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.
Goonasekera N, Lonie A, Taylor J, Afgan E. CloudBridge – a Simple Cross-Cloud Python Library. XSEDE16. July 2016; :1-6Forer 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):103-114Afgan 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-W10Skala 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):16-24Afgan E, Sloggett C, Goonasekera N, Makunin I, Benson D, Crowe M, Gladman S, Kowsar Y, Pheasant M, Horst R, Lonie A. Genomics Virtual Laboratory: A Practical Bioinformatics Workbench for the Cloud. PLoS ONE. October 2015; 10(10):1-20