Search PubMed⌕ Search

Biomedical subjects

Ritsert C Jansen

Publications and source records attributed to Ritsert C Jansen.

8 recordsLinked to original sources
PubMed2004

Molecular Genetics Information System (MOLGENIS): alternatives in developing local experimental genomics databases.

MOTIVATION: Genomic research laboratories need adequate infrastructure to support management of their data production and research workflow. But what makes infrastructure adequate? A lack of appropriate criteria makes any decision on buying or developing a system difficult. Here, we report on the decision process for the case of a molecular genetics group establishing a microarray laboratory. RESULTS: Five typical requirements for experimental genomics database systems were identified: (i) evolution ability to keep up with the fast developing genomics field; (ii) a suitable data model to deal with local diversity; (iii) suitable storage of data files in the system; (iv) easy exchange with other software; and (v) low maintenance costs. The computer scientists and the researchers of the local microarray laboratory considered alternative solutions for these five requirements and chose the following options: (i) use of automatic code generation; (ii) a customized data model based on standards; (iii) storage of datasets as black boxes instead of decomposing them in database tables; (iv) loosely linking to other programs for improved flexibility; and (v) a low-maintenance web-based user interface. Our team evaluated existing microarray databases and then decided to build a new system, Molecular Genetics Information System (MOLGENIS), implemented using code generation in a period of three months. This case can provide valuable insights and lessons to both software developers and a user community embarking on large-scale genomic projects. AVAILABILITY: http://www.molgenis.nl

Computational Biology↗
PubMed2004

The Collaborative Cross, a community resource for the genetic analysis of complex traits.

The goal of the Complex Trait Consortium is to promote the development of resources that can be used to understand, treat and ultimately prevent pervasive human diseases. Existing and proposed mouse resources that are optimized to study the actions of isolated genetic loci on a fixed background are less effective for studying intact polygenic networks and interactions among genes, environments, pathogens and other factors. The Collaborative Cross will provide a common reference panel specifically designed for the integrative analysis of complex systems and will change the way we approach human health and disease.

Animals↗
PubMed2003

The nature and identification of quantitative trait loci: a community's view.

This white paper by eighty members of the Complex Trait Consortium presents a community's view on the approaches and statistical analyses that are needed for the identification of genetic loci that determine quantitative traits. Quantitative trait loci (QTLs) can be identified in several ways, but is there a definitive test of whether a candidate locus actually corresponds to a specific QTL?

Animals↗
PubMed2002

Assembly of two transgenes in an artificial chromatin domain gives highly coordinated expression in tobacco.

The chromatin loop model predicts that genes within the same chromatin domain exhibit coordinated regulation. We here present the first direct experimental support for this model in plants. Two reporter genes, the E. coli beta-glucuronidase gene and the firefly luciferase gene, driven by different promoters, were placed between copies of the chicken lysozyme A element, a member of the matrix-associated region (MAR) group of chromatin boundary elements, and introduced in tobacco (Nicotiana tabacum). In plants carrying A elements, quantitative enzyme activities and mRNA levels of both genes show high correlations compared to control plants. The A element thus creates an artificial chromatin domain that yields coordinated expression. Surprisingly, enzyme activities correlated poorly with their respective mRNA levels. We hypothesize that this indicates the occurrence of "error pipelines" in data generation: systematic errors of a given analytical method will point in the same direction and cancel out in correlation analysis, resulting in better correlations. In combining different methods of analysis, however, such errors do not cancel out and as a result relevant correlations can be masked. Such error pipelines will have to be taken into account when different types of (e.g., whole-genome) data sets are combined in quantitative analyses.

Chromatin↗
PubMed2002

A penalized likelihood method for mapping epistatic quantitative trait Loci with one-dimensional genome searches.

Epistasis is a common and important phenomenon, as indicated by results from a number of recent experiments. Unfortunately, the discovery of epistatic quantitative trait loci (QTL) is difficult since one must search for multiple QTL simultaneously in two or more dimensions. Such a multidimensional search necessitates many statistical tests, and a high statistical threshold must be adopted to avoid false positives. Furthermore, the large number of (interaction) parameters in comparison with the number of observations results in a serious danger of overfitting and overinterpretation of the data. In this article we present a new statistical framework for mapping epistasis in inbred line crosses. It is based on reducing the high dimensionality of the problem in two ways. First, epistatic QTL are mapped in a one-dimensional genome scan for high interactions between QTL and the genetic background. Second, the dimension of the search is bounded by penalized likelihood methods. We use simulated backcross data to illustrate the new approach.

Chromosome Mapping↗