Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia

Active retrotransposons in the human genome (L1, Alu and SVA elements) can create genomic mobile element insertions (MEIs) in both germline and somatic tissue. Specific somatic MEIs have been detected at high levels in human cancers, and at lower to medium levels in human brains. Dysregulation of somatic retrotransposition in the human brain has been hypothesized to contribute to neuropsychiatric diseases. However, individual somatic MEIs are present in small proportions of cells at a given anatomical location, and thus standard whole-genome sequencing (WGS) presents a difficult signal-to-noise problem, while single-cell approaches suffer from limited scalability and experimental artifacts introduced by enzymatic whole-genome amplification6. Previous studies produced widely differing estimates for the somatic retrotransposition rates in human brain. Here, we present a highly precise machine learning method (RetroSom) to directly identify somatic L1 and Alu insertions in