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Background Large (>1 Mb), polymorphic inversions have substantial impacts on population structure and maintenance of genotypes. These large inversions can be detected from single nucleotide polymorphism (SNP) data using unsupervised learning techniques like PCA. Construction and analysis of a feature matrix from millions of SNPs requires large amount of memory and limits the sizes of data sets that can be analyzed. Methods We propose using feature hashing construct a feature matrix from a VCF file of SNPs for reducing memory usage. The matrix is constructed in a streaming fashion such that the entire VCF file is never loaded into memory at one time. Results When evaluated on Anopheles mosquito and Drosophila fly data sets, our approach reduced memory usage by 97% with minimal reductions in accuracy for inversion detection and localization tasks. Conclusion With these changes, inversions in larger data sets can be analyzed easily and efficiently on common laptop and desktop computers. Our method is publicly available through our open-source inversion analysis software, Asaph.
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Segmenting and Genotyping Large, Polymorphic Inversions
Large, polymorphic inversions can contribute to population structure and enable mutually-exclusive adaptations to survive in the same population. Current methods for detecting inversions from single-nucleotide polymorphisms (SNPs) called from population genomics data require an experienced, human user to prepare the data and interpret the results. Ideally, these methods would be completely automated yet robust to allow usage by inexperienced users. Towards this goal, automated approaches for segmentation of inversions and inference of sample genotypes are introduced and evaluated on chromosomes from flies, mosquitoes, and prairie sunflowers.
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- Award ID(s):
- 1947257
- PAR ID:
- 10463483
- Date Published:
- Journal Name:
- 2023 IEEE International Conference on Electro Information Technology (eIT)
- Page Range / eLocation ID:
- 153 to 162
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/eIT57321.2023.10187331
