QPCR: Application for real-time PCR data management and analysis

BACKGROUND: Since its introduction quantitative real-time polymerase chain reaction (qPCR) has become the standard method for quantification of gene expression. Its high sensitivity, large dynamic range, and accuracy led to the development of numerous applications with an increasing number of samples to be analyzed. Data analysis consists of a number of steps, which have to be carried out in several different applications. Currently, no single tool is available which incorporates storage, management, and multiple methods covering the complete analysis pipeline. RESULTS: QPCR is a versatile web-based Java application that allows to store, manage, and analyze data from relative quantification qPCR experiments. It comprises a parser to import generated data from qPCR instruments and includes a variety of analysis methods to calculate cycle-threshold and amplification efficiency values. The analysis pipeline includes technical and biological replicate handling, incorporation of sample or gene specific efficiency, normalization using single or multiple reference genes, inter-run calibration, and fold change calculation. Moreover, the application supports assessment of error propagation throughout all analysis steps and allows conducting statistical tests on biological replicates. Results can be visualized in customizable charts and exported for further investigation. CONCLUSION: We have developed a web-based system designed to enhance and facilitate the analysis of qPCR experiments. It covers the complete analysis workflow combining parsing, analysis, and generation of charts into one single application. The system is freely available a

TAMEE: data management and analysis for tissue microarrays

BACKGROUND: With the introduction of tissue microarrays (TMAs) researchers can investigate gene and protein expression in tissues on a high-throughput scale. TMAs generate a wealth of data calling for extended, high level data management. Enhanced data analysis and systematic data management are required for traceability and reproducibility of experiments and provision of results in a timely and reliable fashion. Robust and scalable applications have to be utilized, which allow secure data access, manipulation and evaluation for researchers from different laboratories. RESULTS: TAMEE (Tissue Array Management and Evaluation Environment) is a web-based database application for the management and analysis of data resulting from the production and application of TMAs. It facilitates storage of production and experimental parameters, of images generated throughout the TMA workflow, and of results from core evaluation. Database content consistency is achieved using structured classifications of parameters. This allows the extraction of high quality results for subsequent biologically-relevant data analyses. Tissue cores in the images of stained tissue sections are automatically located and extracted and can be evaluated using a set of predefined analysis algorithms. Additional evaluation algorithms can be easily integrated into the application via a plug-in interface. Downstream analysis of results is facilitated via a flexible query generator. CONCLUSION: We have developed an integrated system tailored to the specific needs of research projects using high density TMAs. It covers the complete workflow of TMA production, experimental use and subsequent analysis. The system is freely available for academic and non-profit institutions from

Translin and Trax differentially regulate telomere-associated transcript homeostasis

Translin and Trax proteins are highly conserved nucleic acid binding proteins that have been implicated in RNA regulation in a range of biological processes including tRNA processing, RNA interference, microRNA degradation during oncogenesis, spermatogenesis and neuronal regulation. Here, we explore the function of this paralogue pair of proteins in the fission yeast. Using transcript analysis we demonstrate a reciprocal mechanism for control of telomere-associated transcripts. Mutation of tfx1(+) (Trax) elevates transcript levels from silenced sub-telomeric regions of the genome, but not other silenced regions, such as the peri-centromeric heterochromatin. In the case of some sub-telomeric transcripts, but not all, this elevation is dependent on the Trax paralogue, Tsn1 (Translin). In a reciprocal fashion, Tsn1 (Translin) serves to repress levels of transcripts (TERRAs) from the telomeric repeats, whereas Tfx1 serves to maintain these elevated levels. This reveals a novel mechanism for the regulation of telomeric transcripts. We extend this to demonstrate that human Translin and Trax also control telomere-associated transcript levels in human cells in a telomere-specific fashion

Human germ/stem cell-specific gene TEX19 influences cancer cell proliferation and cancer prognosis

Table of cancer data sets analyzed. (DOCX 14 kb

MASPECTRAS: a platform for management and analysis of proteomics LC-MS/MS data

<p>Abstract</p> <p>Background</p> <p>The advancements of proteomics technologies have led to a rapid increase in the number, size and rate at which datasets are generated. Managing and extracting valuable information from such datasets requires the use of data management platforms and computational approaches.</p> <p>Results</p> <p>We have developed the MAss SPECTRometry Analysis System (MASPECTRAS), a platform for management and analysis of proteomics LC-MS/MS data. MASPECTRAS is based on the Proteome Experimental Data Repository (PEDRo) relational database schema and follows the guidelines of the Proteomics Standards Initiative (PSI). Analysis modules include: 1) import and parsing of the results from the search engines SEQUEST, Mascot, Spectrum Mill, X! Tandem, and OMSSA; 2) peptide validation, 3) clustering of proteins based on Markov Clustering and multiple alignments; and 4) quantification using the Automated Statistical Analysis of Protein Abundance Ratios algorithm (ASAPRatio). The system provides customizable data retrieval and visualization tools, as well as export to PRoteomics IDEntifications public repository (PRIDE). MASPECTRAS is freely available at <url>http://genome.tugraz.at/maspectras</url></p> <p>Conclusion</p> <p>Given the unique features and the flexibility due to the use of standard software technology, our platform represents significant advance and could be of great interest to the proteomics community.</p

Codi-strat - an interdisciplinary network geared towards sustainable management of chronic and infective diseases

A collaborative effort of clinicians, infectologists, molecular biologists, pharmacologists, veterinarians, bioinformaticians, management and education specialists is united in order to develop novel strategies of detecting early stages of chronic and infective diseases, their prevention and therapy. CODI-STRAT integrates 15 centers conducting leading–edge research of chronic inflammatory/infective diseases from seven European (five Mediterranean) countries and the USA, with specific aims to: i) establish long-standing partner center cross-disciplinary collaborations for clinical studies and research, ii) provide young investigators with broad and content-driven training and employability and iii) promote scientists up-skilled in genomics, transcriptomics, tissue expression, human serological and genetic studies, bioinformatics, chip technology, cell cultures and animal models, all directed toward clinical translation and chronic/infective disease management. This manuscript outlines the goals, partner roles and development of CODI-STRAT and its programme.peer-reviewe

GiSAO.db: a database for ageing research

<p>Abstract</p> <p>Background</p> <p>Age-related gene expression patterns of <it>Homo sapiens </it>as well as of model organisms such as <it>Mus musculus</it>, <it>Saccharomyces cerevisiae</it>, <it>Caenorhabditis elegans </it>and <it>Drosophila melanogaster </it>are a basis for understanding the genetic mechanisms of ageing. For an effective analysis and interpretation of expression profiles it is necessary to store and manage huge amounts of data in an organized way, so that these data can be accessed and processed easily.</p> <p>Description</p> <p>GiSAO.db (Genes involved in senescence, apoptosis and oxidative stress database) is a web-based database system for storing and retrieving ageing-related experimental data. Expression data of genes and miRNAs, annotation data like gene identifiers and GO terms, orthologs data and data of follow-up experiments are stored in the database. A user-friendly web application provides access to the stored data. KEGG pathways were incorporated and links to external databases augment the information in GiSAO.db. Search functions facilitate retrieval of data which can also be exported for further processing.</p> <p>Conclusions</p> <p>We have developed a centralized database that is very well suited for the management of data for ageing research. The database can be accessed at <url>https://gisao.genome.tugraz.at</url> and all the stored data can be viewed with a guest account.</p

Functional Categories Associated with Clusters of Genes That Are Co-Expressed across the NCI-60 Cancer Cell Lines

The NCI-60 is a panel of 60 diverse human cancer cell lines used by the U.S. National Cancer Institute to screen compounds for anticancer activity. In the current study, gene expression levels from five platforms were integrated to yield a single composite transcriptome profile. The comprehensive and reliable nature of that dataset allows us to study gene co-expression across cancer cell lines.Hierarchical clustering revealed numerous clusters of genes in which the genes co-vary across the NCI-60. To determine functional categorization associated with each cluster, we used the Gene Ontology (GO) Consortium database and the GoMiner tool. GO maps genes to hierarchically-organized biological process categories. GoMiner can leverage GO to perform ontological analyses of gene expression studies, generating a list of significant functional categories.GoMiner analysis revealed many clusters of coregulated genes that are associated with functional groupings of GO biological process categories. Notably, those categories arising from coherent co-expression groupings reflect cancer-related themes such as adhesion, cell migration, RNA splicing, immune response and signal transduction. Thus, these clusters demonstrate transcriptional coregulation of functionally-related genes