Structural Variant Detection Using Targeted Contig Assembly
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Solution Overview
Problem
Existing methods for detecting structural variants (SVs) in genomic data, particularly complex SVs, are limited by false positives from problematic alignment and computational inefficiencies, especially in long-read sequencing data.
Innovation Solution
A pipeline combining alignment-based and assembly-based methods, including encoding SV signals, detecting candidate regions, clustering reads, assembling contigs, and aligning them to a reference genome, deferring SV type assignment until the final alignment step, using tools like wtdbg2 and copmem2 for enhanced detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If alignment-based methods are used for SV detection, then simplicity and efficiency are improved, but false positive findings increase due to problematic alignment and detection of complex SVs is limited
Solution Approach 1:
The method segments the SV detection process into distinct phases: initial alignment-based screening to identify candidate regions, followed by localized assembly-based validation in those specific regions. This segmentation allows the system to use the efficient alignment-based approach for most of the genome while applying the more reliable assembly-based approach only where needed, thus reducing false positives without sacrificing overall efficiency.
Solution Approach 2:
The patent introduces an intermediary validation step using assembly-based methods that acts as a mediator between the initial alignment-based detection and final SV calling. This intermediary process re-assembles reads in candidate regions and re-aligns them to verify SV calls, effectively filtering out false positives generated by problematic alignments in the first stage.
2Reliability
If assembly-based methods are used for SV detection, then false positive findings are reduced, but computational expense increases making large-scale data analysis unsuitable
Solution Approach 1:
The method divides the genome into candidate regions based on initial alignment-based SV signals, and applies computationally intensive assembly-based methods only to these segmented regions rather than the entire genome. This segmentation strategy maintains high reliability in SV detection while preserving computational efficiency for large-scale data analysis.
Solution Approach 2:
The patent applies assembly-based methods partially - not to the entire genome but only to candidate regions where SV signals were initially detected. This partial application of the computationally expensive method provides sufficient validation where needed without the excessive computational cost of genome-wide assembly-based analysis.
3Device complexity
If alignment-based methods determine SV type from initial step, then processing is simplified, but detection of complex SVs is limited
Solution Approach 1:
The method performs preliminary alignment-based analysis to identify candidate SV regions and signals, then uses this preliminary information to guide subsequent assembly-based analysis in those specific regions. This preliminary action allows the system to maintain a relatively simple initial pipeline while enabling complex SV detection through the targeted follow-up assembly step.
Solution Approach 2:
The patent adds a second analytical dimension by introducing assembly-based analysis as a follow-up step to the initial alignment-based approach. This dimensional addition allows the system to detect complex SVs that may be missed or misclassified by alignment-based methods alone, without completely redesigning the entire detection pipeline.
Data Source
AI summary
Methods and systems for detecting structural variants (SVs) from long-read sequencing data. The method including: encoding SV signals from aligned reads in a binary alignment map (BAM) file, wherein the SV signals are encoded in a matrix form; detecting one or more candidate regions from the encoded SV signals, wherein the one or more candidate regions comprise SV signals above a pre-determined signal level; clustering the aligned reads within each of the one or more candidate regions to form one or more clusters, respectively; assembling the aligned reads within each of the one or more clusters to generate one or more contigs, respectively; aligning the one or more contigs to a respective reference sequence to detect a presence of SVs.