Genomic data visualization

Author(s):  
Imen Messaoudi ◽  
Afef Elloumi Oueslati ◽  
Zied Lachiri

2004 ◽  
Vol 20 (11) ◽  
pp. 1804-1805 ◽  
Author(s):  
W. Wu ◽  
W. S. Noble


1998 ◽  
Vol 8 (3) ◽  
pp. 291-305 ◽  
Author(s):  
Gregg A. Helt ◽  
Suzanna Lewis ◽  
Ann E. Loraine ◽  
Gerald M. Rubin


2010 ◽  
Vol 26 (14) ◽  
pp. 1781-1782 ◽  
Author(s):  
H. Jiang ◽  
F. Wang ◽  
N. P. Dyer ◽  
W. H. Wong




Author(s):  
Jung Soh ◽  
Paul M. K. Gordon ◽  
Christoph W. Sensen


2003 ◽  
Vol 19 (10) ◽  
pp. 1292-1293 ◽  
Author(s):  
J. E. Johnson ◽  
M. V. Stromvik ◽  
K. A.T. Silverstein ◽  
J. A. Crow ◽  
E. Shoop ◽  
...  


2019 ◽  
Vol 38 (3) ◽  
pp. 781-805 ◽  
Author(s):  
S. Nusrat ◽  
T. Harbig ◽  
N. Gehlenborg


F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1096 ◽  
Author(s):  
Jayaram Kancherla ◽  
Alexander Zhang ◽  
Brian Gottfried ◽  
Hector Corrada Bravo

Interactive and integrative data visualization tools and libraries are integral to exploration and analysis of genomic data. Web based genome browsers allow integrative data exploration of a large number of data sets for a specific region in the genome. Currently available web-based genome browsers are developed for specific use cases and datasets, therefore integration and extensibility of the visualizations and the underlying libraries from these tools is a challenging task. Genomic data visualization and software libraries that enable bioinformatic researchers and developers to implement customized genomic data viewers and data analyses for their application are much needed. Using recent advances in core web platform APIs and technologies including Web Components, we developed the Epiviz Component Library, a reusable and extensible data visualization library and application framework for genomic data. Epiviz Components can be integrated with most JavaScript libraries and frameworks designed for HTML. To demonstrate the ease of integration with other frameworks, we developed an R/Bioconductor epivizrChart package, that provides interactive, shareable and reproducible visualizations of genomic data objects in R, Shiny and also create standalone HTML documents. The component library is modular by design, reusable and natively extensible and therefore simplifies the process of managing and developing bioinformatic applications.



2009 ◽  
Vol 10 (1) ◽  
Author(s):  
Steffen Durinck ◽  
James Bullard ◽  
Paul T Spellman ◽  
Sandrine Dudoit


2019 ◽  
Author(s):  
Weize Xu ◽  
Da Lin ◽  
Ping Hong ◽  
Liang Yi ◽  
Rohit Tyagi ◽  
...  

AbstractSummaryCoolBox is a Python package for interactive genomic data exploration based on Jupyter notebook. It provides a ggplot2-like Application Programming Interface (API) for genomic data visualization, and a Jupyter/ipywidgets based Graphical User Interface (GUI) for interactive data exploration. CoolBox is a versatile multi-omics explorer supporting most types of data formats generated by various sequencing technologies like RNA-Seq, ChIP-Seq, ChIA-PET and Hi-C.Availability and implementationCoolBox is purely implemented with Python, and the GUI widget in Jupyter notebook is based on the ipywidgets package. It is open-source and available under GPLv3 license at https://github.com/GangCaoLab/CoolBox.



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