Sherry L. Jenkins

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.

Systems approach to explore components and interactions in the presynapse

The application of proteomic techniques to neuroscientific research provides an opportunity for a greater understanding of nervous system structure and function. As increasing amounts of neuroproteomic data become available, it is necessary to formulate methods to integrate these data in a meaningful way to obtain a more comprehensive picture of neuronal subcompartments. Furthermore, computational methods can be used to make biologically relevant predictions from large proteomic data sets. Here, we applied an integrated proteomics and systems biology approach to characterize the presynaptic (PRE) nerve terminal. For this, we carried out proteomic analyses of presynaptically enriched fractions, and generated a PRE literature‐based protein–protein interaction network. We combined these with other proteomic analyses to generate a core list of 117 PRE proteins, and used graph theory‐inspired algorithms to predict 92 additional components and a PRE complex containing 17 proteins. Some of these predictions were validated experimentally, indicating that the computational analyses can identify novel proteins and complexes in a subcellular compartment. We conclude that the combination of techniques (proteomics, data integration, and computational analyses) used in this study are useful in obtaining a comprehensive understanding of functional components, especially low‐abundance entities and/or interactions in the PRE nerve terminal.

SNAVI: Desktop application for analysis and visualization of large-scale signaling networks

BackgroundStudies of cellular signaling indicate that signal transduction pathways combine to form large networks of interactions. Viewing protein-protein and ligand-protein interactions as graphs (networks), where biomolecules are represented as nodes and their interactions are represented as links, is a promising approach for integrating experimental results from different sources to achieve a systematic understanding of the molecular mechanisms driving cell phenotype. The emergence of large-scale signaling networks provides an opportunity for topological statistical analysis while visualization of such networks represents a challenge.ResultsSNAVI is Windows-based desktop application that implements standard network analysis methods to compute the clustering, connectivity distribution, and detection of network motifs, as well as provides means to visualize networks and network motifs. SNAVI is capable of generating linked web pages from network datasets loaded in text format. SNAVI can also create networks from lists of gene or protein names.ConclusionSNAVI is a useful tool for analyzing, visualizing and sharing cell signaling data. SNAVI is open source free software. The installation may be downloaded from: http://snavi.googlecode.com. The source code can be accessed from: http://snavi.googlecode.com/svn/trunk

Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd.

Gene expression data are accumulating exponentially in public repositories. Reanalysis and integration of themed collections from these studies may provide new insights, but requires further human curation. Here we report a crowdsourcing project to annotate and reanalyse a large number of gene expression profiles from Gene Expression Omnibus (GEO). Through a massive open online course on Coursera, over 70 participants from over 25 countries identify and annotate 2,460 single-gene perturbation signatures, 839 disease versus normal signatures, and 906 drug perturbation signatures. All these signatures are unique and are manually validated for quality. Global analysis of these signatures confirms known associations and identifies novel associations between genes, diseases and drugs. The manually curated signatures are used as a training set to develop classifiers for extracting similar signatures from the entire GEO repository. We develop a web portal to serve these signatures for query, download and visualization.

Neuro2A differentiation by Galphai/o pathway.

Neurite outgrowth can be stimulated by G protein-coupled receptors that signal through Gαi and Gαo proteins. Signaling from Gi/o-coupled G protein–coupled receptors (GPCRs), such as the serotonin 1B, cannabinoid 1, and dopamine D2 receptors, inhibits cAMP production by adenylyl cyclases and activates protein kinases, such as Src, mitogen-activated protein kinases 1 and 2, and Akt. Activation of these protein kinases results in stimulation of neurite outgrowth in the central nervous system (CNS) and in neuronal cell lines. This Connections Map traces downstream signaling pathways from Gi/o-coupled GPCRs to key protein kinases and key transcription factors involved in neuronal differentiation. Components in the Science Signaling Connections Map are linked to Nature Molecule Pages. This interoperability provides ready access to detail that includes information about specific states for the nodes.

Systems biology--biomedical modeling.

This course introduces students to computational principles and approaches used in systems biology. Because of the complexity inherent in biological systems, many researchers frequently rely on a combination of global analysis and computational approaches to gain insight into both (i) how interacting components can produce complex system behaviors, and (ii) how changes in conditions may alter these behaviors. Because the biological details of a particular system are generally not taught along with the quantitative approaches that enable hypothesis generation and analysis of the system, we developed a course at Mount Sinai School of Medicine that introduces first-year graduate students to these computational principles and approaches. We anticipate that such approaches will apply throughout the biomedical sciences and that courses such as the one described here will become a core requirement of many graduate programs in the biological and biomedical sciences.

Genes2WordCloud: a quick way to identify biological themes from gene lists and free text.

BackgroundWord-clouds recently emerged on the web as a solution for quickly summarizing text by maximizing the display of most relevant terms about a specific topic in the minimum amount of space. As biologists are faced with the daunting amount of new research data commonly presented in textual formats, word-clouds can be used to summarize and represent biological and/or biomedical content for various applications.ResultsGenes2WordCloud is a web application that enables users to quickly identify biological themes from gene lists and research relevant text by constructing and displaying word-clouds. It provides users with several different options and ideas for the sources that can be used to generate a word-cloud. Different options for rendering and coloring the word-clouds give users the flexibility to quickly generate customized word-clouds of their choice.MethodsGenes2WordCloud is a word-cloud generator and a word-cloud viewer that is based on WordCram implemented using Java, Processing, AJAX, mySQL, and PHP. Text is fetched from several sources and then processed to extract the most relevant terms with their computed weights based on word frequencies. Genes2WordCloud is freely available for use online; it is open source software and is available for installation on any web-site along with supporting documentation at http://www.maayanlab.net/G2W.ConclusionsGenes2WordCloud provides a useful way to summarize and visualize large amounts of textual biological data or to find biological themes from several different sources. The open source availability of the software enables users to implement customized word-clouds on their own web-sites and desktop applications.

Data Portal for the Library of Integrated Network-based Cellular Signatures (LINCS) program: Integrated access to diverse large-scale cellular perturbation response data

Abstract The Library of Integrated Network-based Cellular Signatures (LINCS) program is a national consortium funded by the NIH to generate a diverse and extensive reference library of cell-based perturbation-response signatures, along with novel data analytics tools to improve our understanding of human diseases at the systems level. In contrast to other large-scale data generation efforts, LINCS Data and Signature Generation Centers (DSGCs) employ a wide range of assay technologies cataloging diverse cellular responses. Integration of, and unified access to LINCS data has therefore been particularly challenging. The Big Data to Knowledge (BD2K) LINCS Data Coordination and Integration Center (DCIC) has developed data standards specifications, data processing pipelines, and a suite of end-user software tools to integrate and annotate LINCS-generated data, to make LINCS signatures searchable and usable for different types of users. Here, we describe the LINCS Data Portal (LDP) (http://lincsportal.ccs.miami.edu/), a unified web interface to access datasets generated by the LINCS DSGCs, and its underlying database, LINCS Data Registry (LDR). LINCS data served on the LDP contains extensive metadata and curated annotations. We highlight the features of the LDP user interface that is designed to enable search, browsing, exploration, download and analysis of LINCS data and related curated content.

Systems pharmacology meets predictive, preventive, personalized and participatory medicine.

computational biology; drug–drug networks; systems biologyThe vision of predictive, preventive, personalized, and participatory (P4) [1] medicine isexpected to transform healthcare in the near future, but how exactly is this going to happen?Transformative changes that are relevant to P4 medicine are beginning to bud in theemerging sphere of a new discipline called systems pharmacology. Systems pharmacologycombines high-throughput genome-wide experiments with advanced computation andmodeling to understand drug action in cells and drug-induced events that perturb the humanphenotype. Systems pharmacology combines systems biology with pharmacology but alsoinvolves genetics, genomics and computer science. It attempts to link drug perturbations ofthe molecular networks of cells to the human phenotype. Here we will discuss how thetransformative potential of systems pharmacology touches different aspects of P4 medicine.We focus on two aspects of systems pharmacology: generating and analyzing drug-inducedgene-expression signatures and mining drug/adverse event connections, as well as thepotential synergy of these two activities.Our ability to sequence DNA and RNA fast and inexpensively is opening doors to manyapplications that could transform medicine. For instance, whole-genome sequencing ofmillions of individuals could be used to detect mutations that can further characterizegenetic disorders and identify novel drug targets [2]. In addition, gene expression,metabolomics or proteomics of cells from the blood [3], or sequencing of the microbiomefrom our stool, mouth or skin [4], can be used to monitor the health status of an individualover time with great accuracy [3]. Such data can be correlated with drugs taken byindividuals and the adverse events they may experience. A related transformative approachinvolves the screening of drugs by applying them to stimulate human cells and then

Inquiry learning. Integrating content detail and critical reasoning by peer review.

Classroom lectures by experts in combination with journal clubs and Web-based discussion forums help graduate students develop critical reasoning skills.