Jordan A. Ramilowski

The paper performs knockdown of 200 lncRNAs in human iPS cells, identifies 36 with growth inhibition, and finds molecular phenotypes in 29.3% of cases, with higher agreement in molecular than cell growth phenotypes across cell types.

The paper identifies widespread transcription initiation at microsatellites across species and develops deep learning models to predict STR-associated transcription and its impact on disease variants.

The paper identifies cis-interacting lncRNAs and their targets using RNA-chromatin and Hi-C data across 18 human cell types.

The paper introduces LQ-ssCAGE, a cost-effective CAGE method for low quantity RNA samples, and demonstrates its use in identifying novel enhancer and promoter-derived lncRNAs.

The paper functionally annotates human long non-coding RNAs by systematically knocking down 285 lncRNAs and analyzing molecular phenotypes using CAGE sequencing.

The paper investigates transcription initiation at polyT short tandem repeats (STRs) using CAGE data and develops a deep learning model to predict STR-associated transcription with high accuracy.

The paper analyzes transcriptomic and chromatin accessibility changes in zebrafish cardiomyocytes during heart development to identify key regulatory networks and their disruption in mutant lines.