sample that has been enriched by hybridization to a plurality of capture sequences
Data Source
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AI summary
Provided herein, among other things, is a computer-implemented method for assigning a sequence read to a genomic location, the method including: a) accessing a file containing a sequence read, wherein the sequence read is obtained from a nucleic acid sample that has been enriched by hybridization to a plurality of capture sequences; and b) assigning the sequence read to a genomic location by: i) identifying a capture sequence as being a match with the sequence read if the sequence read contains one or more subsequences of the capture sequence; ii) calculating, using a computer, a score indicating the degree of sequence similarity between each of the matched capture sequences and the sequence read; and iii) assigning the sequence read to the genomic location if the calculated score for a matched capture sequence is above a threshold.