Nanxiang Zhao

The paper uses MPRA to identify functional regulatory variants associated with schizophrenia and shows they differ from eQTLs in genomic location and regulatory impact.

The paper develops TLand, a machine-learning model that prioritizes and annotates non-coding regulatory variants by organ, using RegulomeDB features and outperforming existing models.

Explain-seq is a pipeline that trains and interprets sequence-based deep learning models to predict enhancer activities and identify transcription factor motifs in genomic data.

The paper updates RegulomeDB to improve non-coding variant annotation by integrating new functional genomic data and probabilistic scores from the SURF algorithm.

F-Seq2 improves peak calling by incorporating dynamic statistics and kernel density estimation, outperforming existing tools on ATAC-seq and ChIP-seq data.