CNV Calling With HMM Correction for Targeted Sequencing Bias
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Solution Overview
Problem
Next-generation sequencing (NGS) technologies face challenges in accurately detecting copy number variants (CNVs) due to analytical limitations from sample preparation, sequencing, GC content, target size, and sequence complexity, which affect the relationship between read depth and copy number, leading to inaccuracies in CNV detection.
Innovation Solution
A method using direct targeted sequencing and a hidden Markov model (HMM) to determine copy numbers, accounting for GC bias, sample noise, and spurious capture probes, with a copy number likelihood model adjusted to fit sequencing read data, optimizing the HMM for accurate CNV detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If NGS panel-based testing is used to detect copy number variants, then multiple genes can be tested simultaneously at comparable cost, but analytical limitations from sample preparation, sequencing, GC content, target size, and sequence complexity reduce measurement precision
Solution Approach 1:
The patent applies preliminary action by performing GC bias correction and noise characterization before copy number variant calling. The method pre-processes sequencing data to account for GC content effects and technical variability, establishing corrected read depth metrics that are then used for accurate CNV detection. This preliminary processing step removes systematic biases before the actual measurement is made.
Solution Approach 2:
The patent introduces an intermediary approach by using a copy number likelihood model that incorporates GC bias correction factors and noise parameters as intermediate variables. Rather than directly comparing raw read depths, the method uses these corrected intermediate values to infer copy number states, improving measurement precision while maintaining multi-gene testing capability.
2Measurement precision
If traditional Sanger sequencing is used for germline testing, then single gene mutation detection is achieved, but testing multiple genes simultaneously is not possible
Solution Approach 1:
The patent applies universality by developing a copy number likelihood model that can simultaneously analyze multiple genes and detect various types of variants including CNVs, deletions, and duplications. The same analytical framework and sequencing approach used for single-gene testing is extended to multi-gene panels, allowing the system to perform multiple functions (testing different genes, detecting different variant types) with a unified method.
3Measurement precision
If microarrays are used to complement NGS for CNV detection, then detection accuracy is improved, but device complexity and bias are increased
Solution Approach 1:
The patent extracts and corrects the specific biases (GC content effects, capture efficiency variations, noise) that normally require microarray complementation. By identifying and mathematically correcting for these individual bias sources within the NGS data itself, the method eliminates the need to add microarray technology, thereby maintaining measurement precision while avoiding increased device complexity.
Solution Approach 2:
The patent creates a computational copy of the bias correction process that mimics what microarrays would accomplish physically. Instead of using microarrays to independently measure copy numbers, the method creates a corrected computational representation of copy number likelihoods that accounts for all known biases, achieving similar accuracy through data processing rather than additional hardware.
Data Source
AI summary
Direct targeted sequencing (DTS) methods and a hidden Markov model (HMV) can be used to call the copy number of a segment of interest within a region of interest. Described herein are methods for calling a copy number variant or a copy number variant abnormality using an HMM, and methods for determining a copy number based on a copy number likelihood model, in a test sequencing library that has be sequenced using DTS methods. Also described herein are methods for determining a copy number of a segment, including accounting for spurious capture probes that may arise from the DTS methods.