CRAMER: a lightweight, highly customizable web-based genome browser supporting multiple visualization instances
Author(s) -
Maria Anastasiadi,
Eugene Bragin,
P Biojoux,
A Ahamed,
Josephine Burgin,
Kevin de Castro Cogle,
S Llaneza-Lago,
R Muvunyi,
Magdalena E. Ściślak,
I Aktan,
Corentin Molitor,
Tomasz J. Kurowski,
Fady Mohareb
Publication year - 2020
Publication title -
bioinformatics
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btaa146
Subject(s) - computer science , upload , visualization , genome browser , javascript , world wide web , web server , personalization , web application , the internet , database , genome , genomics , data mining , biochemistry , chemistry , gene
In recent years, the ability to generate genomic data has increased dramatically along with the demand for easily personalized and customizable genome browsers for effective visualization of diverse types of data. Despite the large number of web-based genome browsers available nowadays, none of the existing tools provides means for creating multiple visualization instances without manual set up on the deployment server side. The Cranfield Genome Browser (CRAMER) is an open-source, lightweight and highly customizable web application for interactive visualization of genomic data. Once deployed, CRAMER supports seamless creation of multiple visualization instances in parallel while allowing users to control and customize multiple tracks. The application is deployed on a Node.js server and is supported by a MongoDB database which stored all customizations made by the users allowing quick navigation between instances. Currently, the browser supports visualizing a large number of file formats for genome annotation, variant calling, reads coverage and gene expression. Additionally, the browser supports direct Javascript coding for personalized tracks, providing a whole new level of customization both functionally and visually. Tracks can be added via direct file upload or processed in real-time via links to files stored remotely on an FTP repository. Furthermore, additional tracks can be added by users via simple drag and drop to an existing visualization instance.
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