Luca Schlegel

A multi-component power-law penalty corrects distance bias in single-cell co-accessibility and deep-learning chromatin interaction predictions

The paper corrects distance bias in single-cell co-accessibility and deep-learning chromatin interaction predictions using multi-component power-law penalties informed by Gaussian mixture modeling and polymer-physics scaling.

The genetic architecture of cell-type-specific cis-regulation in maize

The paper identifies cell-type-specific cis-regulatory variants in maize using single-cell genomics and population genetics, revealing their role in local adaptation and flowering.

GenomicLinks: Deep learning predictions of 3Dchromatin loops in the maize genome

GenomicLinks predicts 3D chromatin loops in maize by identifying sequence features associated with transcription factor binding motifs, validated using single-cell co-accessibility data.