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Abstract Mobile element insertions (MEIs) are repetitive genomic sequences that contribute to genetic variation and can lead to genetic disorders. Targeted and whole-genome approaches using short-read sequencing have been developed to identify reference and non-reference MEIs; however, the read length hampers detection of these elements in complex genomic regions. Here, we pair Cas9-targeted nanopore sequencing with computational methodologies to capture active MEIs in human genomes. We demonstrate parallel enrichment for distinct classes of MEIs, averaging 44% of reads on-targeted signals and exhibiting a 13.4-54x enrichment over whole-genome approaches. We show an individual flow cell can recover most MEIs (97% L1Hs, 93% Alu Yb, 51% Alu Ya, 99% SVA_F, and 65% SVA_E). We identify seventeen non-reference MEIs in GM12878 overlooked by modern, long-read analysis pipelines, primarily in repetitive genomic regions. This work introduces the utility of nanopore sequencing for MEI enrichment and lays the foundation for rapid discovery of elusive, repetitive genetic elements.
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This content will become publicly available on June 1, 2026
Genomic Anomaly Detection with Functional Data Analysis
Background: Genetic variation provides a foundation for understanding evolution. With the rise of artificial intelligence, machine learning has emerged as a powerful tool for identifying genomic footprints of evolutionary processes through simulation-based predictive modeling. However, existing approaches require prior knowledge of the factors shaping genetic variation, whereas uncovering anomalous genomic regions regardless of their causes remains an equally important and complementary endeavor. Methods: To address this problem, we introduce ANDES (ANomaly DEtection using Summary statistics), a suite of algorithms that apply statistical techniques to extract features for unsupervised anomaly detection. A key innovation of ANDES is its ability to account for autocovariation due to linkage disequilibrium by fitting curves to contiguous windows and computing their first and second derivatives, thereby capturing the “velocity” and “acceleration” of genetic variation. These features are then used to train models that flag biologically significant or artifactual regions. Results: Application to human genomic data demonstrates that ANDES successfully detects anomalous regions that colocalize with genes under positive or balancing selection. Moreover, these analyses reveal a non-uniform distribution of anomalies, which are enriched in specific autosomes, intergenic regions, introns, and regions with low GC content, repetitive sequences, and poor mappability. Conclusions: ANDES thus offers a novel, model-agnostic framework for uncovering anomalous genomic regions in both model and non-model organisms.
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- Award ID(s):
- 2302258
- PAR ID:
- 10610227
- Publisher / Repository:
- Genes
- Date Published:
- Journal Name:
- Genes
- Volume:
- 16
- Issue:
- 6
- ISSN:
- 2073-4425
- Page Range / eLocation ID:
- 710
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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