ALBERT

Description: Background: Circular chromosome conformation capture (4C) has provided important insights into three dimensional (3D) genome organization and its critical impact on the regulation of gene expression. We developed a new quantitative framework based on polymer physics for the analysis of paired-end sequencing 4C (PE-4Cseq) data. We applied this strategy to the study of chromatin interaction changes upon a 4.3 Mb DNA deletion in mouse region 4E2. Results: A significant number of differentially interacting regions (DIRs) and chromatin compaction changes were detected in the deletion chromosome compared to a wild-type (WT) control. Selected DIRs were validated by 3D DNA FISH experiments, demonstrating the robustness of our pipeline. Interestingly, significant overlaps of DIRs with CTCF/Smc1 binding sites and differentially expressed genes were observed. Conclusions: Altogether, our PE-4Cseq analysis pipeline provides a comprehensive characterization of DNA deletion effects on chromatin structure and function.

Description: Background: Genome comparisons between closely related species often show non-conserved regions across chromosomes. Some of them are located in specific regions of chromosomes and some are even confined to one or more entire chromosomes. The origin and biological relevance of these non-conserved regions are still largely unknown. Here we used the genome of Fusarium graminearum to elucidate the significance of non-conserved regions. Results: The genome of F. graminearum harbours thirteen non-conserved regions dispersed over all of the four chromosomes. Using RNA-Seq data from the mycelium of F. graminearum, we found weakly expressed regions on all of the four chromosomes that exactly matched with non-conserved regions. Comparison of gene expression between two different developmental stages (conidia and mycelium) showed that the expression of genes in conserved regions is stable, while gene expression in non-conserved regions is much more influenced by developmental stage. In addition, genes involved in the production of secondary metabolites and secreted proteins are enriched in non-conserved regions, suggesting that these regions could also be important for adaptations to new environments, including adaptation to new hosts. Finally, we found evidence that non-conserved regions are generated by sequestration of genes from multiple locations. Gene relocations may lead to clustering of genes with similar expression patterns or similar biological functions, which was clearly exemplified by the PKS2 gene cluster. Conclusions: Our results showed that chromosomes can be functionally divided into conserved and non-conserved regions, and both could have specific and distinct roles in genome evolution and regulation of gene expression.

Description: Background: Tuberous sclerosis complex (TSC) is an autosomal dominant disorder caused by mutations in TSC1 and TSC2. Conventional DNA diagnostic screens identify a TSC1 or TSC2 mutation in 75 - 90% of individuals categorised with definite TSC. The remaining individuals either have a mutation that is undetectable using conventional methods, or possibly a mutation in another as yet unidentified gene. Methods: Here we apply a targeted Next Generation Sequencing (NGS) approach to screen the complete TSC1 and TSC2 genomic loci in 7 individuals fulfilling the clinical diagnostic criteria for definite TSC in whom no TSC1 or TSC2 mutations were identified using conventional screening methods. Results: We identified and confirmed pathogenic mutations in 3 individuals. In the remaining individuals we identified variants of uncertain clinical significance. The identified variants included mosaic changes, changes located deep in intronic sequences and changes affecting promoter regions that would not have been identified using exon-only based analyses. Conclusions: Targeted NGS of the TSC1 and TSC2 loci is a suitable method to increase the yield of mutations identified in the TSC patient population.

Description: Background: Cow's milk allergy (CMA) is the most common food allergy among infants. No data are available on the health care burden of suspected CMA in general practice. This study was conducted to evaluate the burden of suspected CMA in general practice (GP): (a) prevalence, (b) presenting symptoms, (c) diagnostic process, (d) guideline adherence, and (e) dietary measures. Methods: A retrospective cohort study was carried out in four Julius Healthcare Centers (JHCs). These JHCs form the core primary care academic network of the department of general practice of the University Medical Center of Utrecht. Electronic records of the first year of infants born May 2009 - April 2010 registered in the JHCs were screened for possible CMA suspicion. Preventive child healthcare (PCH) records were reviewed for additional information. Clinical presentation, diagnostic strategies and dietary measures were extracted. Results: Of 804 infants evaluated, 55 presented with symptoms fitting the suspicion of CMA (prevalence of 7%). Presenting complaints involved the skin (71%); the gastrointestinal tract (60%); the respiratory tract (13%) or other symptoms (36%) and 23 infants presented with symptoms of two or more organ systems. In 31 children (56%) a food challenge was performed (n = 28 open and n = 3 double-blind). Open challenge test results were difficult to interpret due to inadequate implementation or reporting. None had confirmed CMA after an adequate challenge test. Long term milk substitute formulas were prescribed in 39 (71%) infants. Conclusion: On a yearly basis seven percent of children visit their GP for suspected CMA. A positive CMA diagnosis was rarely established after adequate implementation and reporting of diagnostics, yet long term dietary measures were prescribed in 〉70% of patients. There is definitely need for improvement of diagnosing CMA in primary care.

Description: Network enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for ne...

Description: Background: ImmunoPrecipitation (IP) efficiencies may vary largely between different antibodies and between repeated experiments with the same antibody. These differences have a large impact on the quality of ChIP-seq data: a more efficient experiment will necessarily lead to a higher signal to background ratio, and therefore to an apparent larger number of enriched regions, compared to a less efficient experiment. In this paper, we show how IP efficiencies can be explicitly accounted for in the joint statistical modelling of ChIP-seq data. Results: We fit a latent mixture model to eight experiments on two proteins, from two laboratories where different antibodies are used for the two proteins. We use the model parameters to estimate the efficiencies of individual experiments, and find that these are clearly different for the different laboratories, and amongst technical replicates from the same lab. When we account for ChIP efficiency, we find more regions bound in the more efficient experiments than in the less efficient ones, at the same false discovery rate. A priori knowledge of the same number of binding sites across experiments can also be included in the model for a more robust detection of differentially bound regions among two different proteins. Conclusions: We propose a statistical model for the detection of enriched and differentially bound regions from multiple ChIP-seq data sets. The framework that we presentaccounts explicitly for IP efficiencies in ChIP-seq data, and allows to model jointly, rather than individually, replicates and experiments from different proteins, leading to more robust biological conclusions.

Description: Background: Automated gene-calling is still an error-prone process, particularly for the highly plastic genomes of fungal species. Improvement through quality control and manual curation of gene models is a time-consuming process that requires skilled biologists and is only marginally performed. The wealth of available fungal genomes has not yet been exploited by an automated method that applies quality control of gene models in order to obtain more accurate genome annotations. Results: We provide a novel method named alignment-based fungal gene prediction (ABFGP) that is particularly suitable for plastic genomes like those of fungi. It can assess gene models on a gene-by-gene basis making use of informant gene loci. Its performance was benchmarked on 6,965 gene models confirmed by full-length unigenes from ten different fungi. 79.4% of all gene models were correctly predicted by ABFGP. It improves the output of ab initio gene prediction software due to a higher sensitivity and precision for all gene model components. Applicability of the method was shown by revisiting the annotations of six different fungi, using gene loci from up to 29 fungal genomes as informants. Between 7,231 and 8,337 genes were assessed by ABFGP and for each genome between 1,724 and 3,505 gene model revisions were proposed. The reliability of the proposed gene models is assessed by an a posteriori introspection procedure of each intron and exon in the multiple gene model alignment. The total number and type of proposed gene model revisions in the six fungal genomes is correlated to the quality of the genome assembly, and to sequencing strategies used in the sequencing centre, highlighting different types of errors in different annotation pipelines. The ABFGP method is particularly successful in discovering sequence errors and/or disruptive mutations causing truncated and erroneous gene models. Conclusions: The ABFGP method is an accurate and fully automated quality control method for fungal gene catalogues that can be easily implemented into existing annotation pipelines. With the exponential release of new genomes, the ABFGP method will help decreasing the number of gene models that require additional manual curation.

Description: Background: Many QTL studies have two common features: (1) often there is missing marker information, (2) among many markers involved in the biological process only a few are causal. In statistics, the second issue falls under the headings "sparsity" and "causal inference". The goal of this work is to develop a two-step statistical methodology for QTL mapping for markers with binary genotypes. The first step introduces a novel imputation method for missing genotypes. Outcomes of the proposed imputation method are probabilities which serve as weights to the second step, namely in weighted lasso. The sparse phenotype inference is employed to select a set of predictive markers for the trait of interest. Results: Simulation studies validate the proposed methodology under a wide range of realistic settings. Furthermore, the methodology outperforms alternative imputation and variable selection methods in such studies. The methodology was applied to an Arabidopsis experiment, containing 69 markers for 165 recombinant inbred lines of a F8 generation. The results confirm previously identified regions, however several new markers are also found. On the basis of the inferred ROC behavior these markers show good potential for being real, especially for the germination trait Gmax. Conclusions: Our imputation method shows higher accuracy in terms of sensitivity and specificity compared to alternative imputation method. Also, the proposed weighted lasso outperforms commonly practised multiple regression as well as the traditional lasso and adaptive lasso with three weighting schemes. This means that under realistic missing data settings this methodology can be used for QTL identification.