Epigenomics and population-based genomic research have the potential to completely alter the way cancer is treated. "The data we produce and analyze will lead to new targets for drug development and a better understanding of why some patients respond better to certain drug treatments," said Peter W. Laird, principal investigator and director of the Epigenome Center at the University of Southern California. The USC Epigenome Center was awarded funding in 2009 to participate in The Cancer Genome Atlas, a consortium that aims to develop a comprehensive map of molecular changes in cancer.

To achieve the promise of epigenomics, labs will need to focus on finding new ways to process and analyze the data generated by next-generation sequencing efforts. Yet the same bioinformaticians that develop USC’s custom analysis and data processing tools are often tasked with mundane activities such as sample tracking and protocol management.

"It was very important to us to be able to have our [bioinformaticians] just spending their time working on the science aspects of the analysis, and not having to spend all their time working on sample tracking when we could buy a product to do this," said Dr. Benjamin Berman, senior research associate at the USC Epigenome Center.

By implementing a LIMS specifically built for NGS, the USC Epigenome Center has been able to focus its bioinformaticians and software developers on science. The LIMS offers core functionality addressing such activities as tracking samples, managing lab workflows, generating reports, managing QA/QC, and communicating with collaborators. These functions integrate information from the lab’s key instrumentation platforms.

Most importantly, though, the LIMS includes a RESTful application programming interface that bioinformaticians can use to build custom interfaces between the LIMS and the lab’s own analysis pipeline. Pipeline scripts are autopopulated by data stored in the LIMS, which speeds analysis. Researchers have also been able to leverage data stored in the LIMS to develop better tools for comparing run quality, normalizing data, and analyzing the results from sequencing runs. USC aims eventually to generalize its workflows and processing steps in order to distribute them as open-source tools.

"A key requirement for [a LIMS] is it has to handle a really quickly evolving lab workflow and analysis workflow," said Berman. "What is nice about the LIMS is that it frees up our in-house software developers’ time from writing sample tracking software to work on more customized projects."