Molecular Phenotype Neural Networks for Biological Sequence Variant Scoring
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Solution Overview
Problem
Current methods for genome and DNA sequence analysis are limited in their ability to accurately score and visualize biological sequence variations, particularly those outside of exons, as they fail to account for the regulatory elements and molecular phenotypes that contribute to disease-causing variants.
Innovation Solution
The use of molecular phenotype neural networks (MPNNs) to determine variant scores by comparing gradients for substitution, insertion, and deletion variants in biological sequences, allowing for the scoring and visualization of their impact on molecular phenotypes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing methods focus narrowly on exons and conserved regions, then analysis simplicity is maintained, but coverage of functional genomic elements is insufficient
Solution Approach 1:
The neural network model is designed to universally analyze multiple types of biological sequences (DNA, RNA, proteins) and predict multiple molecular phenotypes (binding affinity, stability, folding, etc.) within a single framework, enabling broad coverage of functional genomic elements without requiring separate specialized methods for each sequence type or phenotype
Solution Approach 2:
The system changes the analytical parameters by using learned embeddings and neural network predictions instead of traditional alignment-based or conservation-based parameters, allowing accurate scoring of variants in non-conserved and regulatory regions while maintaining computational efficiency through optimized network architectures
2Measurement precision
If neural networks are used to predict molecular phenotypes for all sequence positions, then variant scoring accuracy is improved, but computational cost increases
Solution Approach 1:
The system performs preliminary actions by pre-training neural network models on large datasets of molecular phenotypes and pre-computing embeddings for all possible k-mers and amino acid triples during model initialization, so that during actual variant scoring, only forward propagation through the trained network is required, significantly reducing per-variant computational cost while maintaining high accuracy
Solution Approach 2:
The system applies partial action by focusing computational resources on predicting only the specific molecular phenotypes relevant to the variant being analyzed, rather than computing all possible phenotypes, and uses gradient-based methods to compute only the necessary derivative information for variant scoring
Data Source
AI summary
Systems and methods for scoring and visualizing the effects of variants in biological sequences. Variants may include substitutions, insertions and deletions. The method comprises encoding biological sequences as vector sequences and then operating a neural network in the forward-propagation mode and possibly in the back-propagation mode to compute variant scores. Variant scores are determined by normalizing the gradients. Variant scores may be used to select a subset of variants, which are then used to produce modified vector sequences which are analyzed by the neural network operating in forward-propagation mode, to determine improved variant scores. The variant scores may be visualized using black and white, greyscale or colored elements that are arranged in blocks with dimensions corresponding to different possible symbols and the length of the sequence. These blocks are aligned with the biological sequence, which is illustrated by a symbol sequence arranged in a line.