Motif-Specific Error Model for Accurate Mutation Calling
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Solution Overview
Problem
Current mutation detection methods in genetic material are costly and time-consuming due to the need for numerous target-specific test assays to account for errors and noise in sequencing data, particularly in processes like PCR, which can introduce new mutations and complicate the estimation of initial mutation fractions.
Innovation Solution
The implementation of a motif-specific error model that uses training data to characterize background errors, allowing for the estimation of mutation fractions without performing extensive target-specific test assays by grouping target bases by their motifs and determining motif-specific error parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of target-specific test assays are performed to account for errors and noise in sequencing data, then measurement precision is improved, but productivity deteriorates and loss of time increases
Solution Approach 1:
The patent segments the error characterization process from the mutation detection process. Instead of performing target-specific test assays for each mutation detection, the method pre-characterizes errors using training data and organizes them into motif-specific error models. This segmentation allows the main mutation detection workflow to proceed without repeated error characterization assays, thereby improving productivity while maintaining measurement precision through the use of pre-established error models.
Solution Approach 2:
The patent applies preliminary action by performing error characterization in advance using training data before actual mutation detection. The motif-specific error models are built beforehand based on training sequences, allowing the system to account for errors and noise without performing additional target-specific test assays during the actual mutation detection process. This preliminary error modeling significantly improves productivity while preserving measurement accuracy.
2Measurement precision
If a large number of target-specific test assays are performed to account for errors and noise in sequencing data, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs error characterization in advance using training data to build motif-specific error models before actual mutation detection. This preliminary action eliminates the need for time-consuming target-specific test assays during the mutation detection workflow, significantly reducing the loss of time while maintaining measurement precision through the use of pre-established error models.
Solution Approach 2:
The patent segments the time-consuming error characterization step from the mutation detection step. By organizing errors into motif-specific categories based on training data, the system creates reusable error models that can be applied across multiple mutation detection tasks without repeating the error characterization process, thereby reducing the overall time required while preserving accuracy.
3Reliability
If target-specific statistics are generated through numerous test assays, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent creates universal motif-specific error models that can be applied across multiple targets and assays. Instead of generating target-specific statistics through numerous individual test assays, the system builds error models based on sequence motifs that can be universally applied to any target with the same motif. This universality maintains reliability by accurately characterizing errors while significantly reducing the complexity of the testing process.
Solution Approach 2:
The patent segments the error characterization approach from target-specific to motif-specific. By organizing errors according to sequence motifs rather than individual targets, the system creates a simplified framework where a limited set of motif-specific error models can replace the need for numerous target-specific test assays, thereby reducing device complexity while maintaining reliability through accurate error accounting.
Data Source
AI summary
A method for calling a mutation includes determining, for each target base of a plurality of target bases, a respective value for a background error parameter based on training data. The method further includes determining a motif-specific error model including the background error parameter by performing processes that include: identifying a respective motif for each target base of the plurality of target bases, grouping the plurality of target bases into a plurality of groups, each group corresponding to a particular motif, and determining, for each group, a respective motif-specific parameter value for the background error parameter based on the determined values for the background error parameter for the target bases included in each group. The method further includes calling a mutation using the motif-specific error model and sequencing information for a biological sample.