We are a computational biology lab in the Biology Department at Johns Hopkins University with research interests in bioinformatics, computational genomics, and data intensive science.

Genomics of gene regulation: we seek to achieve a global understanding of the genomic basis of gene regulation, particularly over time and in development, using functional genomics and machine learning. We have a long standing interest in identification of cis-regulatory modules, particularly long-range enhancers. More recently, we have been focusing on understanding the determinants of 3D genome organization and its role in gene regulation.

Data intensive science: We work to increase access to compute and data intensive methods for the scientific research community, particularly in genomics. We are part of the team that develops Galaxy, a framework for making large scale computational analysis more accessible and reproducible. In the context of Galaxy we have research interests in data visualization and analytics, cloud and high-performance computing, transparent and reproducible scientific publication. We are particularly concerned with improving the reproducibility of published scientific results that depend on complex methods.


Two articles published at MIPRO 2015 – capturing work on multi-cloud support for Galaxy on the Cloud and on expanding the adoption of Big Data in bioinformatics.

Genome-wide comparative analysis reveals human-mouse regulatory landscape and evolution – Our comprehensive analysis of exaptation of regulatory elements is now published in BMC Genomics.

Being a part of the open source community – means participating and contributing to Sprints, Hackathons and Codefests - a process now captured in a paper.

Mouse ENCODE Consortium Papers out – comprehensive mapping and comparison of functional genomic data in human and mouse.

Two articles at Supercomputing 2014 – were published (1) describing a Galaxy federation model and (2) providing an overview of the current Big Data analysis tools.



Sauria MEG, Phillips-Cremins JE, Corces VG, Taylor J. HiFive: a tool suite for easy and efficient HiC and 5C data analysis. Genome Biology. October 2015; 16:237

Stewart CA, Cockerill TM, Foster I, Hancock D, Merchant N, Skidmore E, Stanzione D, Taylor J, Tuecke S, Turner G, Vaughn M, Gaffney, NI. Jetstream: A Self-provisioned, Scalable Science and Engineering Cloud Environment. Proceedings of the 2015 XSEDE Conference: Scientific Advancements Enabled by Enhanced Cyberinfrastructure. July 2015; :29:1--29:8

Martens AT, Taylor J, Hilser VJ. Ribosome A and P sites revealed by length analysis of ribosome profiling data. Nucleic Acids Research. April 2015; 43(7):3680-3687

Blankenberg D, Taylor J, Nekrutenko A. Online Resources for Genomic Analysis Using High-Throughput Sequencing. Cold Spring Harbor Protocols. April 2015; 2015(4)

Goecks J, El-Rayes BF, Maithel SK, Khoury HJ, Taylor J, Rossi MR. Open pipelines for integrated tumor genome profiles reveal differences between pancreatic cancer tumors and cell lines. Cancer Medicine. March 2015; 4(3):392-403