ATAC-seq Data Normalization via Control Peak Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for analyzing chromatin open regions using ATAC-seq data lack effective quantification and normalization techniques, hindering their application in epigenetic studies and predicting responsiveness to anti-PD-1 therapy in cancer patients.
Innovation Solution
A method is developed for selecting normalization control peaks and differential peaks by aligning and peak calling ATAC-seq data, using overlapping peaks, DNase I hypersensitivity consensus peaks, and coefficient of variation criteria to normalize data and predict anti-PD-1 therapy responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ATAC-seq data is used to identify chromatin open regions, then the analysis speed and sensitivity are improved, but the ability to quantify and normalize the data for epigenetic studies is insufficient
Solution Approach 1:
The patent introduces normalization control peaks as intermediary elements that mediate between raw ATAC-seq data and quantitative epigenetic analysis. These control peaks serve as reference points to normalize chromatin accessibility measurements across different samples, enabling precise quantification while preserving the high throughput nature of ATAC-seq
Solution Approach 2:
The patent transforms ATAC-seq data from qualitative peak identification to quantitative measurement by applying normalization factors derived from control peaks. This parameter change enables the data to be used for epigenetic studies requiring precise quantification of chromatin accessibility levels
2Measurement precision
If normalization control peaks are selected using multiple criteria (overlapping peaks, DNase I hypersensitivity consensus, coefficient of variation), then the normalization accuracy is improved, but the complexity of the selection process increases
Solution Approach 1:
The patent segments the normalization control peak selection process into distinct sequential steps: identifying overlapping peaks across samples, filtering by DNase I hypersensitivity consensus regions, and finally selecting peaks with appropriate coefficient of variation. This segmentation makes the complex process more manageable and systematic
Solution Approach 2:
The patent performs preliminary filtering actions before final peak selection by first identifying overlapping peaks across multiple samples and then filtering by DNase I hypersensitivity consensus regions. This preliminary action reduces the candidate pool and simplifies the subsequent selection of peaks based on coefficient of variation
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 approach enables easy normalization and quantitative comparison of ATAC-seq data, facilitating epigenetic studies and improving disease diagnosis and prognosis prediction, particularly for anti-PD-1 therapy responsiveness in gastric cancer patients.
Implementation Method 1
Tn5 transposase cleaves long DNA strands in a process called tagmentation. Tagmentation refers to the action of simultaneously 'tagging' and 'fragmentation' of DNA with a Tn5 transposase preloaded with a sequencing adapter.
Implementation Method 2
the tagged DNA fragment is purified, amplified by polymerase chain reaction (PCR), and sequencing of the amplified product is performed
Data Source
AI summary
The present invention relates to ATAC-seq data normalization for utilizing epigenetic information associated with chromatin openness, and a method for utilizing same. According to the present invention, it is possible to readily normalize and quantitatively compare ATAC-seq in various samples and various cohorts, and selected differential peaks can be used in various epigenetic studies, the diagnosis of diseases, and prediction of the prognoses of diseases.