STR Base Call Correction Using Flow Signal Clustering
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Solution Overview
Problem
Existing nucleic acid sequencing technologies face challenges in accurately determining the length of short tandem repeat (STR) regions due to increased homopolymer errors, which lead to false alleles and difficulty in comparing DNA profiles and identifying mixtures of DNA from different individuals, especially when variations like SNPs and indels are present in the flanks.
Innovation Solution
A method and system for nucleic acid sequence analysis that aligns flank sequences with reference sequences, receives flow space signal measurements, determines optimum clusters associated with homopolymer lengths, and modifies base calls to correct insertion or deletion errors in repeat regions, using a processor and machine-readable storage medium to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sequencing is performed on STR regions, then DNA profiling capability is achieved, but homopolymer errors increase leading to false alleles
Solution Approach 1:
The patent replaces traditional base calling methods with a hidden Markov model (HMM) that uses flow space signal measurements to determine homopolymer lengths. Instead of relying on conventional sequencing base calls that are prone to errors in repeat regions, the system uses signal intensity measurements and probabilistic modeling to accurately determine the number of repeated bases, thereby resolving the contradiction between achieving STR profiling and avoiding homopolymer errors.
2Reliability
If longer STR sequences are sequenced, then more discrimination power is achieved, but homopolymer errors increase
Solution Approach 1:
The patent applies hidden Markov modeling to replace traditional sequencing analysis methods, enabling accurate determination of long STR sequences by using flow space signal measurements and probabilistic state transitions rather than conventional base calling, thus maintaining high discrimination power while reducing homopolymer errors even in longer repeat regions.
Solution Approach 2:
The system changes the parameter used for determining homopolymer length from traditional base call counts to flow space signal intensity measurements. By measuring signal intensities and using HMM to interpret these continuous parameters rather than discrete base calls, the system can accurately determine homopolymer lengths in longer STR sequences without the errors that plagues conventional methods.
3Device complexity
If traditional base calling is used in repeat regions, then processing is simpler, but insertion and deletion errors occur
Solution Approach 1:
The patent replaces simple base calling algorithms with a hidden Markov model that processes flow space signal measurements. Although HMM is more complex than traditional base calling, it provides accurate determination of homopolymer lengths by modeling the probabilistic nature of sequencing signals in repeat regions, thereby improving reliability despite increased computational complexity.
Data Source
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AI summary
Methods, systems and non-transitory machine-readable storage medium are provided to mitigate insertion errors and deletion errors in STR sequences and improve accuracy in determination of the number of repeats. A method includes determining one or more optimum clusters for a set of flow space signal measurements, wherein at least one of the optimum clusters is associated with a homopolymer length, modifying a base call at the position in the repeat region sequence to the homopolymer length associated with the optimum cluster to produce a corrected repeat region sequence, thereby correcting an insertion error or a deletion error. The method may further include detecting variations in the flanks associating those variations with the length of the STR.