Convolutional Coding for DNA Insertion-Deletion Error Detection
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Solution Overview
Problem
DNA storage systems face significant challenges with insertion and deletion errors, which traditional error correction codes cannot effectively address, leading to increased sequencing time and data delivery delays.
Innovation Solution
A convolutional code system using two or more component convolutional codes with disjoint subsets and time interlacing, combined with trellis decoding, to detect and correct insertion and deletion errors in DNA sequences, allowing for reduced sequencing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sequencing operations are performed to minimize insertion and deletion errors, then reliability is improved, but loss of time worsens due to increased sequencing time
Solution Approach 1:
The patent applies preliminary action by encoding data with convolutional codes and creating syndromes before storage. This pre-processing allows the system to detect and correct insertion and deletion errors during decoding without requiring multiple sequencing operations, thus maintaining reliability while reducing the time loss associated with repeated sequencing.
Solution Approach 2:
The patent introduces syndromes as an intermediary mechanism that captures error information during decoding. These syndromes enable the system to identify and correct insertion and deletion errors efficiently, reducing the need for multiple sequencing operations and thereby minimizing data delivery delays while maintaining reliability.
2Measurement precision
If multiple sequencing operations are performed to detect insertion and deletion errors, then measurement precision is improved, but productivity worsens due to reduced output per unit time
Solution Approach 1:
By pre-encoding data with convolutional codes and generating syndromes before storage, the system establishes a framework for accurate error detection that works efficiently during decoding. This approach maintains high measurement precision for detecting insertion and deletion errors while avoiding the productivity loss associated with multiple sequencing operations.
Solution Approach 2:
The patent replaces the mechanical approach of multiple physical sequencing operations with a computational decoding process using convolutional codes and syndromes. This substitution maintains accurate error detection (measurement precision) while significantly improving productivity by eliminating the time-consuming repeated sequencing steps.
3Device complexity
If traditional error correction codes are used, then device complexity is reduced, but reliability worsens due to inability to effectively address insertion and deletion errors
Solution Approach 1:
The patent changes the parameters of the coding system by using convolutional codes with specific constraints (e.g., constraint length K, code rate R) and associated syndrome calculations tailored for insertion and deletion errors. This parameter optimization maintains relative simplicity while dramatically improving reliability for the specific error types encountered in DNA storage.
Solution Approach 2:
The patent creates a composite coding approach by combining convolutional codes with syndrome-based detection and correction mechanisms specifically designed for insertion and deletion errors. This composite system achieves high reliability for DNA storage applications while maintaining manageable complexity through the structured integration of multiple error correction techniques.
Data Source
AI summary
This disclosure describes systems and methods for detecting multiple insertion and deletion errors in the presence of substitution errors in a signal (such as a sequenced DNA string). A convolutional code that includes two or more component convolutional codes is used for encoding. Each of the two or more component convolutional codes generates only a subset of all possible outputs of the convolutional code. The subsets of the two or more component convolutional codes are disjoint from each other. Only one of the two or more convolutional codes is active at any given time. The two or more convolutional codes together define a super code. The two or more convolutional codes are time interlaced within the super code, and the super code defines the convolutional code. A trellis that includes two or more component trellises designed based on the two or more component convolutional codes is used for decoding.