New web-based tool to better analyse human genomic data
To enable researchers to quickly and easily visualise and compare large amounts of genomic information resulting from high-throughput sequencing experiments, scientists have now developed a new free web-based tool called Epiviz.
New York: To enable researchers to quickly and easily visualise and compare large amounts of genomic information resulting from high-throughput sequencing experiments, scientists have now developed a new free web-based tool called Epiviz.
"Epiviz helps biomedical scientists meet the challenge of visualizing large genomic data sets while supporting creative data analysis in a collaborative environment," said Hector Corrada Bravo, an assistant professor in computer science at University of Maryland in the US.
Next-generation sequencing techniques are key to understanding the molecular mechanisms underlying cell function in healthy and diseased individuals and the development of diseases like cancer.
Data from multiple experiments need to be integrated, but the growing number of data sets makes a thorough comparison and analysis of results challenging.
To visualise and browse entire genomes, graphical interfaces that display information from a database of genomic data - called "genome browsers" - were created.
According to the researchers, Epiviz offers a major advantage over browsers currently available.
Epiviz seamlessly integrates with the open-source Bioconductor analysis software widely used by genomic scientists, through its Epivizr Bioconductor package.
"Prior tools limited visualisation to presentation and dissemination, rather than a hybrid tool integrating interactive visualisation with algorithmic analysis," says Corrada Bravo.
Because Epiviz is based on the Bioconductor infrastructure, the tool supports many popular next-generation sequencing techniques, such as ChIP-seq, which is used to analyse protein interactions with DNA; RNA-seq, which reveals a comprehensive snapshot of the abundance of RNAs in cells; and DNA methylation analyses.
The new tool implements multiple visualisation methods for location-based data (such as genomic regions of interest) and feature-based data (such as gene expression), using interactive data visualisation techniques not available in web-based genome browsers.
The study appeared in the journal Nature Methods.