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Institution

Morgridge Institute for Research

NonprofitMadison, Wisconsin, United States
About: Morgridge Institute for Research is a nonprofit organization based out in Madison, Wisconsin, United States. It is known for research contribution in the topics: Induced pluripotent stem cell & Stem cell. The organization has 320 authors who have published 680 publications receiving 44815 citations.


Papers
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Journal ArticleDOI
Anshul Kundaje1, Wouter Meuleman2, Wouter Meuleman1, Jason Ernst3, Misha Bilenky4, Angela Yen2, Angela Yen1, Alireza Heravi-Moussavi4, Pouya Kheradpour2, Pouya Kheradpour1, Zhizhuo Zhang1, Zhizhuo Zhang2, Jianrong Wang1, Jianrong Wang2, Michael J. Ziller2, Viren Amin5, John W. Whitaker, Matthew D. Schultz6, Lucas D. Ward2, Lucas D. Ward1, Abhishek Sarkar1, Abhishek Sarkar2, Gerald Quon2, Gerald Quon1, Richard Sandstrom7, Matthew L. Eaton2, Matthew L. Eaton1, Yi-Chieh Wu1, Yi-Chieh Wu2, Andreas R. Pfenning2, Andreas R. Pfenning1, Xinchen Wang2, Xinchen Wang1, Melina Claussnitzer1, Melina Claussnitzer2, Yaping Liu2, Yaping Liu1, Cristian Coarfa5, R. Alan Harris5, Noam Shoresh2, Charles B. Epstein2, Elizabeta Gjoneska1, Elizabeta Gjoneska2, Danny Leung8, Wei Xie8, R. David Hawkins8, Ryan Lister6, Chibo Hong9, Philippe Gascard9, Andrew J. Mungall4, Richard A. Moore4, Eric Chuah4, Angela Tam4, Theresa K. Canfield7, R. Scott Hansen7, Rajinder Kaul7, Peter J. Sabo7, Mukul S. Bansal2, Mukul S. Bansal10, Mukul S. Bansal1, Annaick Carles4, Jesse R. Dixon8, Kai How Farh2, Soheil Feizi2, Soheil Feizi1, Rosa Karlic11, Ah Ram Kim1, Ah Ram Kim2, Ashwinikumar Kulkarni12, Daofeng Li13, Rebecca F. Lowdon13, Ginell Elliott13, Tim R. Mercer14, Shane Neph7, Vitor Onuchic5, Paz Polak15, Paz Polak2, Nisha Rajagopal8, Pradipta R. Ray12, Richard C Sallari1, Richard C Sallari2, Kyle Siebenthall7, Nicholas A Sinnott-Armstrong1, Nicholas A Sinnott-Armstrong2, Michael Stevens13, Robert E. Thurman7, Jie Wu16, Bo Zhang13, Xin Zhou13, Arthur E. Beaudet5, Laurie A. Boyer1, Philip L. De Jager15, Philip L. De Jager2, Peggy J. Farnham17, Susan J. Fisher9, David Haussler18, Steven J.M. Jones4, Steven J.M. Jones19, Wei Li5, Marco A. Marra4, Michael T. McManus9, Shamil R. Sunyaev2, Shamil R. Sunyaev15, James A. Thomson20, Thea D. Tlsty9, Li-Huei Tsai1, Li-Huei Tsai2, Wei Wang, Robert A. Waterland5, Michael Q. Zhang21, Lisa Helbling Chadwick22, Bradley E. Bernstein6, Bradley E. Bernstein2, Bradley E. Bernstein15, Joseph F. Costello9, Joseph R. Ecker11, Martin Hirst4, Alexander Meissner2, Aleksandar Milosavljevic5, Bing Ren8, John A. Stamatoyannopoulos7, Ting Wang13, Manolis Kellis1, Manolis Kellis2 
19 Feb 2015-Nature
TL;DR: It is shown that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease.
Abstract: The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

5,037 citations

Journal ArticleDOI
19 Nov 2009-Nature
TL;DR: The first genome-wide, single-base-resolution maps of methylated cytosines in a mammalian genome, from both human embryonic stem cells and fetal fibroblasts, along with comparative analysis of messenger RNA and small RNA components of the transcriptome, several histone modifications, and sites of DNA-protein interaction for several key regulatory factors were presented in this article.
Abstract: DNA cytosine methylation is a central epigenetic modification that has essential roles in cellular processes including genome regulation, development and disease. Here we present the first genome-wide, single-base-resolution maps of methylated cytosines in a mammalian genome, from both human embryonic stem cells and fetal fibroblasts, along with comparative analysis of messenger RNA and small RNA components of the transcriptome, several histone modifications, and sites of DNA-protein interaction for several key regulatory factors. Widespread differences were identified in the composition and patterning of cytosine methylation between the two genomes. Nearly one-quarter of all methylation identified in embryonic stem cells was in a non-CG context, suggesting that embryonic stem cells may use different methylation mechanisms to affect gene regulation. Methylation in non-CG contexts showed enrichment in gene bodies and depletion in protein binding sites and enhancers. Non-CG methylation disappeared upon induced differentiation of the embryonic stem cells, and was restored in induced pluripotent stem cells. We identified hundreds of differentially methylated regions proximal to genes involved in pluripotency and differentiation, and widespread reduced methylation levels in fibroblasts associated with lower transcriptional activity. These reference epigenomes provide a foundation for future studies exploring this key epigenetic modification in human disease and development.

4,266 citations

Journal ArticleDOI
TL;DR: ImageJ2 as mentioned in this paper is the next generation of ImageJ, which provides a host of new functionality and separates concerns, fully decoupling the data model from the user interface.
Abstract: ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software’s ability to handle the requirements of modern science. We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called “ImageJ2” in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ’s development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites.

4,093 citations

Journal ArticleDOI
08 May 2009-Science
TL;DR: Results demonstrate that reprograming human somatic cells does not require genomic integration or the continued presence of exogenous reprogramming factors and removes one obstacle to the clinical application of human iPS cells.
Abstract: Reprogramming differentiated human cells to induced pluripotent stem (iPS) cells has applications in basic biology, drug development, and transplantation. Human iPS cell derivation previously required vectors that integrate into the genome, which can create mutations and limit the utility of the cells in both research and clinical applications. We describe the derivation of human iPS cells with the use of nonintegrating episomal vectors. After removal of the episome, iPS cells completely free of vector and transgene sequences are derived that are similar to human embryonic stem (ES) cells in proliferative and developmental potential. These results demonstrate that reprogramming human somatic cells does not require genomic integration or the continued presence of exogenous reprogramming factors and removes one obstacle to the clinical application of human iPS cells.

2,425 citations

Journal ArticleDOI
15 Feb 2017-Methods
TL;DR: TrackMate is an extensible platform where developers can easily write their own detection, particle linking, visualization or analysis algorithms within the TrackMate environment and is validated for quantitative lifetime analysis of clathrin-mediated endocytosis in plant cells.

2,356 citations


Authors

Showing all 330 results

NameH-indexPapersCitations
James A. Thomson10438793683
Deane F. Mosher8335124491
Paul Ahlquist7819317106
John M. Denu7821422851
Miron Livny7728229898
Joshua J. Coon7236622907
Qiang Cui7129220655
Dietram A. Scheufele6521520722
Lei Wang6218515947
Kevin W. Eliceiri5930090511
Thomas R. Mackie5920913165
Michael N. Gould491749597
Marisa S. Otegui461156666
Ming Yuan4617215913
Dominique Brossard451778074
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20231
202216
2021112
2020128
2019112
201878