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.
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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.pinupbet.az onlayn kazino bölməsi oyunçulara bəzi aparıcı proqram təminatçıları tərəfindən hazırlanmış çoxlu sayda oyunlar təqdim edir.
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.
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