Genomic sequencing reveals the entire genetic makeup of an organism. Many biomedical researchers are striving to make sense of the flood of data that has followed recent advances in genomic sequencing technologies. In particular, researchers are often limited by the challenge of getting multiple bioinformatics tools to 'talk' to one another.
To help address this need, researchers at University of California, San Diego School of Medicine, in collaboration with labs at the Broad Institute of MIT and Harvard, Stanford University, Weizmann Institute and Pennsylvania State University, developed GenomeSpace, a cloud-based, biologist-friendly platform that connects more than 20 bioinformatics software packages and resources for genomic data analysis.
The team is now developing and crowdsourcing 'recipes' - step-by-step workflows - to better enable non-programming researchers to interpret their genomic data. The work is described in a paper published in Nature Methods.
Before GenomeSpace, it was extraordinarily difficult for researchers, especially without programming skills, to get many of the available analysis tools to work together. Users needed to know how to write short computer programs in order to transform and transfer data between platforms. GenomeSpace now performs this service seamlessly with a user-friendly interface, connecting popular genomic data analysis tools such as Cytoscape, Galaxy, GenePattern and the Integrative Genomics Viewer (IGV). Several of these tools are themselves 'tool aggregators', so in linking them, GenomeSpace provides access to hundreds of bioinformatics analyses.
What's more, GenomeSpace doesn't just leave users on their own to determine the best tools for their particular research questions. The site also provides 'recipes' - easy-to-follow example workflows that clearly demonstrate the sequence of tools researchers should use to get the information they are looking to extract from their raw data. GenomeSpace currently provides 13 recipes. The platform's developers are now inviting the user community to contribute their own additional recipes.
Michael Reich at UC San Diego School of Medicine, who leads the GenomeSpace development team, said, "No individual lab can possibly develop all the right useful recipes - crowdsourcing will help make GenomeSpace even more useful to non-programming researchers."
Here's how an example GenomeSpace recipe works: A researcher wonders if there is a specific set of genes that leukemia stem cells express differently than normal white blood cell precursors. She also wants to better understand the biological mechanism underlying those differentially expressed genes but doesn't know where to start. With GenomeSpace, the researcher can simply upload the gene expression data and other information about the two cell types (the 'ingredients') and follow a GenomeSpace recipe, designed specifically for these types of research questions. In this case, the recipe tells the researcher how to run the data ingredients through two tools available in GenomeSpace: 1) GenePattern, which finds a list of the 50 genes that differ the most between the two cell types and 2) Cytoscape, which identifies how proteins associated with these genes interact in networks, thus providing clues to the roles that tumor-specific or normal cell-specific genes play in the body.
This type of information provided by GenomeSpace could help the researcher better understand how leukemia develops and help identify possible targets for new therapeutics, said Reich.
Mesirov said, "Our recipe resource was modeled on Tom Maniatis' classic, Molecular Cloning: A Laboratory Manual. We hope, with a combination of our own development and crowdsourcing, to grow the resource and increase its breadth. It's our long-term goal to convert these descriptive workflows into more dynamic, interactive interfaces making them even easier to follow."