Interinstitutional collaboration for end-user bioinformatics training: Cytoscape as a case study.

BACKGROUND: This case study describes the value of and need for interinstitutional collaboration to train biomedical researchers in data visualization, using Cytoscape network analysis software as an example. To provide training on Cytoscape software to Yale University biomedical researchers, a collaboration was formed between the Yale Harvey Cushing/John Hay Whitney Medical Library's (CWML's) biomedical sciences research support librarian and the University of Michigan (U-M) Taubman Health Sciences Library's bioinformationist, who has expertise in Cytoscape software. CASE PRESENTATION: The U-M bioinformationist offered a webinar to the Yale community, followed by a one-day onsite workshop. This collaboration allowed Yale biomedical researchers and librarians to receive training on Cytoscape software, in addition to giving the Yale librarian a collaborator for answering future questions about the software. CONCLUSIONS: This collaboration furthered the U-M bioinformationist's role in the field as an expert in Cytoscape instruction, while also establishing the CWML as a leader in providing support for analyzing and visualizing molecular data at Yale University. The authors found this collaboration to be a successful way for librarians to fill end-user training gaps in rapidly changing fields such as bioinformatics.

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data.

A significant challenge in the analysis of omics data is extracting actionable biological knowledge. Metabolomics is no exception. The general problem of relating changes in levels of individual metabolites to specific biological processes is compounded by the large number of unknown metabolites present in untargeted liquid chromatography-mass spectrometry (LC-MS) studies. Further, secondary metabolism and lipid metabolism are poorly represented in existing pathway databases. To overcome these limitations, our group has developed several tools for data-driven network construction and analysis. These include CorrelationCalculator and Filigree. Both tools allow users to build partial correlation-based networks from experimental metabolomics data when the number of metabolites exceeds the number of samples. CorrelationCalculator supports the construction of a single network, while Filigree allows building a differential network utilizing data from two groups of samples, followed by network clustering and enrichment analysis. We will describe the utility and application of both tools for the analysis of real-life metabolomics data.

Evaluation of a library outreach program to research labs.

The goal of this study was to conduct an outcomes-based evaluation of the National Cancer Institute-Frederick (NCI-F) Scientific Library's Laptop Librarian service, where librarians took a laptop and spent time in research buildings. The authors used statistics from the Laptop Librarian sessions, a NCI-F community-wide online survey, and in-person interviews to evaluate the service. The Laptop Librarian service increased the accessibility of librarians and saved patrons' time. Users gained useful information and expressed overall satisfaction with the service. The Laptop Librarian service proves to be a useful means for increasing access to librarians and providing users with necessary information at this government research facility.

Application of Differential Network Enrichment Analysis for Deciphering Metabolic Alterations.

Modern analytical methods allow for the simultaneous detection of hundreds of metabolites, generating increasingly large and complex data sets. The analysis of metabolomics data is a multi-step process that involves data processing and normalization, followed by statistical analysis. One of the biggest challenges in metabolomics is linking alterations in metabolite levels to specific biological processes that are disrupted, contributing to the development of disease or reflecting the disease state. A common approach to accomplishing this goal involves pathway mapping and enrichment analysis, which assesses the relative importance of predefined metabolic pathways or other biological categories. However, traditional knowledge-based enrichment analysis has limitations when it comes to the analysis of metabolomics and lipidomics data. We present a Java-based, user-friendly bioinformatics tool named Filigree that provides a primarily data-driven alternative to the existing knowledge-based enrichment analysis methods. Filigree is based on our previously published differential network enrichment analysis (DNEA) methodology. To demonstrate the utility of the tool, we applied it to previously published studies analyzing the metabolome in the context of metabolic disorders (type 1 and 2 diabetes) and the maternal and infant lipidome during pregnancy.