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.

Recent Publications

Turaga N, Freeberg MA, Baker D, Chilton J, Galaxy Team, Nekrutenko A, Taylor J. A guide and best practices for R/Bioconductor tool integration in Galaxy. F1000Research. November 2016; 5:2757

Goonasekera N, Lonie A, Taylor J, Afgan E. CloudBridge – a Simple Cross-Cloud Python Library. XSEDE16. July 2016; :1-6

Forer L, Afgan E, Weissensteiner H, Davidovic D, Specht G, Kronenberg F, Schoenherr S. Cloudflow - enabling faster biomedical pipelines with MapReduce and Spark. Scalable Computing: Practice and Experience (SCPE). June 2016; 17(2):103-114

Afgan E, Baker D, Beek M, Blankenberg D, Bouvier D, Čech M, Chilton J, Clements D, Coraor N, Eberhard C, Grüning B, Guerler A, Hillman-Jackson J, Kuster G, Rasche E, Soranzo N, Turaga N, Taylor J, Nekrutenko A, Goecks J. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Research. May 2016; 44(W1):W3-W10

Skala K, Davidović D, Afgan E, Sović I, Šojat Z. Scalable Distributed Computing Hierarchy: Cloud, Fog and Dew Computing. Open Journal of Cloud Computing (OJCC). December 2015; 2(1):16-24

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