669 research outputs found
MARC 21 para recursos contínuos
Full text linkTranslation and adaptation of the MARC 21 Format for Bibliographic Data, and MARC 21 Format for Holdings Data, Network Development and MARC Standards Office, Library of Congress, USA, by Angela Salles. Rio de Janeiro, 2010. 2 v. V.1 MARC 21 format for bibliographic data (updated until October 2010). V.2 MARC 21 format for data collection (Holdings) (updated until October 2008)
MARC 21 para recursos contínuos.
Full text linkTradução e adaptação de MARC 21 Format for Bibliographic Data e MARC 21 Format for Holdings Data, da Network Development and MARC Standards Office, da Library of Congress, USA, por Angela Salles
The interplay between genetics, the microbiome, DNA‐methylation & gene‐expression
Full text linkThere are many factors involved in the development of human diseases and traits. In recent years the field of human genetics has been very successful in linking genetic variation to diseases and traits. By conducting large-scale studies comparing the genetic make-up of affected versus non-affected participants, we have identified thousands of variants in the human genome that are more or less commonly found in cases compared to controls. These genome-wide association studies (GWAS) have been instrumental in the identification of genes linked to a multitude of diseases and traits. Variants in the functional parts of a gene can be relatively straightforward to interpret. However, not all the variants linked to disease can be directly interpreted. By using intermediate molecular data layers, such as gene expression, DNA-methylation or protein levels, we can gain more insight into the genetic variants identified by GWAS.However, we only get a limited picture of disease by focusing on genetic variation. Another important factor related to disease is the environment. But it is much harder to quantify environmental factors than to determine the genetic differences between two individuals. Using the intermediate molecular data, or biological omics, we can gain insights into the environment of individuals. The environment surrounding individuals can, for instance, influence the composition of their microbiome, but also their gene expression, DNA-methylation and protein levels. By studying the differences in these biological omics in relation to phenotypes and disease, we can learn more about the environmental factors that lead to disease. However, as with GWAS studies, we do not always know what the differences in these biological data layers mean.In this thesis we have focused on two biological omics, the gut microbiome composition and the DNA-methylome. The gut microbiome is the collection of micro-organisms that live together in the human gut; DNA-methylation is the occurrence of a methyl group bound to the DNA and this mainly occurs at cysteine-guanine pairs. In the first part of the thesis we have focused on inter-individual differences influencing, or influenced by, differences in the microbiome composition, while in the second part, we have focused on changes in DNA-methylation associated to tissue differences and on the influence of genetic variation on DNA-methylation
HGSVC2 full eQTL results
No full textFull summary statistics of the QTL mappings performed on the GEUVADIS and deep 1000GP RNA-sequencing samples presented in the: "Structural variation characterization from the de novo assembly of 64 haplotype-resolved human genomes of diverse ancestry" paper.
**Updated release now including sQTL and updated eQTL result
HGSVC2 full eQTL results
No full textFull summary statistics of the eQTL mapping performed on the GEUVADIS and deep 1000GP RNA-sequencing samples presented in the: "Structural variation characterization from the de novo assembly of 64 haplotype-resolved human genomes of diverse ancestry" paper
The interplay between genetics, the microbiome, DNA‐methylation & gene‐expression
Full text linkThere are many factors involved in the development of human diseases and traits. In recent years the field of human genetics has been very successful in linking genetic variation to diseases and traits. By conducting large-scale studies comparing the genetic make-up of affected versus non-affected participants, we have identified thousands of variants in the human genome that are more or less commonly found in cases compared to controls. These genome-wide association studies (GWAS) have been instrumental in the identification of genes linked to a multitude of diseases and traits. Variants in the functional parts of a gene can be relatively straightforward to interpret. However, not all the variants linked to disease can be directly interpreted. By using intermediate molecular data layers, such as gene expression, DNA-methylation or protein levels, we can gain more insight into the genetic variants identified by GWAS.However, we only get a limited picture of disease by focusing on genetic variation. Another important factor related to disease is the environment. But it is much harder to quantify environmental factors than to determine the genetic differences between two individuals. Using the intermediate molecular data, or biological omics, we can gain insights into the environment of individuals. The environment surrounding individuals can, for instance, influence the composition of their microbiome, but also their gene expression, DNA-methylation and protein levels. By studying the differences in these biological omics in relation to phenotypes and disease, we can learn more about the environmental factors that lead to disease. However, as with GWAS studies, we do not always know what the differences in these biological data layers mean.In this thesis we have focused on two biological omics, the gut microbiome composition and the DNA-methylome. The gut microbiome is the collection of micro-organisms that live together in the human gut; DNA-methylation is the occurrence of a methyl group bound to the DNA and this mainly occurs at cysteine-guanine pairs. In the first part of the thesis we have focused on inter-individual differences influencing, or influenced by, differences in the microbiome composition, while in the second part, we have focused on changes in DNA-methylation associated to tissue differences and on the influence of genetic variation on DNA-methylation
Erratum: An algorithm-based topographical biomaterials library to instruct cell fate (Proceedings of the National Academy of Sciences of the United States of America (2011) 108, 40 (16565-16570) DOI: 10.1073/pnas.1109861108)
Full text linkAPPLIED BIOLOGICAL SCIENCES Correction for “An algorithm-based topographical biomaterials library to instruct cell fate,” by Hemant V. Unadkat, Marc Hulsman, Kamiel Cornelissen, Bernke J. Papenburg, Roman K. Truckenmüller, Gerhard F. Post, Marc Uetz, Marcel J. T. Reinders, Dimitrios Stamatialis, Clemens A. van Blitterswijk, and Jan de Boer, which appeared in issue 40, October 4, 2011, of Proc Natl Acad Sci USA (108:16565–16570; first published September 26, 2011; 10.1073/pnas.1109861108). The authors note that Anne E. Carpenter and Matthias Wessling should be added to the author list between Roman K. Truckenmüller and Gerhard F. Post. Anne E. Carpenter should be credited with analyzing data. Matthias Wessling should be credited with designing research. The corrected author and affiliation lines, and author contributions appear below. The online version has been corrected
The interplay between genetics, the microbiome, DNA‐methylation & gene‐expression
Full text linkThere are many factors involved in the development of human diseases and traits. In recent years the field of human genetics has been very successful in linking genetic variation to diseases and traits. By conducting large-scale studies comparing the genetic make-up of affected versus non-affected participants, we have identified thousands of variants in the human genome that are more or less commonly found in cases compared to controls. These genome-wide association studies (GWAS) have been instrumental in the identification of genes linked to a multitude of diseases and traits. Variants in the functional parts of a gene can be relatively straightforward to interpret. However, not all the variants linked to disease can be directly interpreted. By using intermediate molecular data layers, such as gene expression, DNA-methylation or protein levels, we can gain more insight into the genetic variants identified by GWAS.However, we only get a limited picture of disease by focusing on genetic variation. Another important factor related to disease is the environment. But it is much harder to quantify environmental factors than to determine the genetic differences between two individuals. Using the intermediate molecular data, or biological omics, we can gain insights into the environment of individuals. The environment surrounding individuals can, for instance, influence the composition of their microbiome, but also their gene expression, DNA-methylation and protein levels. By studying the differences in these biological omics in relation to phenotypes and disease, we can learn more about the environmental factors that lead to disease. However, as with GWAS studies, we do not always know what the differences in these biological data layers mean.In this thesis we have focused on two biological omics, the gut microbiome composition and the DNA-methylome. The gut microbiome is the collection of micro-organisms that live together in the human gut; DNA-methylation is the occurrence of a methyl group bound to the DNA and this mainly occurs at cysteine-guanine pairs. In the first part of the thesis we have focused on inter-individual differences influencing, or influenced by, differences in the microbiome composition, while in the second part, we have focused on changes in DNA-methylation associated to tissue differences and on the influence of genetic variation on DNA-methylation
The interplay between genetics, the microbiome, DNA‐methylation & gene‐expression
Full text linkThere are many factors involved in the development of human diseases and traits. In recent years the field of human genetics has been very successful in linking genetic variation to diseases and traits. By conducting large-scale studies comparing the genetic make-up of affected versus non-affected participants, we have identified thousands of variants in the human genome that are more or less commonly found in cases compared to controls. These genome-wide association studies (GWAS) have been instrumental in the identification of genes linked to a multitude of diseases and traits. Variants in the functional parts of a gene can be relatively straightforward to interpret. However, not all the variants linked to disease can be directly interpreted. By using intermediate molecular data layers, such as gene expression, DNA-methylation or protein levels, we can gain more insight into the genetic variants identified by GWAS.However, we only get a limited picture of disease by focusing on genetic variation. Another important factor related to disease is the environment. But it is much harder to quantify environmental factors than to determine the genetic differences between two individuals. Using the intermediate molecular data, or biological omics, we can gain insights into the environment of individuals. The environment surrounding individuals can, for instance, influence the composition of their microbiome, but also their gene expression, DNA-methylation and protein levels. By studying the differences in these biological omics in relation to phenotypes and disease, we can learn more about the environmental factors that lead to disease. However, as with GWAS studies, we do not always know what the differences in these biological data layers mean.In this thesis we have focused on two biological omics, the gut microbiome composition and the DNA-methylome. The gut microbiome is the collection of micro-organisms that live together in the human gut; DNA-methylation is the occurrence of a methyl group bound to the DNA and this mainly occurs at cysteine-guanine pairs. In the first part of the thesis we have focused on inter-individual differences influencing, or influenced by, differences in the microbiome composition, while in the second part, we have focused on changes in DNA-methylation associated to tissue differences and on the influence of genetic variation on DNA-methylation
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