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Supplementary Figures 1-3 from Identification of Differentially Regulated Splice Variants and Novel Exons in Glial Brain Tumors Using Exon Expression Arrays

Supplementary Table 3 from Identification of Differentially Regulated Splice Variants and Novel Exons in Glial Brain Tumors Using Exon Expression Arrays

Supplementary Table 1 from Identification of Differentially Regulated Splice Variants and Novel Exons in Glial Brain Tumors Using Exon Expression Arrays

Aberrant splice variants are involved in the initiation and/or progression of glial brain tumors. We therefore set out to identify splice variants that are differentially expressed between histologic subgroups of gliomas. Splice variants were identified using a novel platform that profiles the expression of virtually all known and predicted exons present in the human genome. Exon-level expression profiling was done on 26 glioblastomas, 22 oligodendrogliomas, and 6 control brain samples. Our results show that Human Exon arrays can identify subgroups of gliomas based on their histologic appearance and genetic aberrations. We next used our expression data to identify differentially expressed splice variants. In two independent approaches, we identified 49 and up to 459 exons that are differentially spliced between glioblastomas and oligodendrogliomas, a subset of which (47% and 33%) were confirmed by reverse transcription-PCR (RT-PCR). In addition, exon level expression profiling also identified >700 novel exons. Expression of ∼67% of these candidate novel exons was confirmed by RT-PCR. Our results indicate that exon level expression profiling can be used to molecularly classify brain tumor subgroups, can identify differentially regulated splice variants, and can identify novel exons. The splice variants identified by exon level expression profiling may help to detect the genetic changes that cause or maintain gliomas and may serve as novel treatment targets. [Cancer Res 2007;67(12):5635–8]

Supplementary Table 2 from Identification of Differentially Regulated Splice Variants and Novel Exons in Glial Brain Tumors Using Exon Expression Arrays

Aim Variations in treatment are the result of differences in demographic and clinical factors (e.g. anatomy), but physician and hospital factors may also contribute to treatment variation. The choice of treatment is considered important since it could lead to differences in long-term outcomes. This study explores the associations with stent choice: i.e. drug-eluting stent (DES) versus bare-metal stents (BMS) for Dutch patients diagnosed with stable or unstable coronary artery disease (CAD). Methods & results Associations with treatment decisions were based on a prospective cohort of 692 patients with stable or unstable CAD. Of those patients, 442 patients were treated with BMS or DES. Multiple logistic regression analyses were performed to identify variables associated with stent choice. Bivariate analyses showed that NYHA class, number of diseased vessels, previous percutaneous coronary intervention, smoking, diabetes, and the treating hospital were associated with stent type. After correcting for other associations the treating hospital remained significantly associated with stent type in the stable CAD population. Conclusions This study showed that several factors were associated with stent choice. While patients generally appear to receive the most optimal stent given their clinical characteristics, stent choice seems partially determined by the treating hospital, which may lead to differences in longterm outcomes.

Biomarkers for primary or secondary risk prediction of cardiovascular disease (CVD) are urgently needed to improve individual treatment and clinical trial design. The vast majority of biomarker discovery studies has concentrated on plasma/serum as an easily accessible source. Although numerous markers have been identified, their added predictive value on top of traditional risk factors has been limited, as the biological specimen does not specifically reflect expression profiles related with CVD progression and because the signal is often diluted by marker release from other organs. In contrast to serum markers, circulating cells serve as indicators of the actual disease state due to their active role in the pathogenesis of CVD and are responsible for the majority of secreted biomarkers. Therefore, the CIRCULATING CELLS study was initiated, focusing on the cellular effectors of atherosclerosis in the circulation. In total, 714 patients with coronary artery disease (CAD) symptoms were included. Blood cell fractions (monocytes, T-lymphocytes, platelets, granulocytes, PBMC) of all individual patients were isolated and stored for analysis. Concomitantly, extensive flow cytometric characterization of these populations was performed. From each patient, a detailed clinical profile together with extensive questionnaires about medical history and life style was obtained. Various high-throughput -omics approaches (protein, mRNA, miRNA) are currently being undertaken. Data will be integrated with advanced bioinformatics for discovery and validation of secondary risk markers for adverse events. Overall, the CIRCULATING CELLS study grants the interesting possibility that it will both identify novel biomarkers and provide useful insights into the pathophysiology of CAD in patients.

Aberrant splice variants are involved in the initiation and/or progression of glial brain tumors. We therefore set out to identify splice variants that are differentially expressed between histologic subgroups of gliomas. Splice variants were identified using a novel platform that profiles the expression of virtually all known and predicted exons present in the human genome. Exon-level expression profiling was done on 26 glioblastomas, 22 oligodendrogliomas, and 6 control brain samples. Our results show that Human Exon arrays can identify subgroups of gliomas based on their histologic appearance and genetic aberrations. We next used our expression data to identify differentially expressed splice variants. In two independent approaches, we identified 49 and up to 459 exons that are differentially spliced between glioblastomas and oligodendrogliomas, a subset of which (47% and 33%) were confirmed by reverse transcription-PCR (RT-PCR). In addition, exon level expression profiling also identified >700 novel exons. Expression of ∼67% of these candidate novel exons was confirmed by RT-PCR. Our results indicate that exon level expression profiling can be used to molecularly classify brain tumor subgroups, can identify differentially regulated splice variants, and can identify novel exons. The splice variants identified by exon level expression profiling may help to detect the genetic changes that cause or maintain gliomas and may serve as novel treatment targets. [Cancer Res 2007;67(12):5635–8]

Introducing therapeutic genes into hematopoietic stem cells using retroviral vector-mediated gene transfer is an effective treatment for monogenic diseases. The risks of therapeutic gene integration include aberrant expression of a neighboring gene, resulting in oncogenesis at low frequencies (10(-7)-10(-6)/transduced cell). Mechanisms governing insertional mutagenesis are the subject of intensive ongoing studies that produce large amounts of sequencing data representing genomic regions flanking viral integration sites (IS). Validating and analyzing these data require automated bioinformatics applications. The exact methods used vary between applications, based on the requirements and preferences of the designer. The parameters used to analyze sequence data are capable of shaping the resulting integration site annotations, but a comprehensive examination of these effects is lacking. Here we present a web-based tool for integration site analysis, called Methods for Analyzing ViRal Integration Collections (MAVRIC), and use its highly customizable interface to look at how IS annotations can vary based on the analysis parameters. We used the integration data of the previously published adenosine deaminase severe combined immunodeficiency (ADA-SCID) gene therapy trials for evaluation of MAVRIC. The output illustrates how MAVRIC allows for direct multiparameter comparison of integration patterns. Careful analysis of the SCID data and reanalyses using different parameters for trimming, alignment, and repeat masking revealed the degree of variation that can be expected to arise due to changes in these parameters. We observed mainly small differences in annotation, with the largest effects caused by masking repeat sequences and by changing the size of the window around the IS.

Background Next generation sequencing provides clinical research scientists with direct read out of innumerable variants, including personal, pathological and common benign variants. The aim of resequencing studies is to determine the candidate pathogenic variants from individual genomes, or from family-based or tumor/normal genome comparisons. Whilst the use of appropriate controls within the experimental design will minimize the number of false positive variations selected, this number can be reduced further with the use of high quality whole genome reference data to minimize false positives variants prior to candidate gene selection. In addition the use of platform related sequencing error models can help in the recovery of ambiguous genotypes from lower coverage data. Description We have developed a whole genome database of human genetic variations, Huvariome, determined by whole genome deep sequencing data with high coverage and low error rates. The database was designed to be sequencing technology independent but is currently populated with 165 individual whole genomes consisting of small pedigrees and matched tumor/normal samples sequenced with the Complete Genomics sequencing platform. Common variants have been determined for a Benelux population cohort and represented as genotypes alongside the results of two sets of control data (73 of the 165 genomes), Huvariome Core which comprises 31 healthy individuals from the Benelux region, and Diversity Panel consisting of 46 healthy individuals representing 10 different populations and 21 samples in three Pedigrees. Users can query the database by gene or position via a web interface and the results are displayed as the frequency of the variations as detected in the datasets. We demonstrate that Huvariome can provide accurate reference allele frequencies to disambiguate sequencing inconsistencies produced in resequencing experiments. Huvariome has been used to support the selection of candidate cardiomyopathy related genes which have a homozygous genotype in the reference cohorts. This database allows the users to see which selected variants are common variants (> 5% minor allele frequency) in the Huvariome core samples, thus aiding in the selection of potentially pathogenic variants by filtering out common variants that are not listed in one of the other public genomic variation databases. The no-call rate and the accuracy of allele calling in Huvariome provides the user with the possibility of identifying platform dependent errors associated with specific regions of the human genome. Conclusion Huvariome is a simple to use resource for validation of resequencing results obtained by NGS experiments. The high sequence coverage and low error rates provide scientists with the ability to remove false positive results from pedigree studies. Results are returned via a web interface that displays location-based genetic variation frequency, impact on protein function, association with known genetic variations and a quality score of the variation base derived from Huvariome Core and the Diversity Panel data. These results may be used to identify and prioritize rare variants that, for example, might be disease relevant. In testing the accuracy of the Huvariome database, alleles of a selection of ambiguously called coding single nucleotide variants were successfully predicted in all cases. Data protection of individuals is ensured by restricted access to patient derived genomes from the host institution which is relevant for future molecular diagnostics.

BackgroundAccurate analyses of comprehensive genome-wide SNP genotyping and gene expression data sets is challenging for many researchers. In fact, obtaining an integrated view of both large scale SNP genotyping and gene expression is currently complicated since only a limited number of appropriate software tools are available.ResultsWe present SNPExpress, a software tool to accurately analyze Affymetrix and Illumina SNP genotype calls, copy numbers, polymorphic copy number variations (CNVs) and Affymetrix gene expression in a combinatorial and efficient way. In addition, SNPExpress allows concurrent interpretation of these items with Hidden-Markov Model (HMM) inferred Loss-of-Heterozygosity (LOH)- and copy number regions.ConclusionThe combined analyses with the easily accessible software tool SNPExpress will not only facilitate the recognition of recurrent genetic lesions, but also the identification of critical pathogenic genes.

Background In the current era of high throughput genomics a major challenge is the genome-wide identification of target genes for specific transcription factors. Chromatin immunoprecipitation (ChIP) allows the isolation of in vivo binding sites of transcription factors and provides a powerful tool for examining gene regulation. Crosslinked chromatin is immunoprecipitated with antibodies against specific transcription factors, thus enriching for sequences bound in vivo by these factors in the immunoprecipitated DNA. Cloning and sequencing the immunoprecipitated sequences allows identification of transcription factor target genes. Routinely, thousands of such sequenced clones are used in BLAST searches to map their exact location in the genome and the genes located in the vicinity. These genes represent potential targets of the transcription factor of interest. Such bioinformatics analysis is very laborious if performed manually and for this reason there is a need for developing bioinformatic tools to automate and facilitate it. Results In order to facilitate this analysis we generated TF Target Mapper (T ranscription F actor Target Mapper). TF Target Mapper is a BLAST search tool allowing rapid extraction of annotated information on genes around each hit. It combines sequence cleaning/filtering, pattern searching and BLAST searches with extraction of information on genes located around each BLAST hit and comparisons of the output list of genes or gene ontology IDs with user-implemented lists. We successfully applied and tested TF Target Mapper to analyse sequences bound in vivo by the transcription factor GATA-1. We show that TF Target Mapper efficiently extracted information on genes around ChIPed sequences, thus identifying known (e.g. α-globin and ζ-globin) and potentially novel GATA-1 gene targets. Conclusion TF Target Mapper is a very efficient BLAST search tool that allows the rapid extraction of annotated information on the genes around each hit. It can contribute to the comprehensive bioinformatic transcriptome/regulome analysis, by providing insight into the mechanisms of action of specific transcription factors, thus helping to elucidate the pathways these factors regulate.

Background Accurate interpretation of data obtained by unsupervised analysis of large scale expression profiling studies is currently frequently performed by visually combining sample-gene heatmaps and sample characteristics. This method is not optimal for comparing individual samples or groups of samples. Here, we describe an approach to visually integrate the results of unsupervised and supervised cluster analysis using a correlation plot and additional sample metadata. Results We have developed a tool called the HeatMapper that provides such visualizations in a dynamic and flexible manner and is available from http://www.erasmusmc.nl/hematologie/heatmapper/. Conclusion The HeatMapper allows an accessible and comprehensive visualization of the results of gene expression profiling and cluster analysis.

Summary: We present iFUSE (integrated fusion gene explorer), an online visualization tool that provides a fast and informative view of structural variation data and prioritizes those breaks likely representing fusion genes. This application uses calculated break points to determine fusion genes based on the latest annotation for genomic sequence information, and where relevant the structural variation (SV) events are annotated with predicted RNA and protein sequences. iFUSE takes as input a Complete Genomics (CG) junction file, a FusionMap fusion detection report file or a file already analysed and annotated by the iFUSE application on a previous occasion. Results: We demonstrate the use of iFUSE with case studies from tumour-normal SV detection derived from Complete Genomics wholegenome sequencing results. Availability: iFUSE is available as a web service at http://ifuse.eras musmc.nl.