51 research outputs found

    RGG: A general GUI Framework for R scripts

    Get PDF
    <p>Abstract</p> <p>Background</p> <p>R is the leading open source statistics software with a vast number of biostatistical and bioinformatical analysis packages. To exploit the advantages of R, extensive scripting/programming skills are required.</p> <p>Results</p> <p>We have developed a software tool called R GUI Generator (RGG) which enables the easy generation of Graphical User Interfaces (GUIs) for the programming language R by adding a few Extensible Markup Language (XML) – tags. RGG consists of an XML-based GUI definition language and a Java-based GUI engine. GUIs are generated in runtime from defined GUI tags that are embedded into the R script. User-GUI input is returned to the R code and replaces the XML-tags. RGG files can be developed using any text editor. The current version of RGG is available as a stand-alone software (RGGRunner) and as a plug-in for JGR.</p> <p>Conclusion</p> <p>RGG is a general GUI framework for R that has the potential to introduce R statistics (R packages, built-in functions and scripts) to users with limited programming skills and helps to bridge the gap between R developers and GUI-dependent users. RGG aims to abstract the GUI development from individual GUI toolkits by using an XML-based GUI definition language. Thus RGG can be easily integrated in any software. The RGG project further includes the development of a web-based repository for RGG-GUIs. RGG is an open source project licensed under the Lesser General Public License (LGPL) and can be downloaded freely at <url>http://rgg.r-forge.r-project.org</url></p

    Identification of human pathogens isolated from blood using microarray hybridisation and signal pattern recognition

    Get PDF
    <p>Abstract</p> <p>Background</p> <p>Pathogen identification in clinical routine is based on the cultivation of microbes with subsequent morphological and physiological characterisation lasting at least 24 hours. However, early and accurate identification is a crucial requisite for fast and optimally targeted antimicrobial treatment. Molecular biology based techniques allow fast identification, however discrimination of very closely related species remains still difficult.</p> <p>Results</p> <p>A molecular approach is presented for the rapid identification of pathogens combining PCR amplification with microarray detection. The DNA chip comprises oligonucleotide capture probes for 25 different pathogens including Gram positive cocci, the most frequently encountered genera of <it>Enterobacteriaceae</it>, non-fermenter and clinical relevant <it>Candida </it>species. The observed detection limits varied from 10 cells (e.g. <it>E. coli</it>) to 10<sup>5 </sup>cells (<it>S. aureus</it>) per mL artificially spiked blood. Thus the current low sensitivity for some species still represents a barrier for clinical application. Successful discrimination of closely related species was achieved by a signal pattern recognition approach based on the k-nearest-neighbour method. A prototype software providing this statistical evaluation was developed, allowing correct identification in 100 % of the cases at the genus and in 96.7 % at the species level (n = 241).</p> <p>Conclusion</p> <p>The newly developed molecular assay can be carried out within 6 hours in a research laboratory from pathogen isolation to species identification. From our results we conclude that DNA microarrays can be a useful tool for rapid identification of closely related pathogens particularly when the protocols are adapted to the special clinical scenarios.</p

    In silico design and performance of peptide microarrays for breast cancer tumour-auto-antibody testing

    Get PDF
    The simplicity and potential of minimally invasive testing using sera from patients makes auto-antibody based biomarkers a very promising tool for use in cancer diagnostics. Protein microarrays have been used for the identification of such auto-antibody signatures. Because high throughput protein expression and purification is laborious, synthetic peptides might be a good alternative for microarray generation and multiplexed analyses. In this study, we designed 1185 antigenic peptides, deduced from proteins expressed by 642 cDNA expression clones found to be sero-reactive in both breast tumour patients and controls. The sero-reactive proteins and the corresponding peptides were used for the production of protein and peptide microarrays. Serum samples from females with benign and malignant breast tumours and healthy control sera (n=16 per group) were then analysed. Correct classification of the serum samples on peptide microarrays were 78% for discrimination of ‘malignant versus healthy controls’, 72% for ‘benign versus malignant’ and 94% for ‘benign versus controls’. On protein arrays, correct classification for these contrasts was 69%, 59% and 59%, respectively. The over-representation analysis of the classifiers derived from class prediction showed enrichment of genes associated with ribosomes, spliceosomes, endocytosis and the pentose phosphate pathway. Sequence analyses of the peptides with the highest sero-reactivity demonstrated enrichment of the zinc-finger domain. Peptides’ sero-reactivities were found negatively correlated with hydrophobicity and positively correlated with positive charge, high inter-residue protein contact energies and a secondary structure propensity bias. This study hints at the possibility of using in silico designed antigenic peptide microarrays as an alternative to protein microarrays for the improvement of tumour auto-antibody based diagnostics

    Evaluation of auto-antibody serum biomarkers for breast cancer screening and in silico analysis of sero-reactive proteins

    Get PDF
    Aberrantly expressed proteins in tumours evoke an immunological response. These immunogenic proteins can serve as potential biomarkers for the early diagnosis of cancers. In this study, we performed a candidate marker screen on macroarrays containing 38,016 human proteins, derived from a human fetal-brain expression library, with the pools of sera from breast cancer patients (1 pool of benign samples, 3 pools of ductal carcinoma and 2 pools of lobular carcinoma) and 1 pool of sera from healthy women. A panel of 642 sero-reactive clones were deduced from these macroarray experiments which include 284 in-frame clones. Over-representation analyses of the sero-reactive in-frame clones enabled the identification of the sets of genes over-expressed in various pathways of the functional categories (KEGG, Transpath, Pfam and GO). Protein microarrays, generated using the His-tag proteins derived from the macroarray experiments, were used to evaluate the sera from breast cancer patients (24 malignant, 16 benign) and 20 control individuals. Using the PAM algorithm we elucidated a panel of 50 clones which enabled the correct classification prediction of 93% of the breast-nodule positive group (benign &amp; malignant) sera from healthy individuals’ sera with 100% sensitivity and 85% specificity. This was followed by over-representation analysis of the significant clones derived from the class prediction

    Transcriptomal analysis of varicella-zoster virus infection using long oligonucleotide-based microarrays

    Get PDF
    Varicella-zoster virus (VZV) is a human herpes virus that causes varicella as a primary infection and herpes zoster following reactivation of the virus from a latent state in trigeminal and spinal ganglia. In order to study the global pattern of VZV gene transcription, VZV microarrays using 75-base oligomers to 71 VZV open reading frames (ORFs) were designed and validated. The long-oligonucleotide approach maximizes the stringency of detection and polarity of gene expression. To optimize sensitivity, microarrays were hybridized to target RNA and the extent of hybridization measured using resonance light scattering. Microarray data were normalized to a subset of invariant ranked host-encoded positive-control genes and the data subjected to robust formal statistical analysis. The programme of viral gene expression was determined for VZV (Dumas strain)-infected MeWo cells and SVG cells (an immortalized human astrocyte cell line) 72 h post-infection. Marked quantitative and qualitative differences in the viral transcriptome were observed between the two different cell types using the Dumas laboratory-adapted strain. Oligonucleotide-based VZV arrays have considerable promise as a valuable tool in the analysis of viral gene transcription during both lytic and latent infections, and the observed heterogeneity in the global pattern of viral gene transcription may also have diagnostic potentia

    Identification of SERPINA1 as single marker for papillary thyroid carcinoma through microarray meta analysis and quantification of its discriminatory power in independent validation

    Get PDF
    <p>Abstract</p> <p>Background</p> <p>Several DNA microarray based expression signatures for the different clinically relevant thyroid tumor entities have been described over the past few years. However, reproducibility of these signatures is generally low, mainly due to study biases, small sample sizes and the highly multivariate nature of microarrays. While there are new technologies available for a more accurate high throughput expression analysis, we show that there is still a lot of information to be gained from data deposited in public microarray databases. In this study we were aiming (1) to identify potential markers for papillary thyroid carcinomas through meta analysis of public microarray data and (2) to confirm these markers in an independent dataset using an independent technology.</p> <p>Methods</p> <p>We adopted a meta analysis approach for four publicly available microarray datasets on papillary thyroid carcinoma (PTC) nodules versus nodular goitre (NG) from N2-frozen tissue. The methodology included merging of datasets, bias removal using distance weighted discrimination (DWD), feature selection/inference statistics, classification/crossvalidation and gene set enrichment analysis (GSEA). External Validation was performed on an independent dataset using an independent technology, quantitative RT-PCR (RT-qPCR) in our laboratory.</p> <p>Results</p> <p>From meta analysis we identified one gene (SERPINA1) which identifies papillary thyroid carcinoma against benign nodules with 99% accuracy (n = 99, sensitivity = 0.98, specificity = 1, PPV = 1, NPV = 0.98). In the independent validation data, which included not only PTC and NG, but all major histological thyroid entities plus a few variants, SERPINA1 was again markedly up regulated (36-fold, p = 1:3*10<sup>-10</sup>) in PTC and identification of papillary carcinoma was possible with 93% accuracy (n = 82, sensitivity = 1, specificity = 0.90, PPV = 0.76, NPV = 1). We also show that the extracellular matrix pathway is strongly activated in the meta analysis data, suggesting an important role of tumor-stroma interaction in the carcinogenesis of papillary thyroid carcinoma.</p> <p>Conclusions</p> <p>We show that valuable new information can be gained from meta analysis of existing microarray data deposited in public repositories. While single microarray studies rarely exhibit a sample number which allows robust feature selection, this can be achieved by combining published data using DWD. This approach is not only efficient, but also very cost-effective. Independent validation shows the validity of the results from this meta analysis and confirms SERPINA1 as a potent mRNA marker for PTC in a total (meta analysis plus validation) of 181 samples.</p

    A survey of tools for the analysis of quantitative PCR (qPCR) data

    Get PDF
    Real-time quantitative polymerase-chain-reaction (qPCR) is a standard technique in most laboratories used for various applications in basic research. Analysis of qPCR data is a crucial part of the entire experiment, which has led to the development of a plethora of methods. The released tools either cover specific parts of the workflow or provide complete analysis solutions. Here, we surveyed 27 open-access software packages and tools for the analysis of qPCR data. The survey includes 8 Microsoft Windows, 5 web-based, 9 R-based and 5 tools from other platforms. Reviewed packages and tools support the analysis of different qPCR applications, such as RNA quantification, DNA methylation, genotyping, identification of copy number variations, and digital PCR. We report an overview of the functionality, features and specific requirements of the individual software tools, such as data exchange formats, availability of a graphical user interface, included procedures for graphical data presentation, and offered statistical methods. In addition, we provide an overview about quantification strategies, and report various applications of qPCR. Our comprehensive survey showed that most tools use their own file format and only a fraction of the currently existing tools support the standardized data exchange format RDML. To allow a more streamlined and comparable analysis of qPCR data, more vendors and tools need to adapt the standardized format to encourage the exchange of data between instrument software, analysis tools, and researchers
    corecore