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.



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

Denas O, Sandstrom R, Cheng Y, Beal K, Herrero J, Hardison RC, Taylor J. Genome-wide comparative analysis reveals human-mouse regulatory landscape and evolution. BMC Genomics. February 2015; 16:87

Pope BD, Ryba T, Dileep V, Yue F, Wu Q, Denas O, Vera DL, Wang Y, Hansen RS, Canfield TK, Thurman RE, Cheng Y, Gülsoy G, Dennis JH, Snyder MP, Stamatoyannopoulos JA, Taylor J, Hardison RC, Kahveci T, Ren B, Gilbert DM. Topologically associating domains are stable units of replication-timing regulation. Nature. November 2014; 515:402–405