Jo Vandesompele

Bio: Jo Vandesompele is an academic researcher from Ghent University. The author has contributed to research in topics: Neuroblastoma & microRNA. The author has an hindex of 88, co-authored 383 publications receiving 59368 citations. Previous affiliations of Jo Vandesompele include Washington University in St. Louis & Ghent University Hospital.

Multiplex Amplicon Quantification (MAQ), a fast and efficient method for the simultaneous detection of copy number alterations in neuroblastoma.

Abstract: Cancer genomes display characteristic patterns of chromosomal imbalances, often with diagnostic and prognostic relevance. Therefore assays for genome-wide copy number screening and simultaneous detection of copy number alterations in specific chromosomal regions are of increasing importance in the diagnostic work-up of tumors. We tested the performance of Multiplex Amplicon Quantification, a newly developed low-cost, closed-tube and high-throughput PCR-based technique for detection of copy number alterations in regions with prognostic relevance for neuroblastoma. Comparison with array CGH and the established Multiplex Ligation-dependent Probe Amplification method on 52 neuroblastoma tumors showed that Multiplex Amplicon Quantification can reliably detect the important genomic aberrations. Multiplex Amplicon Quantification is a low-cost and high-throughput PCR-based technique that can reliably detect copy number alterations in regions with prognostic relevance for neuroblastoma.

Pathological splice mutations outside the invariant AG/GT splice sites of BRCA1 exon 5 increase alternative transcript levels in the 5' end of the BRCA1 gene

Abstract: We report two novel mutations in the splice sites of BRCA1 exon 5: IVS5+3A>G, a Belgian founder mutation, and IVS3-6T>G, identified in one family with a strong family history of breast cancer. Real-time RT-PCR showed that IVS3-6T>G leads to a fivefold increase of the BRCA1-Deltaex5 (isoform with an in frame skip of exon 5) ratio to the total BRCA1 expression level. lVS5+3A>G results in a 10-fold increase of the BRCA1-Delta22ntex5 ratio (isoform with an out of frame skip of the last 22 nucleotides of exon 5) and a twofold increase of the BRCA1-Deltaex5 ratio. These altered ratios are most likely to result from increased expression of the alternative transcripts, although we cannot completely rule out a small decrease of the total BRCA1 expression level due to highly variable BRCA1 levels in cultured cell lines. In order to explore the functional significance of the isoforms, we evaluated their prevalence in normal tissues and cancer cell lines. The BRCA1-Delta22ntex5 ratio was significantly higher in an ovarian cancer cell line compared to normal ovarian tissue. Our findings suggest that revealing the defects caused by some splice mutations requires accurate quantitative methods. We hypothesize that disruption of alternative transcript ratios of BRCA1 may be a dominant mechanism affecting predisposition to hereditary breast and/or ovarian cancer.

An Assessment of Air As a Source of DNA Contamination Encountered When Performing PCR

Abstract: Sensitive molecular methods, such as the PCR, can detect low-level contamination, and careful technique is required to reduce the impact of contaminants. Yet, some assays that are designed to detect high copy-number target sequences appear to be impossible to perform without contamination, and frequently, personnel or laboratory environment are held responsible as the source. This complicates diagnostic and research analysis when using molecular methods. To investigate the air specifically as a source of contamination, which might occur during PCR setup, we exposed tubes of water to the air of a laboratory and clean hood for up to 24 h. To increase the chances of contamination, we also investigated a busy open-plan office in the same way. All of the experiments showed the presence of human and rodent DNA contamination. However, there was no accumulation of the contamination in any of the environments investigated, suggesting that the air was not the source of contamination. Even the air from a busy open-plan office was a poor source of contamination for all of the DNA sequences investigated (human, bacterial, fungal, and rodent). This demonstrates that the personnel and immediate laboratory environment are not necessarily to blame for the observed contamination.

Model-Based Classification for Digital PCR: Your Umbrella for Rain.

Abstract: Standard data analysis pipelines for digital PCR estimate the concentration of a target nucleic acid by digitizing the end-point fluorescence of the parallel micro-PCR reactions, using an automated hard threshold. While it is known that misclassification has a major impact on the concentration estimate and substantially reduces accuracy, the uncertainty of this classification is typically ignored. We introduce a model-based clustering method to estimate the probability that the target is present (absent) in a partition conditional on its observed fluorescence and the distributional shape in no-template control samples. This methodology acknowledges the inherent uncertainty of the classification and provides a natural measure of precision, both at individual partition level and at the level of the global concentration. We illustrate our method on genetically modified organism, inhibition, dynamic range, and mutation detection experiments. We show that our method provides concentration estimates of similar ...

limma powers differential expression analyses for RNA-sequencing and microarray studies

Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

Cutadapt removes adapter sequences from high-throughput sequencing reads

Abstract: When small RNA is sequenced on current sequencing machines, the resulting reads are usually longer than the RNA and therefore contain parts of the 3' adapter. That adapter must be found and removed error-tolerantly from each read before read mapping. Previous solutions are either hard to use or do not offer required features, in particular support for color space data. As an easy to use alternative, we developed the command-line tool cutadapt, which supports 454, Illumina and SOLiD (color space) data, offers two adapter trimming algorithms, and has other useful features. Cutadapt, including its MIT-licensed source code, is available for download at http://code.google.com/p/cutadapt/

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes

Abstract: Gene-expression analysis is increasingly important in biological research, with real-time reverse transcription PCR (RT-PCR) becoming the method of choice for high-throughput and accurate expression profiling of selected genes. Given the increased sensitivity, reproducibility and large dynamic range of this methodology, the requirements for a proper internal control gene for normalization have become increasingly stringent. Although housekeeping gene expression has been reported to vary considerably, no systematic survey has properly determined the errors related to the common practice of using only one control gene, nor presented an adequate way of working around this problem. We outline a robust and innovative strategy to identify the most stably expressed control genes in a given set of tissues, and to determine the minimum number of genes required to calculate a reliable normalization factor. We have evaluated ten housekeeping genes from different abundance and functional classes in various human tissues, and demonstrated that the conventional use of a single gene for normalization leads to relatively large errors in a significant proportion of samples tested. The geometric mean of multiple carefully selected housekeeping genes was validated as an accurate normalization factor by analyzing publicly available microarray data. The normalization strategy presented here is a prerequisite for accurate RT-PCR expression profiling, which, among other things, opens up the possibility of studying the biological relevance of small expression differences.