Hardware Processing Platform for Genomic Data Analysis
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Solution Overview
Problem
Current bioinformatics methods for analyzing genomic data are labor-intensive, time-consuming, and prone to errors, particularly in building full-length genomic sequences, which hinders the efficient processing and analysis of large-scale genomic data needed for personalized healthcare.
Innovation Solution
The implementation of a hardware processing platform with integrated circuits and software solutions that optimize bioinformatics protocols, enabling faster and more accurate analysis of genetic data through a combination of hardwired digital logic circuits and physical electrical interconnects, configured as processing engines for tasks like DNA/RNA sequencing and variant calling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If software-based bioinformatics methods are used for genomic data analysis, then flexibility and adaptability are maintained, but processing time increases and productivity decreases
Solution Approach 1:
The patent replaces software-based computational methods with hardware-based processing systems. Specifically, it uses Field-Programmable Gate Arrays (FPGAs) and Application-Specific Integrated Circuits (ASICs) to implement bioinformatics algorithms in hardware, thereby substituting the mechanical/software processing system with an electronic/hardware processing system that offers both high speed and reconfigurability.
Solution Approach 2:
The patent employs dynamically reconfigurable hardware architectures that can be programmed and reprogrammed to perform different bioinformatics tasks. The use of FPGAs allows the system to be dynamically configured for specific algorithms while maintaining the ability to adapt to new requirements, thus achieving both high productivity through hardware acceleration and flexibility through reconfigurability.
2Ease of operation
If software-based bioinformatics methods are used for genomic data analysis, then ease of operation is maintained, but time consumption increases and productivity decreases
Solution Approach 1:
The patent replaces time-consuming software-based processing with hardware-accelerated processing using FPGAs and ASICs. This substitution dramatically reduces the time required for genomic data analysis while maintaining ease of operation through high-level programming interfaces and automated workflow management systems that abstract the hardware complexity from the user.
3Productivity
If hardware processing platforms are implemented, then processing speed and productivity improve, but device complexity increases
Solution Approach 1:
The patent implements universal hardware processing platforms using FPGAs that can perform multiple bioinformatics functions through reconfiguration. A single hardware platform can be programmed to execute different algorithms (alignment, assembly, variant calling, etc.), thereby achieving high processing speed without proportionally increasing device complexity, as the same physical hardware serves multiple purposes.
Solution Approach 2:
The patent introduces intermediary software layers and abstraction interfaces that simplify the interaction between users and complex hardware systems. These intermediaries manage the complexity of hardware configuration and operation, allowing users to access high-speed processing capabilities without directly dealing with the underlying hardware complexity.
4Productivity
If hardware processing platforms are implemented, then processing speed improves, but manufacturing complexity increases
Solution Approach 1:
The patent replaces traditional software development and deployment processes with hardware-based implementations using FPGAs and ASICs. While hardware manufacturing is inherently more complex than software distribution, the use of standardized hardware platforms and reconfigurable architectures reduces the overall system complexity compared to building custom hardware for each application, thereby achieving high processing speed with manageable manufacturing complexity.
Data Source
AI summary
A system, method and apparatus for executing a sequence analysis pipeline on genetic sequence data includes a integrated circuit formed of a set of hardwired digital logic circuits that are interconnected by physical electrical interconnects. One of the physical electrical interconnects forms an input to the integrated circuit connected with an electronic data source for receiving reads of genomic data. The hardwired digital logic circuits are arranged as a set of processing engines, each processing engine being formed of a subset of the hardwired digital logic circuits to perform one or more steps in the sequence analysis pipeline on the reads of genomic data. Each subset of the hardwired digital logic circuits is formed in a wired configuration to perform the one or more steps in the sequence analysis pipeline.