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.

Chromosome 2 short arm translocations revealed by M-FISH analysis of neuroblastoma cell lines

Abstract: Procedure M-FISH analysis was performed on 18 neuroblastoma cell lines, which were previously studied with cytogenetic, standard FISH and CGH data. Results One of the most striking findings of this study was the detection of chromosome 2 short arm rearrangements in 61% of the investigated cell lines. These rearrangements resulted from translocations with various partner chromosomes. All translocations, except one were unbalanced, leading to the consistent gain of chromosome segment 2pter-p22. A cryptic balanced translocation t(2;4) was observed with a breakpoint located in the vicinity of MYCN in cell line NBL-S. Conclusions Combination of M-FISH results together with cytogenetic, standard FISH and CGH data yielded the most comprehensive description of chromosome 2 short arm rearrangements, leading to a consistent gain of chromosome 2 short arm material. Med. Pediatr. Oncol. 35: 538–540, 2000. © 2000 Wiley-Liss, Inc.

Analysing 454 amplicon resequencing experiments using the modular and database oriented Variant Identification Pipeline.

Abstract: Next-generation amplicon sequencing enables high-throughput genetic diagnostics, sequencing multiple genes in several patients together in one sequencing run. Currently, no open-source out-of-the-box software solution exists that reliably reports detected genetic variations and that can be used to improve future sequencing effectiveness by analyzing the PCR reactions. We developed an integrated database oriented software pipeline for analysis of 454/Roche GS-FLX amplicon resequencing experiments using Perl and a relational database. The pipeline enables variation detection, variation detection validation, and advanced data analysis, which provides information that can be used to optimize PCR efficiency using traditional means. The modular approach enables customization of the pipeline where needed and allows researchers to adopt their analysis pipeline to their experiments. Clear documentation and training data is available to test and validate the pipeline prior to using it on real sequencing data. We designed an open-source database oriented pipeline that enables advanced analysis of 454/Roche GS-FLX amplicon resequencing experiments using SQL-statements. This modular database approach allows easy coupling with other pipeline modules such as variant interpretation or a LIMS system. There is also a set of standard reporting scripts available.

RT-qPCR-based quantification of small non-coding RNAs.

Abstract: MicroRNAs (miRNAs) are small non-coding RNA molecules that negatively regulate messenger RNA (mRNA) translation into protein. MiRNAs play a key role in gene expression regulation, and their involvement in disease biology is well documented. This has fueled the development of numerous tools for the quantification of miRNA expression levels. These tools are based on three technologies: (microarray) probe hybridization, RNA sequencing, and reverse transcription quantitative polymerase chain reaction (RT-qPCR). In this chapter, we describe a quantification system based on RT-qPCR technology, which is currently considered as the most sensitive, flexible, and accurate method for quantification of not only miRNA but also RNA expression in general. To this purpose, we have divided the protocol in three sections: reverse transcription (RT) reaction, optional preamplification (PA), and finally qPCR. Three quality-control (QC) steps are implemented in this workflow for assessment of RNA extraction efficiency, sample purity (e.g., absence of inhibitors), and inter-run variations, by examining the detection level of different spike-in synthetic miRNAs. We conclude by demonstrating raw data preprocessing and normalization using expression data obtained from high-throughput miRNA profiling of human RNA samples.

Performance assessment of total RNA sequencing of human biofluids and extracellular vesicles

Abstract: RNA profiling has emerged as a powerful tool to investigate the biomarker potential of human biofluids. However, despite enormous interest in extracellular nucleic acids, RNA sequencing methods to quantify the total RNA content outside cells are rare. Here, we evaluate the performance of the SMARTer Stranded Total RNA-Seq method in human platelet-rich plasma, platelet-free plasma, urine, conditioned medium, and extracellular vesicles (EVs) from these biofluids. We found the method to be accurate, precise, compatible with low-input volumes and able to quantify a few thousand genes. We picked up distinct classes of RNA molecules, including mRNA, lncRNA, circRNA, miscRNA and pseudogenes. Notably, the read distribution and gene content drastically differ among biofluids. In conclusion, we are the first to show that the SMARTer method can be used for unbiased unraveling of the complete transcriptome of a wide range of biofluids and their extracellular vesicles.

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.