Eric Y. Durand

Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts and clinical tumors using deep learning

MOBER integrates transcriptomic data from cancer cell lines, xenografts, and clinical tumors using deep learning to remove batch effects and improve clinical translatability.

TRAWLING: a Transcriptome Reference AWare of spLIciNG events.

TRAWLING simplifies aberrant splicing detection by providing a splicing-aware transcriptome reference, improving RNAseq analysis across multiple datasets.

A scalable pipeline for local ancestry inference using tens of thousands of reference haplotypes

This paper introduces Ancestry Composition, a pipeline for ancestry deconvolution that uses over 14,000 reference haplotypes to improve accuracy and scalability.

Ancestry Composition: A Novel, Efficient Pipeline for Ancestry Deconvolution

Ancestry Composition deconvolves chromosomal segments in admixed individuals by resolving ancestry contributions from sub-continental populations with high precision and recall.

The genetic ancestry of African, Latino, and European Americans across the United States.

This study analyzes the genetic ancestry of African, Latino, and European Americans in the US, revealing mixed ancestry and historical influences on regional genetic differences.