Reduced Computation Hidden Markov Model for Genomic Variant Calling
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Solution Overview
Problem
Current bioinformatics methods for constructing genomic sequences and determining variants are labor-intensive, time-consuming, and prone to errors due to the complexity of processing large DNA sequence fragments and the presence of noise and error sources in sequencing data.
Innovation Solution
A reduced computation hidden Markov model (HMM) method and system that pre-filters and processes genomic data using a HMM pre-filter engine to generate control parameters, selecting a reduced number of HMM matrix cells for computation, thereby accelerating the variant calling process and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a full HMM matrix computation is performed on all cells, then the variant calling accuracy is maintained, but the computational time and resources increase significantly
Solution Approach 1:
The HMM matrix is segmented into multiple blocks or regions, where only the most relevant blocks are computed in full detail while less critical blocks use approximations or are skipped entirely. This allows the system to maintain accuracy for the most important variant calls while reducing overall computational burden.
Solution Approach 2:
The system performs partial computation of the HMM matrix by calculating only the necessary portions required for accurate variant calling. By identifying and computing only the critical cells that contribute most to the final accuracy, the system achieves sufficient precision without the excessive computational cost of full matrix evaluation.
2Reliability
If all HMM matrix cells are computed, then comprehensive variant detection is achieved, but the computational complexity increases
Solution Approach 1:
The system performs preliminary filtering and preprocessing steps before the main HMM computation, identifying and prioritizing the most likely variant regions. This preliminary action allows the subsequent HMM computation to focus only on high-probability areas, reducing overall computational complexity while maintaining detection comprehensiveness.
Solution Approach 2:
Different regions of the HMM matrix are treated with different levels of computational quality. High-priority regions receive full computational treatment to ensure reliable variant detection, while lower-priority regions use simplified models or are excluded from computation, thereby reducing overall complexity while maintaining comprehensive detection capability.
3Measurement precision
If the complete HMM computation process is used, then accurate genomic sequence construction is achieved, but the processing speed decreases
Solution Approach 1:
The system dynamically adjusts the level of HMM computation based on real-time data characteristics, read quality scores, and regional importance. This dynamic approach allows the system to maintain high accuracy for critical regions while using faster, simplified methods for less critical areas, thereby improving overall processing speed without sacrificing genomic sequence accuracy.
Solution Approach 2:
The system changes computational parameters such as matrix resolution, computation depth, and precision levels based on the specific requirements of different genomic regions. By adjusting these parameters dynamically, the system achieves accurate genomic sequence construction where needed while maintaining high processing speed in less critical areas.
Data Source
AI summary
A system and method for a reduced computation hidden markov model (HMM) in computational biology applications is disclosed herein. The method includes performing a correlation between the haplotype sequence and the read sequence at the HMM pre-filter engine. The method includes computing a MINI metric from a reduced number of cells at the HMM computation engine.