Laura L. Colbran

Global patterns of natural selection inferred using ancient DNA

This study uses ancient DNA to identify global patterns of natural selection across 7244 individuals from five regions, finding shared and region-specific adaptive signals.

Resurrecting the Alternative Splicing Landscape of Archaic Hominins using Machine Learning

The paper uses machine learning to analyze Neanderthal and Denisovan genomes, identifying splicing variants and their potential impact on phenotypic differences.

A gene-level test for directional selection on gene expression

The paper develops a gene-level test for directional selection on gene expression using regulatory variants and identifies 45 genes with significant evidence of selection.

Tracing the evolution of human gene regulation and its association with shifts in environment

The paper uses ancient DNA and machine learning to study gene regulation changes in human populations and their link to environmental adaptation.

Short DNA sequence patterns accurately identify broadlyactive human enhancers

The paper identifies short DNA sequence patterns that predict broadly active human enhancers using machine learning, showing that GC-rich motifs are associated with broad activity while DRMs are not conserved across species.