Cell-Free DNA Fusion Read Alignment Error Suppression
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Solution Overview
Problem
Duplicated genomic regions caused by genomic rearrangement events pose challenges in accurate variant calling in clinical sequencing applications, leading to false-positive variants due to processed pseudogenes (PPGs) in the reference genome.
Innovation Solution
A method for detecting alignment errors in genetic sequence reads by sequencing cell-free DNA molecules, aligning them to a reference sequence, identifying gene fusion reads with intragenic fusion breakpoints, and filtering out alignment errors to suppress false-positive variant detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sequence reads are aligned to a reference genome containing processed pseudogenes, then variant detection can be performed, but false-positive variants are generated due to duplicated genomic regions
Solution Approach 1:
The method segments the alignment analysis by identifying fusion reads that span intragenic boundaries and separating them from regular reads. By detecting fusion breakpoints and analyzing reads that cross these boundaries, the method isolates and removes alignment errors caused by pseudogenes, thereby improving variant detection accuracy while eliminating false positives
Solution Approach 2:
The method performs preliminary identification of fusion reads and fusion breakpoints before final variant calling. By pre-processing the sequence reads to identify and flag those containing fusion breakpoints, the method prevents these reads from contributing to false-positive variant calls in subsequent analysis steps
2Quantity of substance
If all sequence reads including fusion reads are used for variant detection, then comprehensive coverage is achieved, but alignment errors increase due to misaligned fusion reads
Solution Approach 1:
The method applies different quality standards to different types of reads. Fusion reads that span intragenic boundaries are identified and treated differently from regular reads. By applying local quality control specifically to fusion reads through breakpoint detection and filtering, the method maintains high alignment accuracy for the majority of reads while preserving comprehensive coverage from fusion reads
Data Source
AI summary
Methods for producing a filtered sequencing data set by identifying one or more split sequence reads in a set of test sequence reads obtained from cell-free nucleic acid (cfNA) in a biological sample, wherein each split sequence read comprises at least one breakpoint; and, suppressing, in the set of test sequence reads, (i) at least a portion of one or more of the split sequence reads and/or at least a portion of one or more of the test sequence reads that comprise at least one sequence variant within a selected number of nucleotides from a given breakpoint, (ii) one or more base calls of the split sequence reads and/or one or more base calls of the test sequence reads that comprise at least one sequence variant within a selected number of nucleotides from a given breakpoint, thereby producing the filtered sequence information data set.