Multiple Sequence Alignment via Gradient Descent Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional multiple sequence alignment methods are inefficient and inaccurate due to their reliance on sequential or pair-wise adjustments, which lead to increased computing costs and time as sequence length or number of sequences increases, often requiring approximations that undermine alignment quality.
Innovation Solution
A method that models sequences as connected by flexible spaces, using gradient descent to iteratively converge on optimal gap placements between bases, allowing for concurrent alignment of all input sequences and minimizing total alignment time by making incremental changes proportional to gradient tensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sequential or pair-wise alignment methods are used, then alignment accuracy can be maintained through careful adjustment, but computing time and resources increase dramatically as sequence length or number of sequences increases
Solution Approach 1:
The patent merges all sequence alignments into a single concurrent optimization problem. Instead of performing sequential or pair-wise alignments, the method formulates MSA as optimizing a cost function that simultaneously considers all sequences, allowing parallel adjustment of all alignments toward the global optimum.
Solution Approach 2:
The patent changes the parameter representation from discrete gap placements to continuous floating-point variables representing space lengths. This transformation enables the use of gradient descent optimization, where parameters are iteratively adjusted based on calculated gradients, efficiently navigating the solution space without exhaustive search.
2Measurement precision
If exhaustive search across combinatorial space of possible alignments is performed, then optimal alignment can be found, but computing resources and time become prohibitively expensive
Solution Approach 1:
The patent replaces the mechanical exhaustive search process with a mathematical optimization approach using gradient descent. Instead of systematically enumerating all possible alignments, the method uses calculus-based gradient calculation to directly guide the search toward the optimum, substituting computational brute force with mathematical efficiency.
Solution Approach 2:
The patent introduces a cost function as an intermediary between the alignment configurations and the optimization process. This cost function quantifies alignment quality, and its gradient serves as a mediator that guides the iterative adjustment of alignments toward optimality without requiring direct evaluation of all combinatorial possibilities.
3Productivity
If approximations are used to complete alignment in reasonable time, then computing time is reduced, but alignment accuracy is undermined
Solution Approach 1:
The patent introduces dynamics into the alignment process by using iterative gradient descent optimization. The floating-point space variables are dynamically adjusted in each iteration based on calculated gradients, allowing the system to adaptively converge toward the optimal alignment rather than relying on static approximation methods.
Solution Approach 2:
The patent implements feedback through the gradient calculation process. The cost function gradient provides continuous feedback about the direction and magnitude of adjustments needed, guiding each iteration toward improved alignment. This feedback mechanism ensures that computational efforts are directed toward actually improving alignment quality rather than random sampling.
Data Source
AI summary
Embodiments of the invention are directed to a method for aligning multiple sequences that models a sequence as a series of bases, wherein adjacent bases are connected through flexible spaces with variable lengths that represent extra spaces between adjacent bases. The method begins with creating an array of floating-point variables that represent spaces for bases within sequences. The method iteratively converges to the optimum set of spaces by gradient descent. In a non-limiting example of the method, the gradients of a space can be obtained by calculating a cost value used to increment or decrement the corresponding space by an integer amount. After each iteration, the spaces are updated by summing the calculated gradients scaled by a change rate, and the sequences are updated by adding or removing gaps between each base according to the quantized spaces. The final sequences become the solution to the multiple sequence alignment task.