1
|
Wang Z, Clark NR, Ma'ayan A. Drug-induced adverse events prediction with the LINCS L1000 data. Bioinformatics 2016; 32:2338-45. [PMID: 27153606 PMCID: PMC4965635 DOI: 10.1093/bioinformatics/btw168] [Citation(s) in RCA: 107] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Revised: 03/05/2016] [Accepted: 03/23/2016] [Indexed: 01/22/2023] Open
Abstract
MOTIVATION Adverse drug reactions (ADRs) are a central consideration during drug development. Here we present a machine learning classifier to prioritize ADRs for approved drugs and pre-clinical small-molecule compounds by combining chemical structure (CS) and gene expression (GE) features. The GE data is from the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 dataset that measured changes in GE before and after treatment of human cells with over 20 000 small-molecule compounds including most of the FDA-approved drugs. Using various benchmarking methods, we show that the integration of GE data with the CS of the drugs can significantly improve the predictability of ADRs. Moreover, transforming GE features to enrichment vectors of biological terms further improves the predictive capability of the classifiers. The most predictive biological-term features can assist in understanding the drug mechanisms of action. Finally, we applied the classifier to all >20 000 small-molecules profiled, and developed a web portal for browsing and searching predictive small-molecule/ADR connections. AVAILABILITY AND IMPLEMENTATION The interface for the adverse event predictions for the >20 000 LINCS compounds is available at http://maayanlab.net/SEP-L1000/ CONTACT: avi.maayan@mssm.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Zichen Wang
- Department of Pharmacology and Systems Therapeutics, One Gustave L. Levy Place Box 1215, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Neil R Clark
- Department of Pharmacology and Systems Therapeutics, One Gustave L. Levy Place Box 1215, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Avi Ma'ayan
- Department of Pharmacology and Systems Therapeutics, One Gustave L. Levy Place Box 1215, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| |
Collapse
|
2
|
Rouillard AD, Gundersen GW, Fernandez NF, Wang Z, Monteiro CD, McDermott MG, Ma'ayan A. The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database (Oxford) 2016; 2016:baw100. [PMID: 27374120 PMCID: PMC4930834 DOI: 10.1093/database/baw100] [Citation(s) in RCA: 889] [Impact Index Per Article: 111.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2016] [Revised: 05/15/2016] [Accepted: 05/31/2016] [Indexed: 12/18/2022]
Abstract
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.
Collapse
Affiliation(s)
- Andrew D Rouillard
- Department of Pharmacology and Systems Therapeutics, Department of Genetics and Genomic Sciences, BD2K-LINCS Data Coordination and Integration Center (DCIC), Mount Sinai's Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gregory W Gundersen
- Department of Pharmacology and Systems Therapeutics, Department of Genetics and Genomic Sciences, BD2K-LINCS Data Coordination and Integration Center (DCIC), Mount Sinai's Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nicolas F Fernandez
- Department of Pharmacology and Systems Therapeutics, Department of Genetics and Genomic Sciences, BD2K-LINCS Data Coordination and Integration Center (DCIC), Mount Sinai's Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Zichen Wang
- Department of Pharmacology and Systems Therapeutics, Department of Genetics and Genomic Sciences, BD2K-LINCS Data Coordination and Integration Center (DCIC), Mount Sinai's Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Caroline D Monteiro
- Department of Pharmacology and Systems Therapeutics, Department of Genetics and Genomic Sciences, BD2K-LINCS Data Coordination and Integration Center (DCIC), Mount Sinai's Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael G McDermott
- Department of Pharmacology and Systems Therapeutics, Department of Genetics and Genomic Sciences, BD2K-LINCS Data Coordination and Integration Center (DCIC), Mount Sinai's Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Avi Ma'ayan
- Department of Pharmacology and Systems Therapeutics, Department of Genetics and Genomic Sciences, BD2K-LINCS Data Coordination and Integration Center (DCIC), Mount Sinai's Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| |
Collapse
|
3
|
Reprint of “Abstraction for data integration: Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction”. Comput Biol Chem 2015; 59 Pt B:123-38. [DOI: 10.1016/j.compbiolchem.2015.08.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2015] [Revised: 06/04/2015] [Accepted: 06/05/2015] [Indexed: 12/21/2022]
|
4
|
Rouillard AD, Wang Z, Ma’ayan A. Publisher’s Note:Abstraction for data integration:Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction. Comput Biol Chem 2015; 58:104-19. [PMID: 26101093 PMCID: PMC4675694 DOI: 10.1016/j.compbiolchem.2015.06.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2015] [Revised: 06/04/2015] [Accepted: 06/05/2015] [Indexed: 12/27/2022]
Abstract
With advances in genomics, transcriptomics, metabolomics and proteomics, and more expansive electronic clinical record monitoring, as well as advances in computation, we have entered the Big Data era in biomedical research. Data gathering is growing rapidly while only a small fraction of this data is converted to useful knowledge or reused in future studies. To improve this, an important concept that is often overlooked is data abstraction. To fuse and reuse biomedical datasets from diverse resources, data abstraction is frequently required. Here we summarize some of the major Big Data biomedical research resources for genomics, proteomics and phenotype data, collected from mammalian cells, tissues and organisms. We then suggest simple data abstraction methods for fusing this diverse but related data. Finally, we demonstrate examples of the potential utility of such data integration efforts, while warning about the inherit biases that exist within such data.
Collapse
Affiliation(s)
- Andrew D. Rouillard
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1215, New York, NY 10029
- BD2K-LINCS Data Coordination and Integration Center
- Illuminating the Druggable Genome Knowledge Management Center
| | - Zichen Wang
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1215, New York, NY 10029
- BD2K-LINCS Data Coordination and Integration Center
- Illuminating the Druggable Genome Knowledge Management Center
| | - Avi Ma’ayan
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1215, New York, NY 10029
- BD2K-LINCS Data Coordination and Integration Center
- Illuminating the Druggable Genome Knowledge Management Center
| |
Collapse
|
5
|
Teng B, Zhao C, Liu X, He Z. Network inference from AP-MS data: computational challenges and solutions. Brief Bioinform 2014; 16:658-74. [DOI: 10.1093/bib/bbu038] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2014] [Accepted: 09/30/2014] [Indexed: 02/04/2023] Open
|
6
|
Ma'ayan A, Rouillard AD, Clark NR, Wang Z, Duan Q, Kou Y. Lean Big Data integration in systems biology and systems pharmacology. Trends Pharmacol Sci 2014; 35:450-60. [PMID: 25109570 PMCID: PMC4153537 DOI: 10.1016/j.tips.2014.07.001] [Citation(s) in RCA: 75] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2014] [Revised: 07/01/2014] [Accepted: 07/08/2014] [Indexed: 12/11/2022]
Abstract
Data sets from recent large-scale projects can be integrated into one unified puzzle that can provide new insights into how drugs and genetic perturbations applied to human cells are linked to whole-organism phenotypes. Data that report how drugs affect the phenotype of human cell lines and how drugs induce changes in gene and protein expression in human cell lines can be combined with knowledge about human disease, side effects induced by drugs, and mouse phenotypes. Such data integration efforts can be achieved through the conversion of data from the various resources into single-node-type networks, gene-set libraries, or multipartite graphs. This approach can lead us to the identification of more relationships between genes, drugs, and phenotypes as well as benchmark computational and experimental methods. Overall, this lean 'Big Data' integration strategy will bring us closer toward the goal of realizing personalized medicine.
Collapse
Affiliation(s)
- Avi Ma'ayan
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, Systems Biology Center New York (SBCNY), One Gustave L. Levy Place, Box 1215, New York, NY 10029, USA.
| | - Andrew D Rouillard
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, Systems Biology Center New York (SBCNY), One Gustave L. Levy Place, Box 1215, New York, NY 10029, USA
| | - Neil R Clark
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, Systems Biology Center New York (SBCNY), One Gustave L. Levy Place, Box 1215, New York, NY 10029, USA
| | - Zichen Wang
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, Systems Biology Center New York (SBCNY), One Gustave L. Levy Place, Box 1215, New York, NY 10029, USA
| | - Qiaonan Duan
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, Systems Biology Center New York (SBCNY), One Gustave L. Levy Place, Box 1215, New York, NY 10029, USA
| | - Yan Kou
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, Systems Biology Center New York (SBCNY), One Gustave L. Levy Place, Box 1215, New York, NY 10029, USA
| |
Collapse
|
7
|
Duan Q, Flynn C, Niepel M, Hafner M, Muhlich JL, Fernandez NF, Rouillard AD, Tan CM, Chen EY, Golub TR, Sorger PK, Subramanian A, Ma'ayan A. LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures. Nucleic Acids Res 2014; 42:W449-60. [PMID: 24906883 PMCID: PMC4086130 DOI: 10.1093/nar/gku476] [Citation(s) in RCA: 198] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB implements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an experimental condition highlights gene-sets enriched for the differentially expressed genes from the selected experiment. A search interface allows users to input gene lists and query them against over 100 000 conditions to find the top matching experiments. The tool integrates many resources for an unprecedented potential for new discoveries in systems biology and systems pharmacology. The LCB application is available at http://www.maayanlab.net/LINCS/LCB. Customized versions will be made part of the http://lincscloud.org and http://lincs.hms.harvard.edu websites.
Collapse
Affiliation(s)
- Qiaonan Duan
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, Systems Biology Center New York (SBCNY), One Gustave L. Levy Place, New York, NY 10029, USA
| | - Corey Flynn
- Broad Institute, 7 Cambridge Center, Cambridge, MA 02142, USA
| | - Mario Niepel
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
| | - Marc Hafner
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
| | - Jeremy L Muhlich
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
| | - Nicolas F Fernandez
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, Systems Biology Center New York (SBCNY), One Gustave L. Levy Place, New York, NY 10029, USA
| | - Andrew D Rouillard
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, Systems Biology Center New York (SBCNY), One Gustave L. Levy Place, New York, NY 10029, USA
| | - Christopher M Tan
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, Systems Biology Center New York (SBCNY), One Gustave L. Levy Place, New York, NY 10029, USA
| | - Edward Y Chen
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, Systems Biology Center New York (SBCNY), One Gustave L. Levy Place, New York, NY 10029, USA
| | - Todd R Golub
- Broad Institute, 7 Cambridge Center, Cambridge, MA 02142, USA
| | - Peter K Sorger
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
| | | | - Avi Ma'ayan
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, Systems Biology Center New York (SBCNY), One Gustave L. Levy Place, New York, NY 10029, USA
| |
Collapse
|
8
|
Kalantzaki K, Bei ES, Exarchos KP, Zervakis M, Garofalakis M, Fotiadis DI. Nonparametric network design and analysis of disease genes in oral cancer progression. IEEE J Biomed Health Inform 2014; 18:562-73. [PMID: 24608056 DOI: 10.1109/jbhi.2013.2274643] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Biological networks in living organisms can be seen as the ultimate means of understanding the underlying mechanisms in complex diseases, such as oral cancer. During the last decade, many algorithms based on high-throughput genomic data have been developed to unravel the complexity of gene network construction and their progression in time. However, the small size of samples compared to the number of observed genes makes the inference of the network structure quite challenging. In this study, we propose a framework for constructing and analyzing gene networks from sparse experimental temporal data and investigate its potential in oral cancer. We use two network models based on partial correlations and kernel density estimation, in order to capture the genetic interactions. Using this network construction framework on real clinical data of the tissue and blood at different time stages, we identified common disease-related structures that may decipher the association between disease state and biological processes in oral cancer. Our study emphasizes an altered MET (hepatocyte growth factor receptor) network during oral cancer progression. In addition, we demonstrate that the functional changes of gene interactions during oral cancer progression might be particularly useful for patient categorization at the time of diagnosis and/or at follow-up periods.
Collapse
|
9
|
Xu H, Baroukh C, Dannenfelser R, Chen EY, Tan CM, Kou Y, Kim YE, Lemischka IR, Ma'ayan A. ESCAPE: database for integrating high-content published data collected from human and mouse embryonic stem cells. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2013; 2013:bat045. [PMID: 23794736 PMCID: PMC3689438 DOI: 10.1093/database/bat045] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
High content studies that profile mouse and human embryonic stem cells (m/hESCs) using various genome-wide technologies such as transcriptomics and proteomics are constantly being published. However, efforts to integrate such data to obtain a global view of the molecular circuitry in m/hESCs are lagging behind. Here, we present an m/hESC-centered database called Embryonic Stem Cell Atlas from Pluripotency Evidence integrating data from many recent diverse high-throughput studies including chromatin immunoprecipitation followed by deep sequencing, genome-wide inhibitory RNA screens, gene expression microarrays or RNA-seq after knockdown (KD) or overexpression of critical factors, immunoprecipitation followed by mass spectrometry proteomics and phosphoproteomics. The database provides web-based interactive search and visualization tools that can be used to build subnetworks and to identify known and novel regulatory interactions across various regulatory layers. The web-interface also includes tools to predict the effects of combinatorial KDs by additive effects controlled by sliders, or through simulation software implemented in MATLAB. Overall, the Embryonic Stem Cell Atlas from Pluripotency Evidence database is a comprehensive resource for the stem cell systems biology community. Database URL: http://www.maayanlab.net/ESCAPE
Collapse
Affiliation(s)
- Huilei Xu
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1215, New York, NY 10029, USA
| | | | | | | | | | | | | | | | | |
Collapse
|
10
|
Tan CM, Chen EY, Dannenfelser R, Clark NR, Ma'ayan A. Network2Canvas: network visualization on a canvas with enrichment analysis. ACTA ACUST UNITED AC 2013; 29:1872-8. [PMID: 23749960 DOI: 10.1093/bioinformatics/btt319] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
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 node's 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.
Collapse
Affiliation(s)
- Christopher M Tan
- Department of Pharmacology and Systems Therapeutics, Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | | | | | | |
Collapse
|
11
|
Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, Clark NR, Ma'ayan A. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 2013; 14:128. [PMID: 23586463 PMCID: PMC3637064 DOI: 10.1186/1471-2105-14-128] [Citation(s) in RCA: 4247] [Impact Index Per Article: 386.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2013] [Accepted: 03/28/2013] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND System-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. RESULTS Here, 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. CONCLUSIONS Enrichr 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.
Collapse
Affiliation(s)
- Edward Y Chen
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, One Gustave L, Levy Place, Box 1215, New York, NY 10029, USA
| | | | | | | | | | | | | | | |
Collapse
|
12
|
Kou Y, Chen EY, Clark NR, Duan Q, Tan CM, Ma‘ayan A. ChEA2: Gene-Set Libraries from ChIP-X Experiments to Decode the Transcription Regulome. AVAILABILITY, RELIABILITY, AND SECURITY IN INFORMATION SYSTEMS AND HCI 2013. [DOI: 10.1007/978-3-642-40511-2_30] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
|