There is a huge demand on bioinformaticians to provide their biologists with user friendly and scalable software infrastructures to capture, exchange, and exploit the unprecedented amounts of new ...*omics data. We here present MOLGENIS, a generic, open source, software toolkit to quickly produce the bespoke MOLecular GENetics Information Systems needed.
The MOLGENIS toolkit provides bioinformaticians with a simple language to model biological data structures and user interfaces. At the push of a button, MOLGENIS' generator suite automatically translates these models into a feature-rich, ready-to-use web application including database, user interfaces, exchange formats, and scriptable interfaces. Each generator is a template of SQL, JAVA, R, or HTML code that would require much effort to write by hand. This 'model-driven' method ensures reuse of best practices and improves quality because the modeling language and generators are shared between all MOLGENIS applications, so that errors are found quickly and improvements are shared easily by a re-generation. A plug-in mechanism ensures that both the generator suite and generated product can be customized just as much as hand-written software.
In recent years we have successfully evaluated the MOLGENIS toolkit for the rapid prototyping of many types of biomedical applications, including next-generation sequencing, GWAS, QTL, proteomics and biobanking. Writing 500 lines of model XML typically replaces 15,000 lines of hand-written programming code, which allows for quick adaptation if the information system is not yet to the biologist's satisfaction. Each application generated with MOLGENIS comes with an optimized database back-end, user interfaces for biologists to manage and exploit their data, programming interfaces for bioinformaticians to script analysis tools in R, Java, SOAP, REST/JSON and RDF, a tab-delimited file format to ease upload and exchange of data, and detailed technical documentation. Existing databases can be quickly enhanced with MOLGENIS generated interfaces using the 'ExtractModel' procedure.
The MOLGENIS toolkit provides bioinformaticians with a simple model to quickly generate flexible web platforms for all possible genomic, molecular and phenotypic experiments with a richness of interfaces not provided by other tools. All the software and manuals are available free as LGPLv3 open source at http://www.molgenis.org.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Histone modifications are epigenetic marks that play fundamental roles in many biological processes including the control of chromatin-mediated regulation of gene expression. Little is known about ...interindividual variability of histone modification levels across the genome and to what extent they are influenced by genetic variation. We annotated the rat genome with histone modification maps, identified differences in histone trimethyl-lysine levels among strains, and described their underlying genetic basis at the genome-wide scale using ChIP-seq in heart and liver tissues in a panel of rat recombinant inbred and their progenitor strains. We identified extensive variation of histone methylation levels among individuals and mapped hundreds of underlying cis- and trans-acting loci throughout the genome that regulate histone methylation levels in an allele-specific manner. Interestingly, most histone methylation level variation was trans-linked and the most prominent QTL identified influenced H3K4me3 levels at 899 putative promoters throughout the genome in the heart. Cis- acting variation was enriched in binding sites of distinct transcription factors in heart and liver. The integrated analysis of DNA variation together with histone methylation and gene expression levels showed that histoneQTLs are an important predictor of gene expression and that a joint analysis significantly enhanced the prediction of gene expression traits (eQTLs). Our data suggest that genetic variation has a widespread impact on histone trimethylation marks that may help to uncover novel genotype-phenotype relationships.
The recent successes of genome-wide expression profiling in biology tend to overlook the power of genetics. We here propose a merger of genomics and genetics into ‘genetical genomics’. This involves ...expression profiling and marker-based fingerprinting of each individual of a segregating population, and exploits all the statistical tools used in the analysis of quantitative trait loci. Genetical genomics will combine the power of two different worlds in a way that is likely to become instrumental in the further unravelling of metabolic, regulatory and developmental pathways.
In high-throughput molecular profiling studies, genotype labels can be wrongly assigned at various experimental steps; the resulting mislabeled samples seriously reduce the power to detect the ...genetic basis of phenotypic variation. We have developed an approach to detect potential mislabeling, recover the "ideal" genotype and identify "best-matched" labels for mislabeled samples. On average, we identified 4% of samples as mislabeled in eight published datasets, highlighting the necessity of applying a "data cleaning" step before standard data analysis.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
For many complex traits, genetic variants have been found associated. However, it is still mostly unclear through which downstream mechanism these variants cause these phenotypes. Knowledge of these ...intermediate steps is crucial to understand pathogenesis, while also providing leads for potential pharmacological intervention. Here we relied upon natural human genetic variation to identify effects of these variants on trans-gene expression (expression quantitative trait locus mapping, eQTL) in whole peripheral blood from 1,469 unrelated individuals. We looked at 1,167 published trait- or disease-associated SNPs and observed trans-eQTL effects on 113 different genes, of which we replicated 46 in monocytes of 1,490 different individuals and 18 in a smaller dataset that comprised subcutaneous adipose, visceral adipose, liver tissue, and muscle tissue. HLA single-nucleotide polymorphisms (SNPs) were 10-fold enriched for trans-eQTLs: 48% of the trans-acting SNPs map within the HLA, including ulcerative colitis susceptibility variants that affect plausible candidate genes AOAH and TRBV18 in trans. We identified 18 pairs of unlinked SNPs associated with the same phenotype and affecting expression of the same trans-gene (21 times more than expected, P<10(-16)). This was particularly pronounced for mean platelet volume (MPV): Two independent SNPs significantly affect the well-known blood coagulation genes GP9 and F13A1 but also C19orf33, SAMD14, VCL, and GNG11. Several of these SNPs have a substantially higher effect on the downstream trans-genes than on the eventual phenotypes, supporting the concept that the effects of these SNPs on expression seems to be much less multifactorial. Therefore, these trans-eQTLs could well represent some of the intermediate genes that connect genetic variants with their eventual complex phenotypic outcomes.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The functional consequences of trait associated SNPs are often investigated using expression quantitative trait locus (eQTL) mapping. While trait-associated variants may operate in a cell-type ...specific manner, eQTL datasets for such cell-types may not always be available. We performed a genome-environment interaction (GxE) meta-analysis on data from 5,683 samples to infer the cell type specificity of whole blood cis-eQTLs. We demonstrate that this method is able to predict neutrophil and lymphocyte specific cis-eQTLs and replicate these predictions in independent cell-type specific datasets. Finally, we show that SNPs associated with Crohn's disease preferentially affect gene expression within neutrophils, including the archetypal NOD2 locus.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The genetics of plant metabolism Hall, Robert D; Keurentjes, Joost J B; de Vos, C H Ric ...
Nature genetics,
07/2006, Letnik:
38, Številka:
7
Journal Article
Recenzirano
Odprti dostop
Variation for metabolite composition and content is often observed in plants. However, it is poorly understood to what extent this variation has a genetic basis. Here, we describe the genetic ...analysis of natural variation in the metabolite composition in Arabidopsis thaliana. Instead of focusing on specific metabolites, we have applied empirical untargeted metabolomics using liquid chromatography-time of flight mass spectrometry (LC-QTOF MS). This uncovered many qualitative and quantitative differences in metabolite accumulation between A. thaliana accessions. Only 13.4% of the mass peaks were detected in all 14 accessions analyzed. Quantitative trait locus (QTL) analysis of more than 2,000 mass peaks, detected in a recombinant inbred line (RIL) population derived from the two most divergent accessions, enabled the identification of QTLs for about 75% of the mass signals. More than one-third of the signals were not detected in either parent, indicating the large potential for modification of metabolic composition through classical breeding.
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DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
It is known that genetic variants can affect gene expression, but it is not yet completely clear through what mechanisms genetic variation mediate this expression. We therefore compared the ...cis-effect of single nucleotide polymorphisms (SNPs) on gene expression between blood samples from 1,240 human subjects and four primary non-blood tissues (liver, subcutaneous, and visceral adipose tissue and skeletal muscle) from 85 subjects. We characterized four different mechanisms for 2,072 probes that show tissue-dependent genetic regulation between blood and non-blood tissues: on average 33.2% only showed cis-regulation in non-blood tissues; 14.5% of the eQTL probes were regulated by different, independent SNPs depending on the tissue of investigation. 47.9% showed a different effect size although they were regulated by the same SNPs. Surprisingly, we observed that 4.4% were regulated by the same SNP but with opposite allelic direction. We show here that SNPs that are located in transcriptional regulatory elements are enriched for tissue-dependent regulation, including SNPs at 3' and 5' untranslated regions (P = 1.84×10(-5) and 4.7×10(-4), respectively) and SNPs that are synonymous-coding (P = 9.9×10(-4)). SNPs that are associated with complex traits more often exert a tissue-dependent effect on gene expression (P = 2.6×10(-10)). Our study yields new insights into the genetic basis of tissue-dependent expression and suggests that complex trait associated genetic variants have even more complex regulatory effects than previously anticipated.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
We profiled 162 lines of Arabidopsis for variation in transcript, protein and metabolite abundance using mRNA microarrays, two-dimensional polyacrylamide gel electrophoresis, gas chromatography ...time-of-flight mass spectrometry, liquid chromatography quadrupole time-of-flight mass spectrometry, and proton nuclear magnetic resonance. We added all publicly available phenotypic data from the same lines and mapped quantitative trait loci (QTL) for 40,580 molecular and 139 phenotypic traits. We found six QTL hot spots with major, system-wide effects, suggesting there are six breakpoints in a system otherwise buffered against many of the 500,000 SNPs.
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DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
High-throughput genomics, transcriptomics, proteomics and metabolomics have the potential to identify the functional consequences of induced and natural genetic variation. Surprisingly, the ...experiments of most genomics researchers still mainly involve perturbing a biological system of interest by modifying either one factor or one gene at a time. By contrast, this article argues that multifactorial experimentation would allow the study of many more biologically relevant questions in parallel at the same or lower cost.
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DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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