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  • 1
    Publication Date: 2014-12-24
    Description: Models derived from human pluripotent stem cells that accurately recapitulate neural development in vitro and allow for the generation of specific neuronal subtypes are of major interest to the stem cell and biomedical community. Notch signalling, particularly through the Notch effector HES5, is a major pathway critical for the onset and maintenance of neural progenitor cells in the embryonic and adult nervous system. Here we report the transcriptional and epigenomic analysis of six consecutive neural progenitor cell stages derived from a HES5::eGFP reporter human embryonic stem cell line. Using this system, we aimed to model cell-fate decisions including specification, expansion and patterning during the ontogeny of cortical neural stem and progenitor cells. In order to dissect regulatory mechanisms that orchestrate the stage-specific differentiation process, we developed a computational framework to infer key regulators of each cell-state transition based on the progressive remodelling of the epigenetic landscape and then validated these through a pooled short hairpin RNA screen. We were also able to refine our previous observations on epigenetic priming at transcription factor binding sites and suggest here that they are mediated by combinations of core and stage-specific factors. Taken together, we demonstrate the utility of our system and outline a general framework, not limited to the context of the neural lineage, to dissect regulatory circuits of differentiation.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4336237/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4336237/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Ziller, Michael J -- Edri, Reuven -- Yaffe, Yakey -- Donaghey, Julie -- Pop, Ramona -- Mallard, William -- Issner, Robbyn -- Gifford, Casey A -- Goren, Alon -- Xing, Jeffrey -- Gu, Hongcang -- Cacchiarelli, Davide -- Tsankov, Alexander M -- Epstein, Charles -- Rinn, John L -- Mikkelsen, Tarjei S -- Kohlbacher, Oliver -- Gnirke, Andreas -- Bernstein, Bradley E -- Elkabetz, Yechiel -- Meissner, Alexander -- F32 DK095537/DK/NIDDK NIH HHS/ -- HG006911/HG/NHGRI NIH HHS/ -- P01 GM099117/GM/NIGMS NIH HHS/ -- P01GM099117/GM/NIGMS NIH HHS/ -- U01 ES017155/ES/NIEHS NIH HHS/ -- U01ES017155/ES/NIEHS NIH HHS/ -- U54 HG006991/HG/NHGRI NIH HHS/ -- England -- Nature. 2015 Feb 19;518(7539):355-9. doi: 10.1038/nature13990. Epub 2014 Dec 24.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉1] Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA [2] Harvard Stem Cell Institute, Cambridge, Massachusetts 02138, USA [3] Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts 02138, USA. ; Department of Cell and Developmental Biology, Sackler School of Medicine, Tel Aviv University, Ramat Aviv 6997801, Israel. ; 1] Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA [2] Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts 02138, USA. ; Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA. ; 1] Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA [2] Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA [3] Center for Systems Biology and Center for Cancer Research, Massachusetts General Hospital, Boston, Massachusetts 02114, USA. ; Applied Bioinformatics, Center for Bioinformatics and Quantitative Biology Center, University of Tubingen, Tubingen 72076, Germany.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25533951" target="_blank"〉PubMed〈/a〉
    Keywords: Binding Sites ; Cell Differentiation/*genetics ; Cell Lineage/genetics ; Embryonic Stem Cells/*cytology/metabolism ; Epigenesis, Genetic/*genetics ; Epigenomics/*methods ; Humans ; Neural Stem Cells/*cytology/*metabolism ; RNA, Small Interfering/analysis/genetics ; Reproducibility of Results ; Transcription Factors/metabolism ; Transcription, Genetic/genetics
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
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  • 2
    Publication Date: 2013-08-09
    Description: DNA methylation is a defining feature of mammalian cellular identity and is essential for normal development. Most cell types, except germ cells and pre-implantation embryos, display relatively stable DNA methylation patterns, with 70-80% of all CpGs being methylated. Despite recent advances, we still have a limited understanding of when, where and how many CpGs participate in genomic regulation. Here we report the in-depth analysis of 42 whole-genome bisulphite sequencing data sets across 30 diverse human cell and tissue types. We observe dynamic regulation for only 21.8% of autosomal CpGs within a normal developmental context, most of which are distal to transcription start sites. These dynamic CpGs co-localize with gene regulatory elements, particularly enhancers and transcription-factor-binding sites, which allow identification of key lineage-specific regulators. In addition, differentially methylated regions (DMRs) often contain single nucleotide polymorphisms associated with cell-type-related diseases as determined by genome-wide association studies. The results also highlight the general inefficiency of whole-genome bisulphite sequencing, as 70-80% of the sequencing reads across these data sets provided little or no relevant information about CpG methylation. To demonstrate further the utility of our DMR set, we use it to classify unknown samples and identify representative signature regions that recapitulate major DNA methylation dynamics. In summary, although in theory every CpG can change its methylation state, our results suggest that only a fraction does so as part of coordinated regulatory programs. Therefore, our selected DMRs can serve as a starting point to guide new, more effective reduced representation approaches to capture the most informative fraction of CpGs, as well as further pinpoint putative regulatory elements.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3821869/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3821869/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Ziller, Michael J -- Gu, Hongcang -- Muller, Fabian -- Donaghey, Julie -- Tsai, Linus T-Y -- Kohlbacher, Oliver -- De Jager, Philip L -- Rosen, Evan D -- Bennett, David A -- Bernstein, Bradley E -- Gnirke, Andreas -- Meissner, Alexander -- ES017690/ES/NIEHS NIH HHS/ -- P01 GM099117/GM/NIGMS NIH HHS/ -- P01GM099117/GM/NIGMS NIH HHS/ -- P30AG10161/AG/NIA NIH HHS/ -- R01 AG017917/AG/NIA NIH HHS/ -- R01AG15819/AG/NIA NIH HHS/ -- R01AG17917/AG/NIA NIH HHS/ -- R01AG36042/AG/NIA NIH HHS/ -- U01 ES017155/ES/NIEHS NIH HHS/ -- U01ES017155/ES/NIEHS NIH HHS/ -- England -- Nature. 2013 Aug 22;500(7463):477-81. doi: 10.1038/nature12433. Epub 2013 Aug 7.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/23925113" target="_blank"〉PubMed〈/a〉
    Keywords: Binding Sites ; CpG Islands/genetics ; *DNA Methylation ; Enhancer Elements, Genetic/genetics ; Genome, Human/*genetics ; Genome-Wide Association Study ; Humans ; Organ Specificity ; Polymorphism, Single Nucleotide/genetics ; Sequence Analysis, DNA ; Sulfites/metabolism ; Transcription Factors/metabolism
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
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  • 3
    Publication Date: 2004-08-23
    Print ISSN: 0027-8424
    Electronic ISSN: 1091-6490
    Topics: Biology , Medicine , Natural Sciences in General
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  • 4
    Publication Date: 2012-04-24
    Print ISSN: 0027-8424
    Electronic ISSN: 1091-6490
    Topics: Biology , Medicine , Natural Sciences in General
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  • 5
    Publication Date: 2004-09-02
    Print ISSN: 0031-9007
    Electronic ISSN: 1079-7114
    Topics: Physics
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  • 6
  • 7
    Publication Date: 2016-06-16
    Description: : In medical research, it is crucial to understand the functional consequences of genetic alterations, for example, non-synonymous single nucleotide variants (nsSNVs). NsSNVs are known to be causative for several human diseases. However, the genetic basis of complex disorders such as diabetes or cancer comprises multiple factors. Methods to analyze putative synergetic effects of multiple such factors, however, are limited. Here, we concentrate on nsSNVs and present BALL-SNPgp, a tool for structural and functional characterization of nsSNVs, which is aimed to improve pathogenicity assessment in computational diagnostics. Based on annotated SNV data, BALL-SNPgp creates a three-dimensional visualization of the encoded protein, collects available information from different resources concerning disease relevance and other functional annotations, performs cluster analysis, predicts putative binding pockets and provides data on known interaction sites. Availability and implementation: BALL-SNPgp is based on the comprehensive C ++ framework Biochemical Algorithms Library (BALL) and its visualization front-end BALLView. Our tool is available at www.ccb.uni-saarland.de/BALL-SNPgp . Contact: ballsnp@milaman.cs.uni-saarland.de
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 8
    Publication Date: 2015-06-27
    Description: : EpiToolKit is a virtual workbench for immunological questions with a focus on vaccine design. It offers an array of immunoinformatics tools covering MHC genotyping, epitope and neo-epitope prediction, epitope selection for vaccine design, and epitope assembly. In its recently re-implemented version 2.0, EpiToolKit provides a range of new functionality and for the first time allows combining tools into complex workflows. For inexperienced users it offers simplified interfaces to guide the users through the analysis of complex immunological data sets. Availability and implementation: http://www.epitoolkit.de Contact: schubert@informatik.uni-tuebingen.de Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 9
    Publication Date: 2012-05-02
    Description: Small-molecule inhibitors of 14-3-3 protein–protein interactions could serve as valuable chemical biology tools and starting points for drug development. The article by Zhao et al. (1) described FOBISIN101 (1) (Fig. 1A), a pyridoxal-phosphate (PLP) derivative that inhibits 14-3-3 protein–protein interactions. The authors provided a crystal structure of 14-3-3ζ with PLP covalently bound to K120 [Protein Data Bank (PDB) ID code 3RDH] lacking the p-amino-benzoate moiety. As a mechanism, they propose X-ray–induced cleavage of the N=N bond. They suggest this mechanism could serve as a radiation-triggered anticancer prodrug concept.pnas;109/18/E1051/FIG01F1fig01Fig. 1.(A) PLP derivatives 1 (FOBISIN101) and 2. The PLP part is highlighted...
    Keywords: Letters
    Print ISSN: 0027-8424
    Electronic ISSN: 1091-6490
    Topics: Biology , Medicine , Natural Sciences in General
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  • 10
    Publication Date: 2014-11-26
    Description: Motivation: The human leukocyte antigen (HLA) gene cluster plays a crucial role in adaptive immunity and is thus relevant in many biomedical applications. While next-generation sequencing data are often available for a patient, deducing the HLA genotype is difficult because of substantial sequence similarity within the cluster and exceptionally high variability of the loci. Established approaches, therefore, rely on specific HLA enrichment and sequencing techniques, coming at an additional cost and extra turnaround time. Result: We present OptiType, a novel HLA genotyping algorithm based on integer linear programming, capable of producing accurate predictions from NGS data not specifically enriched for the HLA cluster. We also present a comprehensive benchmark dataset consisting of RNA, exome and whole-genome sequencing data. OptiType significantly outperformed previously published in silico approaches with an overall accuracy of 97% enabling its use in a broad range of applications. Contact: szolek@informatik.uni-tuebingen.de Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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