Projects

Publications

LogoAlexander Sullivan

Alexander Sullivan head drawn and stylized

Alexander Joo-Hyun Sullivan

Software Developer & Bioinformatician

Featured Projects

Masterpiece X Thumbnail

Masterpiece X

Full Stack Developer

Impact Depth Thumbnail

Impact Depth

Creator

ePlant's Plant eFP Thumbnail

ePlant's Plant eFP

Software Engineer

GAIA Thumbnail

GAIA

Bioinformatician

SciGrade Thumbnail

SciGrade

Full Stack Developer

Gold Biosensing Thumbnail

Gold Biosensing

Laboratory Research Lead

Small Dev Talk Thumbnail

Small Dev Talk

Co-creator & Journalist

Featured Publications

ePlant in 2021: New Species, Viewers, Data Sets, and Widgets

Ben Waese-Perlman, Asher Pasha, Chantal Ho, Amirahmad Azhieh, Yushan Liu, Alexander Sullivan, Vincent Lau, Eddi Esteban, Jamie Waese, George Ly, Cornelia Hooper, S. Evan Staton, Nicholas Brereton, Cuong Le, Rex Nelson, Shelley Lumba, David Goodstein, A. Harvey Millar, Isobel Parkin, Lewis Lukens, Juergen Ehlting, Loren Rieseberg, Frédéric Pitre, Anne Brown, Nicholas J. Provart

10.1101/2021.04.28.441805 | Preprint (bioRxiv) | 2021-04-29

ePlant was introduced in 2017 for exploring large Arabidopsis thaliana data sets from the kilometre to nanometre scales. In the past four years we have used the ePlant framework to develop ePlants for 15 agronomically-important species: maize, poplar, tomato, Camelina sativa, soybean, potato, barley, Medicago truncatula, eucalyptus, rice, willow, sunflower, Cannabis sativa, wheat and sugarcane. We also updated the interface to improve performance and accessibility, and added two new views to the Arabidopsis ePlant - the Navigator and Pathways v...

An 'eFP-Seq Browser' for visualizing and exploring RNA sequencing data

Alexander Sullivan, Priyank Purohit, Nowlan H. Freese, Asher Pasha, Eddi Esteban, Jamie Waese, Alison Wu, Michelle Chen, Chih Ying Chin, Richard Song, Snehal R. Watharkar, Agnes P. Chan, Vivek Krishnakumar, Matthew W. Vaughn, Chris Town, Ann E. Loraine, Nicholas J. Provart

10.1111/tpj.14468 | The Plant Journal | 2019-07-26

Improvements in next-generation sequencing technologies have resulted in dramatically reduced sequencing costs. This has led to an explosion of '-seq'-based methods, of which RNA sequencing (RNA-seq) for generating transcriptomic data is the most popular. By analysing global patterns of gene expression in organs/tissues/cells of interest or in response to chemical or environmental perturbations, researchers can better understand an organism's biology. Tools designed to work with large RNA-seq data sets enable analyses and visualizations to help...