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

Malhotra S, Freeberg MA, Winans SJ, Taylor J, Beemon KL. A Novel Long Non-Coding RNA in the hTERT Promoter Region Regulates hTERT Expression. Non-Coding RNA. December 2017; 2018(4):1

Anderson C, Reiss I, Zhou C, Cho A, Siddiqi H, Mormann B, Avelis CM, DeFord P, Bergland A, Roberts E, Taylor J, Vasiliauskas D, Johnston Jr RJ. Natural variation in stochastic photoreceptor specification and color preference in Drosophila. eLife. December 2017; 2017(6):e29593

Grüning BA, Rasche E, Rebolledo-Jaramillo B, Eberhard C†, Houwaart T, Chilton J, Coraor N, Backofen R, Taylor J, Nekrutenko A. Jupyter and Galaxy: Easing entry barriers into complex data analyses for biomedical researchers. PLOS Computational Biology. May 2017; 13(5):e1005425

Yoon HJ, Sauria M, Lyu X, Cheema M, Ausio J, Taylor J, Corces V. Chromatin States in Mouse Sperm Correlate with Embryonic and Adult Regulatory Landscapes. Cell Reports. February 2017; 18(6):1366-1382

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