Nucleic Acid Sequence Snippet Filtering for Malicious DNA Detection
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Solution Overview
Problem
Current techniques struggle to detect malicious nucleic acid sequences that could be reassembled from short or hybrid DNA fragments, posing a risk of synthesizing harmful organisms.
Innovation Solution
The system identifies 'signatures' of malicious organisms by comparing short snippets of known malicious sequences to benign sequences, using probabilistic data structures like Bloom filters to efficiently filter out benign snippets and identify unique malicious signatures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current sequence recognition techniques are used, then sequences as short as 200 base pairs can be detected, but malicious organisms can be synthesized by clipping and stitching small DNA segments that evade detection
Solution Approach 1:
The patent segments malicious organism sequences into smaller k-mer units (subsequences of length k) to create unique signatures. By breaking down sequences into manageable segments and comparing these segments against a database of benign sequences, the system can identify malicious patterns even when the overall sequence is fragmented or hybridized. This segmentation approach allows detection of malicious organisms composed of clipped and stitched DNA segments.
Solution Approach 2:
The patent performs preliminary action by pre-computing and storing k-mer signatures for both benign and malicious sequences in a database before actual detection occurs. Bloom filters are pre-built from benign sequence data, and candidate malicious signatures are identified in advance through comparison. When a new sequence needs detection, the pre-computed signatures and filters enable rapid identification without requiring full sequence analysis at detection time.
2Reliability
If comprehensive sequence analysis is performed to ensure safety, then malicious sequences can be detected, but synthesis time and processing complexity increase significantly
Solution Approach 1:
The patent replaces traditional mechanical sequence comparison methods with probabilistic data structures, specifically Bloom filters. Instead of performing exhaustive pairwise comparisons between query sequences and database sequences, the system uses Bloom filters to efficiently test whether k-mer signatures from the query sequence exist in the benign sequence database. This substitution dramatically reduces computational complexity from O(n*m) to O(n*k) where n is the number of k-mers in the query, k is the Bloom filter size, and m would be the database size.
Solution Approach 2:
The patent changes parameters by using k-mers of variable length k and adjusting Bloom filter parameters (filter size, number of hash functions) to optimize the balance between false positive rate and memory usage. By tuning these parameters, the system can achieve high detection reliability while maintaining fast processing speeds. The ability to adjust k and filter parameters allows optimization for different detection scenarios and resource constraints.
3Adaptability or versatility
If short DNA snippets are used for assembly, then flexible organism construction is enabled, but detection becomes more difficult as malicious sequences can be hidden in fragments
Solution Approach 1:
The patent creates copies of sequence segments in the form of k-mer signatures that can be independently compared against a database. Instead of requiring the complete original sequence for detection, the system extracts multiple k-mer copies from the query sequence and tests each against the Bloom filter. This copying approach allows detection to proceed with fragmented sequences, as long as enough characteristic k-mers from the malicious portion are present in the fragments.
Data Source
AI summary
Disclosed techniques include generating a first set of sequence snippets from a set of non-contiguous nucleic acid sequences having a first trait; generating a second set of second sequence snippets from a set of non-contiguous nucleic acid sequences having a second trait; identifying a third set of sequence snippets categorized as being of a particular type; and filtering the first set of sequence snippets and the second set of sequence snippets to remove at least one sequence snippet in the third set of sequence snippets.