Avi Ma’ayan

Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool

BackgroundSystem-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement.ResultsHere, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios.ConclusionsEnrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr.

Minimal Differentiation of Classical Monocytes as They Survey Steady-State Tissues and Transport Antigen to Lymph Nodes

It is thought that monocytes rapidly differentiate to macrophages or dendritic cells (DCs) upon leaving blood. Here we have shown that Ly-6C⁺ monocytes constitutively trafficked into skin, lung, and lymph nodes (LNs). Entry was unaffected in gnotobiotic mice. Monocytes in resting lung and LN had similar gene expression profiles to blood monocytes but elevated transcripts of a limited number of genes including cyclo-oxygenase-2 (COX-2) and major histocompatibility complex class II (MHCII), induced by monocyte interaction with endothelium. Parabiosis, bromodoxyuridine (BrdU) pulse-chase analysis, and intranasal instillation of tracers indicated that instead of contributing to resident macrophages in the lung, recruited endogenous monocytes acquired antigen for carriage to draining LNs, a function redundant with DCs though differentiation to DCs did not occur. Thus, monocytes can enter steady-state nonlymphoid organs and recirculate to LNs without differentiation to macrophages or DCs, revising a long-held view that monocytes become tissue-resident macrophages by default.

The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins

Genomics, epigenomics, transcriptomics, proteomics and metabolomics efforts rapidly generate a plethora of data on the activity and levels of biomolecules within mammalian cells. At the same time, curation projects that organize knowledge from the biomedical literature into online databases are expanding. Hence, there is a wealth of information about genes, proteins and their associations, with an urgent need for data integration to achieve better knowledge extraction and data reuse. For this purpose, we developed the Harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins from over 70 major online resources. We extracted, abstracted and organized data into ∼72 million functional associations between genes/proteins and their attributes. Such attributes could be physical relationships with other biomolecules, expression in cell lines and tissues, genetic associations with knockout mouse or human phenotypes, or changes in expression after drug treatment. We stored these associations in a relational database along with rich metadata for the genes/proteins, their attributes and the original resources. The freely available Harmonizome web portal provides a graphical user interface, a web service and a mobile app for querying, browsing and downloading all of the collected data. To demonstrate the utility of the Harmonizome, we computed and visualized gene–gene and attribute–attribute similarity networks, and through unsupervised clustering, identified many unexpected relationships by combining pairs of datasets such as the association between kinase perturbations and disease signatures. We also applied supervised machine learning methods to predict novel substrates for kinases, endogenous ligands for G-protein coupled receptors, mouse phenotypes for knockout genes, and classified unannotated transmembrane proteins for likelihood of being ion channels. The Harmonizome is a comprehensive resource of knowledge about genes and proteins, and as such, it enables researchers to discover novel relationships between biological entities, as well as form novel data-driven hypotheses for experimental validation. Database URL: http://amp.pharm.mssm.edu/Harmonizome.

The characteristic direction: a geometrical approach to identify differentially expressed genes

BackgroundIdentifying differentially expressed genes (DEG) is a fundamental step in studies that perform genome wide expression profiling. Typically, DEG are identified by univariate approaches such as Significance Analysis of Microarrays (SAM) or Linear Models for Microarray Data (LIMMA) for processing cDNA microarrays, and differential gene expression analysis based on the negative binomial distribution (DESeq) or Empirical analysis of Digital Gene Expression data in R (edgeR) for RNA-seq profiling.ResultsHere we present a new geometrical multivariate approach to identify DEG called the Characteristic Direction. We demonstrate that the Characteristic Direction method is significantly more sensitive than existing methods for identifying DEG in the context of transcription factor (TF) and drug perturbation responses over a large number of microarray experiments. We also benchmarked the Characteristic Direction method using synthetic data, as well as RNA-Seq data. A large collection of microarray expression data from TF perturbations (73 experiments) and drug perturbations (130 experiments) extracted from the Gene Expression Omnibus (GEO), as well as an RNA-Seq study that profiled genome-wide gene expression and STAT3 DNA binding in two subtypes of diffuse large B-cell Lymphoma, were used for benchmarking the method using real data. ChIP-Seq data identifying DNA binding sites of the perturbed TFs, as well as known drug targets of the perturbing drugs, were used as prior knowledge silver-standard for validation. In all cases the Characteristic Direction DEG calling method outperformed other methods. We find that when drugs are applied to cells in various contexts, the proteins that interact with the drug-targets are differentially expressed and more of the corresponding genes are discovered by the Characteristic Direction method. In addition, we show that the Characteristic Direction conceptualization can be used to perform improved gene set enrichment analyses when compared with the gene-set enrichment analysis (GSEA) and the hypergeometric test.ConclusionsThe application of the Characteristic Direction method may shed new light on relevant biological mechanisms that would have remained undiscovered by the current state-of-the-art DEG methods. The method is freely accessible via various open source code implementations using four popular programming languages: R, Python, MATLAB and Mathematica, all available at: http://www.maayanlab.net/CD.

An Integrated Transcriptome Atlas of Embryonic Hair Follicle Progenitors, Their Niche, and the Developing Skin.

Defining the unique molecular features of progenitors and their niche requires a genome-wide, whole-tissue approach with cellular resolution. Here, we co-isolate embryonic hair follicle (HF) placode and dermal condensate cells, precursors of adult HF stem cells and the dermal papilla/sheath niche, along with lineage-related keratinocytes and fibroblasts, Schwann cells, melanocytes, and a population inclusive of all remaining skin cells. With next-generation RNA sequencing, we define gene expression patterns in the context of the entire embryonic skin, and through transcriptome cross-comparisons, we uncover hundreds of enriched genes in cell-type-specific signatures. Axon guidance signaling and many other pathway genes are enriched in multiple signatures, implicating these factors in driving the large-scale cellular rearrangements necessary for HF formation. Finally, we share all data in an interactive, searchable companion website. Our study provides an overarching view of signaling within the entire embryonic skin and captures a molecular snapshot of HF progenitors and their niche.

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.

AVIS: AJAX viewer of interactive signaling networks

MOTIVATION Increasing complexity of cell signaling network maps requires sophisticated visualization technologies. Simple web-based visualization tools can allow for improved data presentation and collaboration. Researchers studying cell signaling would benefit from having the ability to embed dynamic cell signaling maps in web pages. SUMMARY AVIS is a Google gadget compatible web-based viewer of interactive cell signaling networks. AVIS is an implementation of AJAX (Asynchronous JavaScript with XML) with the usage of the libraries GraphViz, ImageMagic (PerlMagic) and overLib. AVIS provides web-based visualization of text-based signaling networks with dynamical zooming, panning and linking capabilities. AVIS is a cross-platform web-based tool that can be used to visualize network maps as embedded objects in any web page. AVIS was implemented for visualization of PathwayGenerator, a tool that displays over 4000 automatically generated mammalian cell signaling maps; NodeNeighborhood a tool to visualize first and second interacting neighbors of yeast and mammalian proteins; and for Genes2Networks, a tool to connect lists of genes and protein using background protein interaction networks. AVAILABILITY A demo page of AVIS and links to applications and distributions can be found at http://actin.pharm.mssm.edu/AVIS2. Detailed instructions for using and configuring AVIS can be found in the user manual at http://actin.pharm.mssm.edu/AVIS2/manual.pdf.

L1000CDS2: LINCS L1000 characteristic direction signatures search engine

The library of integrated network-based cellular signatures (LINCS) L1000 data set currently comprises of over a million gene expression profiles of chemically perturbed human cell lines. Through unique several intrinsic and extrinsic benchmarking schemes, we demonstrate that processing the L1000 data with the characteristic direction (CD) method significantly improves signal to noise compared with the MODZ method currently used to compute L1000 signatures. The CD processed L1000 signatures are served through a state-of-the-art web-based search engine application called L1000CDS2. The L1000CDS2 search engine provides prioritization of thousands of small-molecule signatures, and their pairwise combinations, predicted to either mimic or reverse an input gene expression signature using two methods. The L1000CDS2 search engine also predicts drug targets for all the small molecules profiled by the L1000 assay that we processed. Targets are predicted by computing the cosine similarity between the L1000 small-molecule signatures and a large collection of signatures extracted from the gene expression omnibus (GEO) for single-gene perturbations in mammalian cells. We applied L1000CDS2 to prioritize small molecules that are predicted to reverse expression in 670 disease signatures also extracted from GEO, and prioritized small molecules that can mimic expression of 22 endogenous ligand signatures profiled by the L1000 assay. As a case study, to further demonstrate the utility of L1000CDS2, we collected expression signatures from human cells infected with Ebola virus at 30, 60 and 120 min. Querying these signatures with L1000CDS2 we identified kenpaullone, a GSK3B/CDK2 inhibitor that we show, in subsequent experiments, has a dose-dependent efficacy in inhibiting Ebola infection in vitro without causing cellular toxicity in human cell lines. In summary, the L1000CDS2 tool can be applied in many biological and biomedical settings, while improving the extraction of knowledge from the LINCS L1000 resource.

Network2Canvas: network visualization on a canvas with enrichment analysis

MOTIVATION Networks are vital to computational systems biology research, but visualizing them is a challenge. For networks larger than ∼100 nodes and ∼200 links, ball-and-stick diagrams fail to convey much information. To address this, we developed Network2Canvas (N2C), a web application that provides an alternative way to view networks. N2C visualizes networks by placing nodes on a square toroidal canvas. The network nodes are clustered on the canvas using simulated annealing to maximize local connections where a nodes brightness is made proportional to its local fitness. The interactive canvas is implemented in HyperText Markup Language (HTML)5 with the JavaScript library Data-Driven Documents (D3). We applied N2C to visualize 30 canvases made from human and mouse gene-set libraries and 6 canvases made from the Food and Drug Administration (FDA)-approved drug-set libraries. Given lists of genes or drugs, enriched terms are highlighted on the canvases, and their degree of clustering is computed. Because N2C produces visual patterns of enriched terms on canvases, a trained eye can detect signatures instantly. In summary, N2C provides a new flexible method to visualize large networks and can be used to perform and visualize gene-set and drug-set enrichment analyses. AVAILABILITY N2C is freely available at http://www.maayanlab.net/N2C and is open source. CONTACT avi.maayan@mssm.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Krüppel-like factor 6 regulates mitochondrial function in the kidney

Maintenance of mitochondrial structure and function is critical for preventing podocyte apoptosis and eventual glomerulosclerosis in the kidney; however, the transcription factors that regulate mitochondrial function in podocyte injury remain to be identified. Here, we identified Krüppel-like factor 6 (KLF6), a zinc finger domain transcription factor, as an essential regulator of mitochondrial function in podocyte apoptosis. We observed that podocyte-specific deletion of Klf6 increased the susceptibility of a resistant mouse strain to adriamycin-induced (ADR-induced) focal segmental glomerulosclerosis (FSGS). KLF6 expression was induced early in response to ADR in mice and cultured human podocytes, and prevented mitochondrial dysfunction and activation of intrinsic apoptotic pathways in these podocytes. Promoter analysis and chromatin immunoprecipitation studies revealed that putative KLF6 transcriptional binding sites are present in the promoter of the mitochondrial cytochrome c oxidase assembly gene (SCO2), which is critical for preventing cytochrome c release and activation of the intrinsic apoptotic pathway. Additionally, KLF6 expression was reduced in podocytes from HIV-1 transgenic mice as well as in renal biopsies from patients with HIV-associated nephropathy (HIVAN) and FSGS. Together, these findings indicate that KLF6-dependent regulation of the cytochrome c oxidase assembly gene is critical for maintaining mitochondrial function and preventing podocyte apoptosis.