Netherlands Bioinformatics Centre

About: Netherlands Bioinformatics Centre is a nonprofit organization based out in Nijmegen, Netherlands. It is known for research contribution in the topics: Gene & Genome. The organization has 126 authors who have published 164 publications receiving 9177 citations.

A community-driven global reconstruction of human metabolism

Abstract: Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ~2× more reactions and ~1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type–specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.

Protein structure analysis of mutations causing inheritable diseases. An e-Science approach with life scientist friendly interfaces

Abstract: Background: Many newly detected point mutations are located in protein-coding regions of the human genome. Knowledge of their effects on the protein’s 3D structure provides insight into the protein’s mechanism, can aid the design of further experiments, and eventually can lead to the development of new medicines and diagnostic tools. Results: In this article we describe HOPE, a fully automatic program that analyzes the structural and functional effects of point mutations. HOPE collects information from a wide range of information sources including calculations on the 3D coordinates of the protein by using WHAT IF Web services, sequence annotations from the UniProt database, and predictions by DAS services. Homology models are built with YASARA. Data is stored in a database and used in a decision scheme to identify the effects of a mutation on the protein’s 3D structure and function. HOPE builds a report with text, figures, and animations that is easy to use and understandable for (bio) medical researchers. Conclusions: We tested HOPE by comparing its output to the results of manually performed projects. In all straightforward cases HOPE performed similar to a trained bioinformatician. The use of 3D structures helps optimize the results in terms of reliability and details. HOPE’s results are easy to understand and are presented in a way that is attractive for researchers without an extensive bioinformatics background. Background The omics-revolution has led to a rapid increase in detected disease-related human mutations. A considerable fraction of these mutations is located in proteincoding regions of the genome and thus can affect the structure and function of that protein, thereby causing a phenotypic effect. Knowledge of these structural and functional effects can aid the design of further experiments and can eventually lead to the development of better disease diagnostics or even medicines to help cure patients. The analysis of mutations that cause the EEC syndrome, for example, revealed that some patients carry a mutation that disturbs dimerisation of the affected P63 protein [1]. This information has triggered a search for drugs http://www.epistem.eu; [2]). In another case, the study of a mutation in the human hemochromatosis protein (HFE), which causes hereditary hemochromatosis, resulted in new insights that are now being used to develop novel diagnostic methods [3]. These and numerous other examples have highlighted the importance of using heterogeneous data, especially structure information, in the study of human disease-linked protein variants. The data that can aid our understanding of the underlying mechanism of disease related mutations can range from the protein’s three-dimensional (3D) structure to its role in biological pathways, or from information generated by mutagenesis experiments to predicted functional motifs. Collecting all available information related to the protein of interest can be challenging and timeconsuming. It is a difficult task to extract exactly those pieces of information that can lead to a conclusion about the effects of a mutation. Several online Web servers exist that offer help to the (bio)medical researcher in predicting these effects. These servers use information from a wide range of sources to reach conclusions

Cohesin and CTCF differentially affect chromatin architecture and gene expression in human cells.

Abstract: Recent studies of genome-wide chromatin interactions have revealed that the human genome is partitioned into many self-associating topological domains. The boundary sequences between domains are enriched for binding sites of CTCC-binding factor (CTCF) and the cohesin complex, implicating these two factors in the establishment or maintenance of topological domains. To determine the role of cohesin and CTCF in higher-order chromatin architecture in human cells, we depleted the cohesin complex or CTCF and examined the consequences of loss of these factors on higher-order chromatin organization, as well as the transcriptome. We observed a general loss of local chromatin interactions upon disruption of cohesin, but the topological domains remain intact. However, we found that depletion of CTCF not only reduced intradomain interactions but also increased interdomain interactions. Furthermore, distinct groups of genes become misregulated upon depletion of cohesin and CTCF. Taken together, these observations suggest that CTCF and cohesin contribute differentially to chromatin organization and gene regulation.

Toward interoperable bioscience data

Abstract: To make full use of research data, the bioscience community needs to adopt technologies and reward mechanisms that support interoperability and promote the growth of an open 'data commoning' culture. Here we describe the prerequisites for data commoning and present an established and growing ecosystem of solutions using the shared 'Investigation-Study-Assay' framework to support that vision.

The Genome of the Netherlands: design, and project goals

Abstract: Within the Netherlands a national network of biobanks has been established (Biobanking and Biomolecular Research Infrastructure-Netherlands (BBMRI-NL)) as a national node of the European BBMRI. One of the aims of BBMRI-NL is to enrich biobanks with different types of molecular and phenotype data. Here, we describe the Genome of the Netherlands (GoNL), one of the projects within BBMRI-NL. GoNL is a whole-genome-sequencing project in a representative sample consisting of 250 trio-families from all provinces in the Netherlands, which aims to characterize DNA sequence variation in the Dutch population. The parent-offspring trios include adult individuals ranging in age from 19 to 87 years (mean=53 years; SD=16 years) from birth cohorts 1910-1994. Sequencing was done on blood-derived DNA from uncultured cells and accomplished coverage was 14-15x. The family-based design represents a unique resource to assess the frequency of regional variants, accurately reconstruct haplotypes by family-based phasing, characterize short indels and complex structural variants, and establish the rate of de novo mutational events. GoNL will also serve as a reference panel for imputation in the available genome-wide association studies in Dutch and other cohorts to refine association signals and uncover population-specific variants. GoNL will create a catalog of human genetic variation in this sample that is uniquely characterized with respect to micro-geographic location and a wide range of phenotypes. The resource will be made available to the research and medical community to guide the interpretation of sequencing projects. The present paper summarizes the global characteristics of the project.