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This web-site was developed to support the publications:

Xu H, Ang YS, Sevilla A, Lemischka IR, Ma'ayan A. Construction and validation of a regulatory network for pluripotency and self-renewal of mouse embryonic stem cells. PLoS Comput Biol. 2014 Aug 14;10(8):e1003777 | PubMed | single cell and KD followed by RT-PCR data |

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 (Oxford). 2013 Jun 21;2013:bat045 | PubMed |

Please cite these papers if you use the resources available on this web-site!

Generate a network from a list of genes and background networks based on ESCAPE.

Use ESCAPE to conduct enrichment analysis.

Look through all available data tables.

Download an individual table or the entire ESCAPE database.

206,521 interactions from Chip-seq/chip studies

153,920 interactions from logof followed by microarrays

1,037 protein protein interactions

693,552 miRNA target interactions

813 Putative pluripotency genes determined by RNAi screens

19,801 Undifferentiated and differentiating ESC specific proteins

16,881 histone modifications determined by Chip-seq/chip

8,323 Undifferentiated and differentiating ESC specific phosphoproteins from phosphoproteomics

9 genome-wide mRNA expression profile summaries

3,136 miRNA expression entries

14,105 time course expression entries from J1 #1

17,022 time course expression entries from J1 #2

14,105 time course expression entries from R1 #1

17,022 time course expression entries from R1 #2

14,668 time course expression entries from V6.5 #1

17,022 time course expression entries from V6.5 #2

14,698 time-course expression entries of shRNAi #1

18,265 time-course expression entries of shRNAi #2


Wdr5 Mediates Self-Renewal and Reprogramming via the Embryonic Stem Cell Core Transcriptional Network.
Yen-Sin Ang, Su-Yi Tsai, Dung-Fang Lee, Jonathan Monk, Jie Su, Kajan Ratnakumar, et al. in Cell (2011)

SVM classifier to predict genes important for self-renewal and pluripotency of mouse embryonic stem cells.
Huilei Xu, Ihor R Lemischka, Avi Ma'ayan in BMC systems biology (2010)

GATE: software for the analysis and visualization of high-dimensional time series expression data.
Ben D MacArthur, Alexander Lachmann, Ihor R Lemischka, Avi Ma'ayan in Bioinformatics (2010)

Systems-level dynamic analyses of fate change in murine embryonic stem cells.
Rong Lu, Florian Markowetz, Richard D Unwin, Jeffrey T Leek, Edoardo M Airoldi, Ben D MacArthur, et al. in Nature (2009)

Systems biology of stem cell fate and cellular reprogramming.
Ben D MacArthur, Avi Ma'ayan, Ihor R Lemischka in Nat Rev Mol Cell Biol (2009)

lineage prediction
Control the levels of OCT4, NANOG, and SOX2 to predict differentiation into 1 of 4 lineages.

MATLAB software for learning Boolean transition functions given a directed network.
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 (ESCAPE) integrating data from many recent diverse high-throughput studies including: gene expression microarrays, RNA-seq, ChIP-chip/seq, genome-wide inhibitory RNA (RNAi) screens, immunoprecipitation followed by mass spectrometry (IP-MS) proteomics and phosphoproteomics. The database provides web-based interactive search and visualization tools that can be used to build subnetworks and identify known and novel regulatory interactions across various regulatory layers, as well as predict the effects of combinatorial knockdowns.

Stem cells, systems biology and human feedback
Mount Sinai researchers hope that systems biology can show how molecular processes within a cell control its fate. Lemischka works with his biologist colleagues to perturb gene expression in stem cells. Using the technique of RNA interference, they can remove one transcription factor at a time at time zero, right after cell division, and measure changes such as those in transcription levels for genes over time.

New database could speed up drug discovery
A new database and software, called ChIP Enrichment Analysis, or ChEA, is set to revolutionize how researchers identify drug targets and biomarkers. Until ChEA was developed, no centralized database integrated results from, for instance, ChIP-seq and ChIP-chip experiments (these are used to identify how "transcription factor" proteins might regulate all genes in humans and mice). Now this new computational method should help streamline how scientists analyze these gene expression experiments.

Try these other tools developed by the Ma'ayan Lab:
Enrichr- Analyze Gene Lists with over 30 Gene Set Libraries Drug Pair Seeker- Identify Pairs of Drugs that can Reverse or Mimic Gene Expression Patterns ChIP-X Enrichment Analysis- Database of Transcription Factors and their Target Genes extracted from ChIP-seq and ChIP-chip studies Kinase Enrichment Analysis- Database of Literature-Based Kinase Substrate Interactions LINCS Canvas Browser- Query and Visualize 1000's of L1000 Experiments GATE- Desktop Application for Gene Expression Time-Series Data Analysis Genes2FANs- Tools to Build Networks from List of Genes Sets2Networks- Tool to Build Networks from Gene-Set Libraries Expression2Kinases- Tool to Infer Cell Signaling Pathways from Sets of Differentially Expressed Genes Lists2Networks- Web-Based Platform for Performing Gene Set Enrichment Analyses Genes2Networks- Tool to Build Protein-Protein Interaction Networks from Lists of Genes ESCAPE- Database that Collects, Organizes and Visualizes High-Content Data from Embryonic Stem Cell Research