Leveraging long-read assemblies and machine learning to enhance short-read transposable element detection and genotyping.

ABSTRACT: Transposable elements (TEs) are parasitic genomic elements that are ubiquitous across the tree of life and play a crucial role in genome evolution. Advances in long-read sequencing have allowed highly accurate TE detection, though at a higher cost than short-read sequencing. Recent studies using long reads have shown that existing short-read TE detection methods perform inadequately when applied to real data. In this study, we use a machine learning approach (called TEforest) to discover and genotype TE insertions and deletions with short-read data by using TEs detected from long-read genome assemblies as training data. Our method first uses a highly sensitive algorithm to discover potential TE insertion or deletion sites in the genome, extracting relevant features from short-read alignmen

SUBMITTER: Daigle A 

PROVIDER: S-EPMC11844559 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature