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  1. Article ; Online: Systematic genetic analysis of the MHC region reveals mechanistic underpinnings of HLA type associations with disease

    Matteo D'Antonio / Joaquin Reyna / David Jakubosky / Margaret KR Donovan / Marc-Jan Bonder / Hiroko Matsui / Oliver Stegle / Naoki Nariai / Agnieszka D'Antonio-Chronowska / Kelly A Frazer

    eLife, Vol

    2019  Volume 8

    Abstract: The MHC region is highly associated with autoimmune and infectious diseases. Here we conduct an in-depth interrogation of associations between genetic variation, gene expression and disease. We create a comprehensive map of regulatory variation in the ... ...

    Abstract The MHC region is highly associated with autoimmune and infectious diseases. Here we conduct an in-depth interrogation of associations between genetic variation, gene expression and disease. We create a comprehensive map of regulatory variation in the MHC region using WGS from 419 individuals to call eight-digit HLA types and RNA-seq data from matched iPSCs. Building on this regulatory map, we explored GWAS signals for 4083 traits, detecting colocalization for 180 disease loci with eQTLs. We show that eQTL analyses taking HLA type haplotypes into account have substantially greater power compared with only using single variants. We examined the association between the 8.1 ancestral haplotype and delayed colonization in Cystic Fibrosis, postulating that downregulation of RNF5 expression is the likely causal mechanism. Our study provides insights into the genetic architecture of the MHC region and pinpoints disease associations that are due to differential expression of HLA genes and non-HLA genes.
    Keywords major histocompatibility complex ; eQTLs ; gene expression ; HLA types ; Medicine ; R ; Science ; Q ; Biology (General) ; QH301-705.5
    Subject code 570
    Language English
    Publishing date 2019-11-01T00:00:00Z
    Publisher eLife Sciences Publications Ltd
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Probabilistic protein function prediction from heterogeneous genome-wide data.

    Naoki Nariai / Eric D Kolaczyk / Simon Kasif

    PLoS ONE, Vol 2, Iss 3, p e

    2007  Volume 337

    Abstract: Dramatic improvements in high throughput sequencing technologies have led to a staggering growth in the number of predicted genes. However, a large fraction of these newly discovered genes do not have a functional assignment. Fortunately, a variety of ... ...

    Abstract Dramatic improvements in high throughput sequencing technologies have led to a staggering growth in the number of predicted genes. However, a large fraction of these newly discovered genes do not have a functional assignment. Fortunately, a variety of novel high-throughput genome-wide functional screening technologies provide important clues that shed light on gene function. The integration of heterogeneous data to predict protein function has been shown to improve the accuracy of automated gene annotation systems. In this paper, we propose and evaluate a probabilistic approach for protein function prediction that integrates protein-protein interaction (PPI) data, gene expression data, protein motif information, mutant phenotype data, and protein localization data. First, functional linkage graphs are constructed from PPI data and gene expression data, in which an edge between nodes (proteins) represents evidence for functional similarity. The assumption here is that graph neighbors are more likely to share protein function, compared to proteins that are not neighbors. The functional linkage graph model is then used in concert with protein domain, mutant phenotype and protein localization data to produce a functional prediction. Our method is applied to the functional prediction of Saccharomyces cerevisiae genes, using Gene Ontology (GO) terms as the basis of our annotation. In a cross validation study we show that the integrated model increases recall by 18%, compared to using PPI data alone at the 50% precision. We also show that the integrated predictor is significantly better than each individual predictor. However, the observed improvement vs. PPI depends on both the new source of data and the functional category to be predicted. Surprisingly, in some contexts integration hurts overall prediction accuracy. Lastly, we provide a comprehensive assignment of putative GO terms to 463 proteins that currently have no assigned function.
    Keywords Medicine ; R ; Science ; Q
    Subject code 612
    Language English
    Publishing date 2007-03-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Pancreatic islet chromatin accessibility and conformation reveals distal enhancer networks of type 2 diabetes risk

    William W. Greenwald / Joshua Chiou / Jian Yan / Yunjiang Qiu / Ning Dai / Allen Wang / Naoki Nariai / Anthony Aylward / Jee Yun Han / Nikita Kadakia / Laura Regue / Mei-Lin Okino / Frauke Drees / Dana Kramer / Nicholas Vinckier / Liliana Minichiello / David Gorkin / Joseph Avruch / Kelly A. Frazer /
    Maike Sander / Bing Ren / Kyle J. Gaulton

    Nature Communications, Vol 10, Iss 1, Pp 1-

    2019  Volume 12

    Abstract: Risk loci for type 2 diabetes (T2D) reside in pancreatic islet enhancers. Here, the authors generate high-resolution maps of islet chromatin conformation using Hi-C which they combine with ATAC-seq and ChIP-seq data to annotate candidate target genes of ... ...

    Abstract Risk loci for type 2 diabetes (T2D) reside in pancreatic islet enhancers. Here, the authors generate high-resolution maps of islet chromatin conformation using Hi-C which they combine with ATAC-seq and ChIP-seq data to annotate candidate target genes of enhancers and validate IGF2BP2 activity in mouse islets.
    Keywords Science ; Q
    Language English
    Publishing date 2019-05-01T00:00:00Z
    Publisher Nature Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Pancreatic islet chromatin accessibility and conformation reveals distal enhancer networks of type 2 diabetes risk

    William W. Greenwald / Joshua Chiou / Jian Yan / Yunjiang Qiu / Ning Dai / Allen Wang / Naoki Nariai / Anthony Aylward / Jee Yun Han / Nikita Kadakia / Laura Regue / Mei-Lin Okino / Frauke Drees / Dana Kramer / Nicholas Vinckier / Liliana Minichiello / David Gorkin / Joseph Avruch / Kelly A. Frazer /
    Maike Sander / Bing Ren / Kyle J. Gaulton

    Nature Communications, Vol 10, Iss 1, Pp 1-

    2019  Volume 12

    Abstract: Risk loci for type 2 diabetes (T2D) reside in pancreatic islet enhancers. Here, the authors generate high-resolution maps of islet chromatin conformation using Hi-C which they combine with ATAC-seq and ChIP-seq data to annotate candidate target genes of ... ...

    Abstract Risk loci for type 2 diabetes (T2D) reside in pancreatic islet enhancers. Here, the authors generate high-resolution maps of islet chromatin conformation using Hi-C which they combine with ATAC-seq and ChIP-seq data to annotate candidate target genes of enhancers and validate IGF2BP2 activity in mouse islets.
    Keywords Science ; Q
    Language English
    Publishing date 2019-05-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: A crowdsourced set of curated structural variants for the human genome.

    Lesley M Chapman / Noah Spies / Patrick Pai / Chun Shen Lim / Andrew Carroll / Giuseppe Narzisi / Christopher M Watson / Christos Proukakis / Wayne E Clarke / Naoki Nariai / Eric Dawson / Garan Jones / Daniel Blankenberg / Christian Brueffer / Chunlin Xiao / Sree Rohit Raj Kolora / Noah Alexander / Paul Wolujewicz / Azza E Ahmed /
    Graeme Smith / Saadlee Shehreen / Aaron M Wenger / Marc Salit / Justin M Zook

    PLoS Computational Biology, Vol 16, Iss 6, p e

    2020  Volume 1007933

    Abstract: A high quality benchmark for small variants encompassing 88 to 90% of the reference genome has been developed for seven Genome in a Bottle (GIAB) reference samples. However a reliable benchmark for large indels and structural variants (SVs) is more ... ...

    Abstract A high quality benchmark for small variants encompassing 88 to 90% of the reference genome has been developed for seven Genome in a Bottle (GIAB) reference samples. However a reliable benchmark for large indels and structural variants (SVs) is more challenging. In this study, we manually curated 1235 SVs, which can ultimately be used to evaluate SV callers or train machine learning models. We developed a crowdsourcing app-SVCurator-to help GIAB curators manually review large indels and SVs within the human genome, and report their genotype and size accuracy. SVCurator displays images from short, long, and linked read sequencing data from the GIAB Ashkenazi Jewish Trio son [NIST RM 8391/HG002]. We asked curators to assign labels describing SV type (deletion or insertion), size accuracy, and genotype for 1235 putative insertions and deletions sampled from different size bins between 20 and 892,149 bp. 'Expert' curators were 93% concordant with each other, and 37 of the 61 curators had at least 78% concordance with a set of 'expert' curators. The curators were least concordant for complex SVs and SVs that had inaccurate breakpoints or size predictions. After filtering events with low concordance among curators, we produced high confidence labels for 935 events. The SVCurator crowdsourced labels were 94.5% concordant with the heuristic-based draft benchmark SV callset from GIAB. We found that curators can successfully evaluate putative SVs when given evidence from multiple sequencing technologies.
    Keywords Biology (General) ; QH301-705.5
    Subject code 006
    Language English
    Publishing date 2020-06-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  6. Article ; Online: Profiling of microRNA in human and mouse ES and iPS cells reveals overlapping but distinct microRNA expression patterns.

    Siti Razila Abdul Razak / Kazuko Ueno / Naoya Takayama / Naoki Nariai / Masao Nagasaki / Rika Saito / Hideto Koso / Chen-Yi Lai / Miyako Murakami / Koichiro Tsuji / Tatsuo Michiue / Hiromitsu Nakauchi / Makoto Otsu / Sumiko Watanabe

    PLoS ONE, Vol 8, Iss 9, p e

    2013  Volume 73532

    Abstract: Using quantitative PCR-based miRNA arrays, we comprehensively analyzed the expression profiles of miRNAs in human and mouse embryonic stem (ES), induced pluripotent stem (iPS), and somatic cells. Immature pluripotent cells were purified using SSEA-1 or ... ...

    Abstract Using quantitative PCR-based miRNA arrays, we comprehensively analyzed the expression profiles of miRNAs in human and mouse embryonic stem (ES), induced pluripotent stem (iPS), and somatic cells. Immature pluripotent cells were purified using SSEA-1 or SSEA-4 and were used for miRNA profiling. Hierarchical clustering and consensus clustering by nonnegative matrix factorization showed two major clusters, human ES/iPS cells and other cell groups, as previously reported. Principal components analysis (PCA) to identify miRNAs that segregate in these two groups identified miR-187, 299-3p, 499-5p, 628-5p, and 888 as new miRNAs that specifically characterize human ES/iPS cells. Detailed direct comparisons of miRNA expression levels in human ES and iPS cells showed that several miRNAs included in the chromosome 19 miRNA cluster were more strongly expressed in iPS cells than in ES cells. Similar analysis was conducted with mouse ES/iPS cells and somatic cells, and several miRNAs that had not been reported to be expressed in mouse ES/iPS cells were suggested to be ES/iPS cell-specific miRNAs by PCA. Comparison of the average expression levels of miRNAs in ES/iPS cells in humans and mice showed quite similar expression patterns of human/mouse miRNAs. However, several mouse- or human-specific miRNAs are ranked as high expressers. Time course tracing of miRNA levels during embryoid body formation revealed drastic and different patterns of changes in their levels. In summary, our miRNA expression profiling encompassing human and mouse ES and iPS cells gave various perspectives in understanding the miRNA core regulatory networks regulating pluripotent cells characteristics.
    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2013-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: iPSCORE

    Athanasia D. Panopoulos / Matteo D'Antonio / Paola Benaglio / Roy Williams / Sherin I. Hashem / Bernhard M. Schuldt / Christopher DeBoever / Angelo D. Arias / Melvin Garcia / Bradley C. Nelson / Olivier Harismendy / David A. Jakubosky / Margaret K.R. Donovan / William W. Greenwald / KathyJean Farnam / Megan Cook / Victor Borja / Carl A. Miller / Jonathan D. Grinstein /
    Frauke Drees / Jonathan Okubo / Kenneth E. Diffenderfer / Yuriko Hishida / Veronica Modesto / Carl T. Dargitz / Rachel Feiring / Chang Zhao / Aitor Aguirre / Thomas J. McGarry / Hiroko Matsui / He Li / Joaquin Reyna / Fangwen Rao / Daniel T. O'Connor / Gene W. Yeo / Sylvia M. Evans / Neil C. Chi / Kristen Jepsen / Naoki Nariai / Franz-Josef Müller / Lawrence S.B. Goldstein / Juan Carlos Izpisua Belmonte / Eric Adler / Jeanne F. Loring / W. Travis Berggren / Agnieszka D'Antonio-Chronowska / Erin N. Smith / Kelly A. Frazer

    Stem Cell Reports, Vol 8, Iss 4, Pp 1086-

    A Resource of 222 iPSC Lines Enabling Functional Characterization of Genetic Variation across a Variety of Cell Types

    2017  Volume 1100

    Abstract: Summary: Large-scale collections of induced pluripotent stem cells (iPSCs) could serve as powerful model systems for examining how genetic variation affects biology and disease. Here we describe the iPSCORE resource: a collection of systematically ... ...

    Abstract Summary: Large-scale collections of induced pluripotent stem cells (iPSCs) could serve as powerful model systems for examining how genetic variation affects biology and disease. Here we describe the iPSCORE resource: a collection of systematically derived and characterized iPSC lines from 222 ethnically diverse individuals that allows for both familial and association-based genetic studies. iPSCORE lines are pluripotent with high genomic integrity (no or low numbers of somatic copy-number variants) as determined using high-throughput RNA-sequencing and genotyping arrays, respectively. Using iPSCs from a family of individuals, we show that iPSC-derived cardiomyocytes demonstrate gene expression patterns that cluster by genetic background, and can be used to examine variants associated with physiological and disease phenotypes. The iPSCORE collection contains representative individuals for risk and non-risk alleles for 95% of SNPs associated with human phenotypes through genome-wide association studies. Our study demonstrates the utility of iPSCORE for examining how genetic variants influence molecular and physiological traits in iPSCs and derived cell lines. : Working as part of the NHLBI NextGen consortium, Panopoulos and colleagues report the derivation and characterization of 222 publicly available iPSCs from ethnically diverse individuals with corresponding genomic data including SNP arrays, RNA-seq, and whole-genome sequencing. This collection provides a powerful resource to investigate the function of genetic variants. Keywords: iPSCORE, iPSC, GWAS, molecular traits, physiological traits, cardiac disease, NHLBI Next Gen, LQT2, KCNH2, iPSC-derived cardiomyocytes
    Keywords Medicine (General) ; R5-920 ; Biology (General) ; QH301-705.5
    Subject code 616
    Language English
    Publishing date 2017-04-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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