MicroRNA Identification via Genomic Subsequence Alignment
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Solution Overview
Problem
Current methods for characterizing microRNAs from next-generation sequencing reads are inefficient due to short read lengths and the complexity of RNA folding analysis, leading to high computational demands and reduced accuracy, especially when dealing with star-miRNAs and genome-level applications.
Innovation Solution
A computer-implemented method that screens genomic neighborhoods for alignments with both the nucleic acid sequence and its reverse complementary, allowing for alignments with up to 0.6L errors within a 3000-nucleotide region, significantly reducing computational time while maintaining specificity and selectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sophisticated pipelines with RNA folding analysis are used to identify miRNAs, then measurement precision is improved, but computing time increases significantly
Solution Approach 1:
The method segments the genomic sequence into multiple subsequences and processes each subsequence independently for alignment. This divides the large-scale genome-level analysis into smaller, manageable units that can be processed faster while maintaining overall identification accuracy
Solution Approach 2:
The method extracts only the essential alignment information from genomic subsequences without performing complete RNA folding analysis on the entire genome. By taking out and analyzing only relevant regions that contain potential miRNA candidates, the computing time is reduced while preserving measurement precision
2Adaptability or versatility
If genome-level RNA folding analysis is performed, then miRNA detection coverage is improved, but device complexity increases
Solution Approach 1:
The genomic sequence is segmented into multiple subsequences that are processed independently. This segmentation enables genome-level coverage without requiring complex orchestration of entire-genome analysis, thereby reducing device complexity while maintaining detection coverage
Solution Approach 2:
Instead of performing RNA folding analysis on the entire genome and then identifying miRNAs, the method inverts the approach by first identifying candidate regions through alignment and then performing focused analysis. This inversion simplifies the analysis pipeline while maintaining genome-level detection capability
3Measurement precision
If multiple alignment checks including star-miRNA verification are performed, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The method performs preliminary alignment checks against multiple subsequences before conducting more rigorous verification steps. By establishing candidate regions early through efficient alignment, subsequent precision checks are performed only on relevant candidates, maintaining measurement precision while improving overall productivity
Data Source
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AI summary
The present invention relates to a computer-implemented method for determining if a nucleic acid sequence belongs to a microRNA (miRNA) candidate encoded by at least one known genomic sequence, comprising: a) determining if the nucleic acid sequence or the reverse complementary sequence thereof can fully align with a first subsequence of the known genomic sequence; b) then, - if the nucleic acid sequence fully aligns with the first subsequence of the known genomic sequence, determining if the reverse complementary sequence of the nucleic acid sequence can align with a second subsequence of the known genomic sequence with X error(s) at the most, or - if the reverse complementary nucleic acid sequence of the nucleic acid sequence fully aligns with the first subsequence of the known genomic sequence, determining if the nucleic acid sequence can align with a second subsequence of the known genomic sequence with X error(s) at the most; wherein X is an integer from 0 to 0.4L, wherein L is the length of the nucleic acid sequence, and a third subsequence of the known genomic sequence which consists of the first and second subsequences and of the portion of the known genomic sequence between the first and second subsequences has a length T, wherein T ≤ 3000 nucleotides; c) whereby, if conditions a) and b) are met, the nucleic acid sequence is determined to belong to a miRNA candidate. The method uses less computer power than the commonly used methods based on hairpin folding.