Scientists have developed an interactive tool to help researchers and clinicians explore the genetic underpinnings of cancer.
The tool - Mutation Annotation and Genome Interpretation (MAGI) - is an open-source web application that enables users to search, visualise, and annotate large public cancer genetic datasets, including data from The Cancer Genome Atlas (TCGA) project.
"MAGI lets users explore these data in a regular web browser and with no computational expertise required," said Max Leiserson, led developer of the tool.
In addition to viewing TCGA data, it also allows users to upload their own data and compare their results to those in the larger databases.
Over the last decade, researchers working with TCGA have sequenced genes from thousands of tumours and dozens of cancer types in an effort to understand which mutations contribute to the development of cancer.
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At the same time, as sequencing has gotten faster and cheaper, individual researchers have begun sequencing samples from their own studies, sometimes from just a few tumours.
By uploading their data to MAGI, researchers can leverage the large public datasets to help interpret their own data.
"In cancer genomes, there's real value in large sample sizes because mutations are diverse and spread all over the genome," said Ben Raphael, director of Brown's Centre for Computational and Molecular Biology.
"If I had just sequenced a few cancer genomes from my local tumour bank, one of the first things I'd want to do is compare my data to these big public datasets and look for similarities," Raphael said.
MAGI has data from TCGA already loaded. Users can search by cancer type, by individual genes, or by groups of genes.
The output offers several ways of visualising the search results, showing how often a given gene is mutated across samples, what types of mutations they were, and other information.
"When someone uploads data to MAGI, they can use the public data to help them interpret their own dataset," Raphael said.
The lab is making MAGI available for free, with the hope that many in the cancer genomics community will take advantage of it.
