Bacterial Shape Gene Identification via Protein Domain ML
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Solution Overview
Problem
Current methods for identifying candidate genes that regulate bacterial shape are time-consuming and resource-intensive, relying on random mutagenesis and whole-genome association analysis, which require significant effort and resources.
Innovation Solution
A method involving obtaining reference genome data, performing protein domain analysis, determining feature value datasets, training a bacterial shape prediction model, and determining influence weights of protein domains to identify candidate genes that regulate bacterial shape, using machine learning algorithms like Random Forest to facilitate rapid screening.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional mutagenesis screening methods are used to identify candidate genes, then gene function can be studied, but the process requires substantial time and effort to screen meaningful mutations
Solution Approach 1:
The patent performs preliminary protein domain analysis on reference genome data before conducting shape analysis. By pre-identifying and categorizing protein domains (e.g., cell wall synthesis, cell division, cytoskeleton-related domains), the system prepares feature value datasets that can be directly used for shape prediction, eliminating the need for time-consuming trial-and-error mutation screening while maintaining identification accuracy
Solution Approach 2:
The patent replaces the mechanical/conventional mutagenesis screening process with a computational machine learning system. Instead of physically screening mutations through biological experiments, the system uses Random Forest algorithms to predict bacterial shape based on protein domain features, achieving both speed and accuracy in candidate gene identification
2Measurement precision
If whole-genome association analysis is used to identify candidate genes, then comprehensive gene screening can be achieved, but it requires considerable cost of resources, time, and effort
Solution Approach 1:
The patent extracts only the relevant protein domain features from the entire genome data. Instead of analyzing every gene through expensive whole-genome association studies, the system extracts and focuses on specific protein domains known to be involved in shape determination (such as cell wall, cytoskeleton, and division-related domains), significantly reducing computational and resource requirements while maintaining identification precision
Solution Approach 2:
The patent changes the analysis parameter from whole-genome sequencing and association testing to protein domain feature extraction and machine learning prediction. This parameter transformation allows the system to work with smaller, more manageable datasets of protein domain features rather than requiring comprehensive genomic data, thereby reducing resource consumption
3Adaptability or versatility
If random mutagenesis is used to generate mutations, then genetic variation can be obtained, but the mutation points are randomly distributed and some mutations may be ineffective or irrelevant
Solution Approach 1:
The patent applies local quality by focusing analysis on specific protein domains known to be critical for bacterial shape (such as cell wall synthesis domains, cytoskeleton domains, and cell division domains). Instead of randomly sampling mutations across the entire genome, the system concentrates on these locally important regions, ensuring that analyzed mutations are both meaningful and relevant to shape determination
Data Source
AI summary
The present application relates to a method, apparatus, device and medium for identifying candidate genes that regulate the shape of bacteria. The method includes: obtaining reference genome data of bacteria and performing protein domain analysis on the reference genome data of bacteria; determining the feature value dataset for each bacterium based on the structural domains of all proteins obtained from the analysis; obtaining shape information of each bacterium; training a bacterial shape prediction model based on the shape information of each bacterium and the feature value dataset, and determining the weights of each protein domain in influencing the shape of the bacterium according to the bacterial prediction model; determining candidate genes that regulate the shape of bacteria based on the weights. This method can be used to rapidly screen out the candidate genes that regulate the shape of bacteria, and establish a new method for mining biofunctional genes.