Bioinformatics Pipeline Orchestration with Dynamic Tool Reconfiguration
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Solution Overview
Problem
Existing bioinformatics pipelines do not allow for real-time modifications, leading to long execution times and wastage of computational resources due to their deterministic and fixed nature.
Innovation Solution
Implementing a method that uses machine learning to dynamically modify bioinformatics processing pipelines by analyzing the output of one tool and adjusting the sequence of tools, allowing for the inclusion of additional tools or parallel processing, thereby creating a self-morphing workflow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a deterministic and fixed bioinformatics pipeline is used, then the pipeline structure is simple and easy to manage, but the execution time is long and computational resources are wasted due to inability to adapt
Solution Approach 1:
The patent applies dynamics by transforming the static, fixed pipeline into a dynamic system that can modify its structure during runtime. The orchestration engine continuously monitors tool outputs and dynamically adds, removes, or reorders processing tools based on data characteristics, thereby reducing unnecessary processing steps and execution time while improving adaptability.
Solution Approach 2:
The patent implements feedback mechanisms where the orchestration engine analyzes the output of each processing tool and uses this information to make real-time decisions about pipeline modifications. This feedback loop enables the system to adapt the pipeline structure based on actual data characteristics and processing results, optimizing execution efficiency.
2Adaptability or versatility
If a deterministic and fixed bioinformatics pipeline is used, then the pipeline design is straightforward, but computational resources are wasted due to lack of real-time modifications
Solution Approach 1:
The system dynamically adjusts the pipeline configuration during execution based on monitored data characteristics and processing outcomes. This prevents wasteful computation by adapting the pipeline to only perform necessary processing steps, thereby reducing computational resource consumption while enabling real-time modifications.
Solution Approach 2:
The orchestration engine uses feedback from tool outputs to make intelligent decisions about pipeline modifications. By analyzing processing results in real-time, the system can identify and eliminate redundant computational steps, optimizing resource utilization while maintaining adaptability.
3Productivity
If traditional bioinformatics pipelines are used, then the workflow is simple to implement, but processing efficiency is low due to fixed sequence of tools
Solution Approach 1:
The patent employs dynamics to create a flexible pipeline structure that can be reconfigured during runtime. The orchestration engine dynamically adjusts the sequence and selection of processing tools based on data characteristics, improving processing efficiency by optimizing the workflow in real-time while managing complexity through automated control.
Solution Approach 2:
The system implements self-service by enabling the pipeline to automatically optimize its own structure without external intervention. The orchestration engine autonomously monitors processing state, analyzes outputs, and makes decisions about pipeline modifications, thereby improving efficiency while keeping the system relatively simple to implement.
4Adaptability or versatility
If a fixed bioinformatics pipeline is used, then the initial setup is simple, but the pipeline cannot adapt to different data characteristics during processing
Solution Approach 1:
The patent implements feedback mechanisms where the orchestration engine continuously monitors tool outputs and uses this information to dynamically adjust the pipeline. This feedback-driven approach enables automatic adaptation to different data characteristics while maintaining a relatively simple initial setup, as the system learns and adjusts during execution rather than requiring complex preconfiguration.
Solution Approach 2:
The system applies self-service by enabling automatic pipeline optimization without extensive manual configuration. The orchestration engine autonomously adapts the pipeline to different data characteristics through real-time analysis and decision-making, reducing the automation complexity burden while achieving high adaptability.
Data Source
AI summary
Computer software that performs the following operations: (i) identifying a bioinformatics dataset and instructions for processing the bioinformatics dataset, the instructions identifying a sequence of bioinformatics processing tools including at least a first bioinformatics processing tool followed by a second bioinformatics processing tool; (ii) instructing the first bioinformatics processing tool to process the bioinformatics dataset in accordance with the instructions; (iii) analyzing an output of the first bioinformatics processing tool, utilizing a machine learning based decision model, to determine a modification to the sequence of bioinformatics processing tools; and (iv) instructing a third bioinformatics processing tool to process at least a first portion of the bioinformatics dataset in accordance with the determined modification.