Sliding Window Genome Methylation Analysis
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Solution Overview
Problem
Current genome methylation sequencing methods face inefficiencies in alignment processes and errors from upstream experiments, leading to inaccurate methylation level identification due to long alignment times and low resolution, especially in identifying methylation levels at specific sites or regions.
Innovation Solution
A method involving the acquisition of genome methylation sequencing data, alignment to a reference genome, and the use of a sliding window approach with a step size smaller than the window length to count and analyze methylation indices across the alignment result, integrating data from multiple window positions to reduce errors and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a sliding window approach with overlapping positions is used to count methylation indices, then the accuracy of methylation evaluation is improved by reducing local errors, but the computational time and processing complexity increase
Solution Approach 1:
The alignment result is segmented into multiple windows that slide across the sequence. Each window counts methylation indices independently at different positions, and these results are integrated to produce the final methylation level. This segmentation allows overlapping coverage of the same genomic regions from multiple window positions, reducing the impact of local errors while maintaining manageable computational segments.
2Manufacturing precision
If the window step size is made smaller than the window length to ensure overlapping coverage, then the resolution of methylation detection is improved, but the number of windows and computational load increase
Solution Approach 1:
The window is designed to slide dynamically across the alignment result with a step size smaller than the window length. This creates overlapping windows at different positions, where each position provides a slightly different view of the methylation landscape. The dynamic sliding approach ensures comprehensive coverage and allows integration of multiple measurements to improve detection resolution while systematically managing the number of window positions.
Data Source
AI summary
A method for analyzing genome methylation sequencing data includes: acquiring a genome methylation sequencing sequence to be detected and a reference genome sequence; aligning the genome methylation sequencing sequence to the reference genome sequence so as to obtain an alignment result; constructing a window, and moving the window from a first end of the alignment result to a second end of the alignment result successively, and counting a methylation index of a part of the alignment result covered by the window at a different position during each movement, a step size of each movement of the window being smaller than a length of the window; and analyzing a counted methylation index of the part of the alignment result covered by a window at a respective different position, and outputting a comprehensive methylation evaluation result of the genome methylation sequencing sequence.