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  • 1
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    The evolutionary landscape of alternative splicing in vertebrate species (2012)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 2012-12-22
    Description: How species with similar repertoires of protein-coding genes differ so markedly at the phenotypic level is poorly understood. By comparing organ transcriptomes from vertebrate species spanning ~350 million years of evolution, we observed significant differences in alternative splicing complexity between vertebrate lineages, with the highest complexity in primates. Within 6 million years, the splicing profiles of physiologically equivalent organs diverged such that they are more strongly related to the identity of a species than they are to organ type. Most vertebrate species-specific splicing patterns are cis-directed. However, a subset of pronounced splicing changes are predicted to remodel protein interactions involving trans-acting regulators. These events likely further contributed to the diversification of splicing and other transcriptomic changes that underlie phenotypic differences among vertebrate species.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Barbosa-Morais, Nuno L -- Irimia, Manuel -- Pan, Qun -- Xiong, Hui Y -- Gueroussov, Serge -- Lee, Leo J -- Slobodeniuc, Valentina -- Kutter, Claudia -- Watt, Stephen -- Colak, Recep -- Kim, TaeHyung -- Misquitta-Ali, Christine M -- Wilson, Michael D -- Kim, Philip M -- Odom, Duncan T -- Frey, Brendan J -- Blencowe, Benjamin J -- 15603/Cancer Research UK/United Kingdom -- A15603/Cancer Research UK/United Kingdom -- Canadian Institutes of Health Research/Canada -- New York, N.Y. -- Science. 2012 Dec 21;338(6114):1587-93. doi: 10.1126/science.1230612.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Banting and Best Department of Medical Research, Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/23258890" target="_blank"〉PubMed〈/a〉
    Keywords: *Alternative Splicing ; Animals ; Biological Evolution ; Chickens/genetics ; *Evolution, Molecular ; Exons ; Introns ; Lizards/genetics ; Mice/genetics ; Mice, Inbred C57BL/genetics ; Opossums/genetics ; Phenotype ; Platypus/genetics ; Primates/genetics ; RNA Splice Sites ; Regulatory Sequences, Ribonucleic Acid ; Species Specificity ; *Transcriptome ; Vertebrates/*genetics ; Xenopus/genetics
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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  • 2
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    RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease (2014)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 2014-12-20
    Description: To facilitate precision medicine and whole-genome annotation, we developed a machine-learning technique that scores how strongly genetic variants affect RNA splicing, whose alteration contributes to many diseases. Analysis of more than 650,000 intronic and exonic variants revealed widespread patterns of mutation-driven aberrant splicing. Intronic disease mutations that are more than 30 nucleotides from any splice site alter splicing nine times as often as common variants, and missense exonic disease mutations that have the least impact on protein function are five times as likely as others to alter splicing. We detected tens of thousands of disease-causing mutations, including those involved in cancers and spinal muscular atrophy. Examination of intronic and exonic variants found using whole-genome sequencing of individuals with autism revealed misspliced genes with neurodevelopmental phenotypes. Our approach provides evidence for causal variants and should enable new discoveries in precision medicine.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4362528/" 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/PMC4362528/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Xiong, Hui Y -- Alipanahi, Babak -- Lee, Leo J -- Bretschneider, Hannes -- Merico, Daniele -- Yuen, Ryan K C -- Hua, Yimin -- Gueroussov, Serge -- Najafabadi, Hamed S -- Hughes, Timothy R -- Morris, Quaid -- Barash, Yoseph -- Krainer, Adrian R -- Jojic, Nebojsa -- Scherer, Stephen W -- Blencowe, Benjamin J -- Frey, Brendan J -- P30 CA045508/CA/NCI NIH HHS/ -- R37 GM042699/GM/NIGMS NIH HHS/ -- R37-GM42699A/GM/NIGMS NIH HHS/ -- Canadian Institutes of Health Research/Canada -- New York, N.Y. -- Science. 2015 Jan 9;347(6218):1254806. doi: 10.1126/science.1254806. Epub 2014 Dec 18.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Canada. Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada. Program on Genetic Networks and Program on Neural Computation & Adaptive Perception, Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, Canada. ; Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Canada. Program on Genetic Networks and Program on Neural Computation & Adaptive Perception, Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, Canada. Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3G4, Canada. ; McLaughlin Centre, University of Toronto, Toronto, Ontario M5G 0A4, Canada. Centre for Applied Genomics, Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada. Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada. ; Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA. ; Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada. Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada. ; Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada. Program on Genetic Networks and Program on Neural Computation & Adaptive Perception, Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, Canada. Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada. ; Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Canada. Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada. Program on Genetic Networks and Program on Neural Computation & Adaptive Perception, Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, Canada. Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada. ; Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Canada. Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada. School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA. ; eScience Group, Microsoft Research, Redmond, WA 98052, USA. ; Program on Genetic Networks and Program on Neural Computation & Adaptive Perception, Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, Canada. McLaughlin Centre, University of Toronto, Toronto, Ontario M5G 0A4, Canada. Centre for Applied Genomics, Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada. Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada. ; Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada. McLaughlin Centre, University of Toronto, Toronto, Ontario M5G 0A4, Canada. Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada. ; Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Canada. Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada. Program on Genetic Networks and Program on Neural Computation & Adaptive Perception, Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, Canada. Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3G4, Canada. McLaughlin Centre, University of Toronto, Toronto, Ontario M5G 0A4, Canada. Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada. eScience Group, Microsoft Research, Redmond, WA 98052, USA. frey@psi.toronto.edu.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25525159" target="_blank"〉PubMed〈/a〉
    Keywords: Adaptor Proteins, Signal Transducing/genetics ; *Artificial Intelligence ; Child Development Disorders, Pervasive/*genetics ; Colorectal Neoplasms, Hereditary Nonpolyposis/*genetics ; Computer Simulation ; DNA/genetics ; Exons/genetics ; Genetic Code ; Genetic Markers ; Genetic Variation ; Genome-Wide Association Study/*methods ; Humans ; Introns/genetics ; Models, Genetic ; Molecular Sequence Annotation/*methods ; Muscular Atrophy, Spinal/*genetics ; Mutation, Missense ; Nuclear Proteins/genetics ; Polymorphism, Single Nucleotide ; Quantitative Trait Loci ; RNA Splice Sites/genetics ; RNA Splicing/*genetics ; RNA-Binding Proteins/genetics
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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  • 3
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    The D-dopachrome tautomerase (DDT) gene product is a cytokine and functional homolog of macrophage migration inhibitory factor (MIF) [Immunology] (2011)
    National Academy of Sciences
    Details
    Publication Date: 2011-08-24
    Description: Macrophage migration inhibitory factor (MIF) is a pivotal regulator of the immune response. Neutralization or genetic deletion of MIF does not completely abrogate activation responses, however, and deletion of the MIF receptor, CD74, produces a more pronounced phenotype than MIF deficiency. We hypothesized that these observations may be explained by a second MIF-like ligand, and we considered a probable candidate to be the protein encoded by the homologous, D-dopachrome tautomerase (D-DT) gene. We show that recombinant D-DT protein binds CD74 with high affinity, leading to activation of ERK1/2 MAP kinase and downstream proinflammatory pathways. Circulating D-DT levels correlate with disease severity in sepsis or malignancy, and the specific immunoneutralization of D-DT protects mice from lethal endotoxemia by reducing the expression of downstream effector cytokines. These data indicate that D-DT is a MIF-like cytokine with an overlapping spectrum of activities that are important for our understanding of MIF-dependent physiology and pathology.
    Print ISSN: 0027-8424
    Electronic ISSN: 1091-6490
    Topics: Biology , Medicine , Natural Sciences in General
    Published by National Academy of Sciences
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  • 4
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    Mesh size selectivity of the gillnet in East China Sea (2017)
    Institute of Physics (IOP)
    Details
    Publication Date: 2017-07-26
    Description: A production test using several gillnets with various mesh sizes was carried out to discover the selectivity of gillnets in the East China Sea. The result showed that the composition of the catch species was synthetically affected by panel height and mesh size. The bycatch species of the 10-m nets were more than those of the 6-m nets. For target species, the effect of panel height on juvenile fish was ambiguous, but the number of juvenile fish declined quickly with the increase in mesh size. According to model deviance ( D ) and Akaike’s information criterion, the bi-normal model provided the best fit for small yellow croaker ( Larimichthy polyactis ), and the relative retention was 0.2 and 1, respectively. For Chelidonichthys spinosus , the log-normal was the best model; the right tilt of the selectivity curve was obvious and well coincided with the original data. The contact population of small yellow croaker showed a bi-normal distribution, and body lengths ra...
    Print ISSN: 1755-1307
    Electronic ISSN: 1755-1315
    Topics: Geography , Geosciences , Physics
    Published by Institute of Physics (IOP)
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  • 5
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    Deep learning of the tissue-regulated splicing code (2014)
    Oxford University Press
    Details
    Publication Date: 2014-06-17
    Description: Motivation: Alternative splicing (AS) is a regulated process that directs the generation of different transcripts from single genes. A computational model that can accurately predict splicing patterns based on genomic features and cellular context is highly desirable, both in understanding this widespread phenomenon, and in exploring the effects of genetic variations on AS. Methods: Using a deep neural network, we developed a model inferred from mouse RNA-Seq data that can predict splicing patterns in individual tissues and differences in splicing patterns across tissues. Our architecture uses hidden variables that jointly represent features in genomic sequences and tissue types when making predictions. A graphics processing unit was used to greatly reduce the training time of our models with millions of parameters. Results: We show that the deep architecture surpasses the performance of the previous Bayesian method for predicting AS patterns. With the proper optimization procedure and selection of hyperparameters, we demonstrate that deep architectures can be beneficial, even with a moderately sparse dataset. An analysis of what the model has learned in terms of the genomic features is presented. Contact: frey@psi.toronto.edu Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 6
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    Plasmodium MIF hinders memory CD4 T-cell formation [Immunology] (2012)
    National Academy of Sciences
    Details
    Publication Date: 2012-08-01
    Description: The inability to acquire protective immunity against Plasmodia is the chief obstacle to malaria control, and inadequate T-cell responses may facilitate persistent blood-stage infection. Malaria is characterized by a highly inflammatory cytokine milieu, and the lack of effective protection against infection suggests that memory T cells are not adequately formed or maintained. Using a genetically targeted strain of Plasmodium berghei, we observed that the Plasmodium ortholog of macrophage migration inhibitory factor enhanced inflammatory cytokine production and also induced antigen-experienced CD4 T cells to develop into short-lived effector cells rather than memory precursor cells. The short-lived effector CD4 T cells were more susceptible to Bcl-2–associated apoptosis, resulting in decreased CD4 T-cell recall responses against challenge infections. These findings indicate that Plasmodia actively interfere with the development of immunological memory and may account for the evolutionary conservation of parasite macrophage migration inhibitory factor orthologs.
    Print ISSN: 0027-8424
    Electronic ISSN: 1091-6490
    Topics: Biology , Medicine , Natural Sciences in General
    Published by National Academy of Sciences
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  • 7
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    HEALER: homomorphic computation of ExAct Logistic rEgRession for secure rare disease variants analysis in GWAS (2016)
    Oxford University Press
    Details
    Publication Date: 2016-01-10
    Description: Motivation: Genome-wide association studies (GWAS) have been widely used in discovering the association between genotypes and phenotypes. Human genome data contain valuable but highly sensitive information. Unprotected disclosure of such information might put individual’s privacy at risk. It is important to protect human genome data. Exact logistic regression is a bias-reduction method based on a penalized likelihood to discover rare variants that are associated with disease susceptibility. We propose the HEALER framework to facilitate secure rare variants analysis with a small sample size. Results: We target at the algorithm design aiming at reducing the computational and storage costs to learn a homomorphic exact logistic regression model (i.e. evaluate P -values of coefficients), where the circuit depth is proportional to the logarithmic scale of data size. We evaluate the algorithm performance using rare Kawasaki Disease datasets. Availability and implementation: Download HEALER at http://research.ucsd-dbmi.org/HEALER/ Contact: shw070@ucsd.edu Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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