Analyte Data Preparation via Color Information Extraction
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Solution Overview
Problem
Current methods for identifying nucleic acid sequences using multiple coloring rounds generate large data volumes, leading to high memory and computing requirements, and SSD hard drives face limitations in write cycles and throughput due to these large data sets.
Innovation Solution
A method that reduces data volume by eliminating irrelevant color information, using a camera to detect markers and store only candidate data points, and employing machine learning models to assess and combine color information from neighboring pixels, thereby reducing memory needs and processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all color information from multiple coloring rounds is stored for evaluating analytes, then measurement precision is improved, but data volume increases leading to high memory requirements and increased write cycles on SSD hard drives
Solution Approach 1:
The patent extracts and stores only the relevant color information data points that are necessary for analyte identification, eliminating redundant background color information. This selective extraction reduces the quantity of stored data while preserving the essential information needed for precise analyte identification through multiple coloring rounds.
Solution Approach 2:
The patent applies different quality standards to different portions of the data: relevant color information associated with analyte locations is stored with high fidelity, while background color information is either stored with reduced quality or eliminated entirely. This local differentiation optimizes storage efficiency without compromising measurement precision for the actual analytes.
2Measurement precision
If large data volumes from multiple coloring rounds are processed, then analyte identification accuracy is improved, but computing effort and processing time increase significantly
Solution Approach 1:
The patent extracts only the essential color information data points that contribute to analyte identification, removing redundant background information. This extraction reduces the total computing effort required for data processing while maintaining the accuracy needed for identifying analytes through multiple coloring rounds.
Solution Approach 2:
The patent processes only the necessary portion of the color information data rather than all available data. By applying partial action - processing only the relevant data points associated with analyte locations - the system achieves sufficient identification accuracy without the excessive computational burden of processing complete raw data sets.
3Speed
If SSD hard drives are used to store large data volumes, then rapid data access is achieved, but the limited number of write cycles is quickly exhausted
Solution Approach 1:
The patent extracts and stores only the essential color information data points, significantly reducing the total data volume that must be written to the SSD hard drive. This reduction in write volume extends the operational lifetime of the SSD by preserving its limited write cycles, while still maintaining rapid data access performance for the compressed data set.
4Loss of information
If all color information is stored and processed, then complete data availability is maintained, but memory requirements and acquisition costs increase
Solution Approach 1:
The patent extracts and retains only the color information data points that are essential for analyte identification, eliminating redundant background information. This selective extraction reduces memory capacity requirements and acquisition costs while maintaining sufficient data completeness for accurate analyte identification through multiple coloring rounds.
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 allows for efficient and economical processing of color information, reducing memory requirements and write cycles, and enhancing sample throughput by storing only relevant data points and using compressed data for further analysis.
Implementation Method 1
The markers are made up of oligonucleotides and dyes coupled thereto, which are generally fluorescent dyes
Data Source
AI summary
A method for preparing data for identifying analytes in a sample, one or more analytes being colored with markers in multiple coloring rounds in an experiment, the markers in each case being specific for a certain set of analytes, the multiple markers being detected using a camera, which for each coloring round generates at least one image may contain color information of one or more markers, and the color information of the particular coloring rounds being stored for the evaluation.