Jun Sese

This study introduces a complex-valued representation for time-series gene expression data, showing it captures distinct gene-gene similarities and temporal programs comparable to conventional methods.

This paper introduces HOBIT, a statistical method to detect shifts in homeolog expression ratios in allopolyploids, outperforming existing methods in simulation and real data applications.

ARPEGGIO provides an automated workflow for analyzing epigenetic data in polyploids, improving accuracy and reproducibility in DNA methylation analysis of allopolyploids.

The paper develops MENTR, a machine learning framework to predict cell-type-specific ncRNA transcription from genome sequence, identifying causal variants and ncRNAs associated with complex traits.

reactIDR evaluates the statistical reproducibility of high-throughput RNA structural analyses to classify RNA reactivity into stem, loop, and unmapped categories with high accuracy.

This study compares RNA-seq expression quantification methods for allopolyploids, finding that subgenome-classification approaches significantly reduce error rates compared to standard methods.

The paper integrates public ChIP-seq and DNase-seq data to identify transcription factor interactions and their association with disease risk loci in noncoding regions.