Query-Driven Omics Data Analysis with Automated Preprocessing
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Solution Overview
Problem
The existing GEO database system faces challenges in dataset curation integrity, complex data management, and requires specialized expertise for API usage, leading to analytic inaccuracies and increased research costs.
Innovation Solution
A system and method for data preprocessing and analytics that constructs complex queries, transcodes datasets, and provides an interactive GUI for exploring and configuring datasets, enabling efficient analysis and visualization of omics data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis of high-throughput sequencing data is performed, then analysis accuracy can be maintained, but the process becomes impossible to complete due to the huge amount of data
Solution Approach 1:
The patent introduces an intermediary processing layer between raw sequencing data and final analysis results. This layer includes automated quality control filters, data normalization procedures, and preprocessing pipelines that transform raw data into analysis-ready formats, enabling efficient handling of huge datasets without manual intervention while preserving analytical accuracy
Solution Approach 2:
The analysis process is divided into discrete, automated segments including quality control, trimming, alignment, and variant calling. Each segment can be independently optimized and executed, allowing the system to process huge amounts of data through a series of manageable automated steps rather than requiring holistic manual analysis
2Ease of manufacture
If 3rd party curation services are used for GEO datasets, then data submission is simplified, but curation integrity and data correlation accuracy deteriorate
Solution Approach 1:
The system implements automated self-curation capabilities where datasets undergo automatic quality assessment, metadata validation, and integrity checking upon submission. The platform performs self-verification of data correlations and relationships without relying on external 3rd party curators, maintaining both submission simplicity and curation integrity through algorithmic validation
Solution Approach 2:
The system incorporates feedback mechanisms where curation quality metrics are continuously monitored and fed back into the submission process. Automated validation rules provide immediate feedback on data quality issues, allowing submitters to correct problems before final ingestion, thereby maintaining high curation integrity while keeping the submission process simple
3Adaptability or versatility
If complex API interfaces are used to access E-Utilities, then data access capability is enhanced, but system complexity and expertise requirements increase
Solution Approach 1:
The patent implements a universal data access interface that consolidates multiple E-Utility access points into a single unified API. This universal interface handles diverse data retrieval operations (sequence search, gene expression data, structural data) through a common protocol, maintaining enhanced data access capability while reducing system complexity by eliminating the need to manage multiple separate API interfaces
Solution Approach 2:
The system merges multiple complex E-Utility interfaces into a single integrated access layer. By combining disparate data access functions into one unified interface, the system maintains versatile data access capabilities while significantly reducing the complexity burden on users, who now interact with a single standardized API rather than multiple specialized interfaces
4Quantity of substance
If millions of rows of big data are processed, then comprehensive analysis coverage is achieved, but resource consumption and processing time increase
Solution Approach 1:
The system performs preliminary data filtering, aggregation, and preprocessing operations before main analysis execution. By pre-processing data to identify and retain only relevant records, the system reduces the effective data volume requiring full computational analysis, thereby achieving comprehensive coverage of important data while reducing overall resource consumption
Solution Approach 2:
The system applies partial analysis strategies where not all millions of records receive full computational treatment. Instead, data is stratified into priority levels, with only the most relevant subsets undergoing intensive analysis while others receive summary-level processing. This approach maintains comprehensive analytical coverage of critical data while significantly reducing total resource consumption
Data Source
AI summary
A method for analyzing a dataset of biological measurement sample, the method comprises examining the dataset to enable or disable a set of configuration options from a plurality of configuration options, converting the dataset into a high-performance format, receiving a query that includes at least one query symbol, for which the dataset is to be searched, analyzing the dataset based on the query, and generating an output that is responsive to the query.