Rachel E. Rodin

The paper identifies metabolite signatures linked to MS severity using plasma metabolomics, finding associations between lipid and amino acid metabolism and disease activity.

DeepMosaic detects mosaic single nucleotide variants without control samples using deep convolutional neural networks, achieving high accuracy and uncovering many variants missed by existing methods.

The paper evaluates brain somatic mosaicism in the adult human to identify rules governing cellular distribution during embryogenesis.

This study identifies somatic SNVs in non-cancerous human brain tissues and establishes best practices for detecting low-frequency variants.

The paper investigates the role of mosaic copy number variants (mCNVs) in autism spectrum disorder (ASD) by analyzing genetic data from ASD probands and unaffected siblings, finding a significant association between large mCNVs and ASD risk.

The paper introduces a machine learning method to detect somatic L1 insertions in human brain tissue, revealing their widespread distribution in neurons and glia and potential role in neuropsychiatric diseases.

PRDD-seq simultaneously reconstructs cell type, lineage, and birthdate in human brain tissue by analyzing RNA and DNA mutations in single cells.

The paper analyzes somatic mutations in autistic and control brains, finding higher mutation rates in ASD brains, particularly in enhancer regions.

The paper introduces LiRA, a method to distinguish true mutations from artifacts in single-cell DNA sequencing data by phasing somatic variants with germline variants.

The paper identifies age-related accumulation of somatic mutations in human neurons and links them to neurodegenerative diseases.