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Hidden biases in germline structural variant detection. | LitMetric

AI Article Synopsis

  • Genomic structural variations (SV) play a crucial role in genetic differences, but detecting them from next-generation sequencing data is tricky.
  • A study on DNA from a Chinese family identified that the choice of mapping methods largely influences the variability in SV detection, with some true positives being missed due to high false negative rates.
  • The findings highlight the need for improved techniques and recommendations to reduce variability in SV calling, especially in larger studies.

Article Abstract

Background: Genomic structural variations (SV) are important determinants of genotypic and phenotypic changes in many organisms. However, the detection of SV from next-generation sequencing data remains challenging.

Results: In this study, DNA from a Chinese family quartet is sequenced at three different sequencing centers in triplicate. A total of 288 derivative data sets are generated utilizing different analysis pipelines and compared to identify sources of analytical variability. Mapping methods provide the major contribution to variability, followed by sequencing centers and replicates. Interestingly, SV supported by only one center or replicate often represent true positives with 47.02% and 45.44% overlapping the long-read SV call set, respectively. This is consistent with an overall higher false negative rate for SV calling in centers and replicates compared to mappers (15.72%). Finally, we observe that the SV calling variability also persists in a genotyping approach, indicating the impact of the underlying sequencing and preparation approaches.

Conclusions: This study provides the first detailed insights into the sources of variability in SV identification from next-generation sequencing and highlights remaining challenges in SV calling for large cohorts. We further give recommendations on how to reduce SV calling variability and the choice of alignment methodology.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8686633PMC
http://dx.doi.org/10.1186/s13059-021-02558-xDOI Listing

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