Unaligned Sequencing Data Processing for ALS Diagnosis
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Solution Overview
Problem
Current methods for diagnosing amyotrophic lateral sclerosis (ALS) through whole genome sequencing are computationally expensive due to the alignment of reads to a reference genome, making them time-consuming and potentially inaccurate.
Innovation Solution
A computer-implemented method that processes unaligned sequencing data by determining sub-sequences, counting their occurrences in control and ALS groups, selecting significant sub-sequences based on change measures, and using these counts to generate a diagnostic output, bypassing the alignment process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If alignment of reads to reference genome is performed, then genetic markers can be found, but computational time and resources are excessively consumed
Solution Approach 1:
The patent extracts and counts specific sub-sequences (k-mers) directly from unaligned sequencing reads, bypassing the alignment step entirely. This extraction approach focuses only on the relevant genetic markers needed for ALS prediction, eliminating the computationally expensive alignment process while maintaining diagnostic accuracy
Solution Approach 2:
The patent segments the genome into fixed-length sub-sequences (k-mers) and processes them independently through counting operations. This segmentation allows for parallel processing and eliminates the need for complex alignment operations, significantly reducing computational time while preserving the ability to identify ALS-associated genetic markers
2Reliability
If alignment process is used, then genetic markers can be identified, but the process becomes computationally expensive
Solution Approach 1:
The patent replaces the complex mechanical alignment process with a simpler counting-based computational approach. Instead of aligning reads to a reference genome through iterative comparison operations, the system simply counts occurrences of specific sub-sequences, dramatically reducing computational resource consumption while maintaining the ability to identify ALS-associated genetic markers
Data Source
AI summary
This disclosure relates to computationally efficient processing of sequencing data relating to amyotrophic lateral sclerosis (ALS). A processor receives unaligned training reads and determines training sub-sequences from them. The processor then counts the training sub-sequences in a control group and in a group diagnosed with ALS and determines a measure of change, for each of the training sub-sequences, in the counting between the control group and the group with ALS. The processor further selects a subset of training sub-sequences that are distal from a mean value of the measure of change and then receives testing sequencing data comprising multiple unaligned testing reads. The processor determines sub-sequences from the testing reads, counts the sub-sequences that are in the subset, and determines a diagnostic output value related to ALS for the sample based on the counting of the testing sub-sequences that are in the subset.