Lipid identification from data produced with high-throughput technologies is essential to the elucidation of the roles played by lipids in cellular function and disease. Software tools for identifying lipids from tandem mass (MS/MS) spectra have been developed, but they are often costly or lack the sophistication of their proteomics counterparts. We have developed Greazy, an open source tool for the automated identification of phospholipids from MS/MS spectra, that utilizes methods similar to those developed for proteomics. From user-supplied parameters, Greazy builds a phospholipid search space and associated theoretical MS/MS spectra. Experimental spectra are scored against search space lipids with similar precursor masses using a peak score based on the hypergeometric distribution and an intensity score utilizing the percentage of total ion intensity residing in matching peaks. The LipidLama component filters the results via mixture modelling and density estimation. We assess Greazy’s performance against the NIST 2014 metabolomics library, observing high accuracy in a search of multiple lipid classes. We compare Greazy/LipidLama against the commercial lipid identification software LipidSearch and show that the two platforms differ considerably in the sets of identified spectra while showing good agreement on those spectra identified by both. Lastly, we demonstrate the utility of Greazy/LipidLama with different instruments. We searched data from replicates of alveolar type 2 epithelial cells obtained with an Orbitrap and from human serum replicates generated on a Q-TOF. These findings substantiate the application of proteomics derived methods to the identification of lipids. The software is available from the ProteoWizard repository: [http://tiny.cc/bumbershoot-vc12-bin64].

The National Institutes of Health (NIH), National Cancer Institute's Early Detection Research Network (EDRN) is a cross-institutional collaborative initiative seeking to accelerate the clinical application of cancer biomarker research. Over the past decade, it has been our role, as EDRN's Informatics Center (IC), to develop a comprehensive information services infrastructure as well as a set of software tools and services to support this overall initiative. We have recently developed a novel application called the Laboratory Catalog and Archive Service (LabCAS) whose focus is to extend EDRN IC data management and processing capabilities to EDRN laboratories. By leveraging the same technologies used to manage and process NASA Earth and Planetary data sets, we offer EDRN researchers an effective way of managing their laboratory data. More specifically, LabCAS enables EDRN researchers to reliably archive their experimental data, to optionally share these data in a controlled manner with other researchers, and to gain insight into these data through highly configurable data analysis pipelines tailored to the broad range of biomarker related experiments. This particular collaboration leverages expertise from NASA's Jet Propulsion Laboratory, Vanderbilt University Medical Center, and Dartmouth Medical School, as well as builds upon existing cross-governmental collaboration between NASA and the NIH.