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Background The specific characteristics of copy number variations (CNVs) require specific methods of detection and characterization. We developed the Easy One-Step Amplification and Labeling procedure for CNV detection (EOSAL-CNV), a new method based on proportional amplification and labeling of amplicons in 1 PCR. Methods We used tailed primers for specific amplification and a pair of labeling probes (only 1 labeled) for amplification and labeling of all amplicons in just 1 reaction. Products were loaded directly onto a capillary DNA sequencer for fragment sizing and quantification. Data obtained could be analyzed by Microsoft Excel spreadsheet or EOSAL-CNV analysis software. We developed the protocol using the LDLR (low density lipoprotein receptor) gene including 23 samples with 8 different CNVs. After optimizing the protocol, it was used for genes in the following multiplexes: BRCA1 (BRCA1 DNA repair associated), BRCA2 (BRCA2 DNA repair associated), CHEK2 (checkpoint kinase 2), MLH1 (mutL homolog 1) plus MSH6 (mutS homolog 6), MSH2 (mutS homolog 2) plus EPCAM (epithelial cell adhesion molecule) and chromosome 17 (especially the TP53 [tumor protein 53] gene). We compared our procedure with multiplex ligation-dependent probe amplification (MLPA). Results The simple procedure for CNV detection required 150 min, with 240 samples, EOSAL-CNV excluded the presence of CNVs in all controls, and in all cases, results were identical using MLPA and EOSAL-CNV. Analysis of the 17p region in tumor samples showed 100% similarity between fluorescent in situ hybridization and EOSAL-CNV. Conclusions EOSAL-CNV allowed reliable, fast, easy detection and characterization of CNVs. It provides an alternative to targeted analysis methods such as MLPA.

The possibility of integrating viral vectors to become a persistent part of the host genome makes them a crucial element of clinical gene therapy. However, viral integration has associated risks, such as the unintentional activation of oncogenes that can result in cancer. Therefore, the analysis of integration sites of retroviral vectors is a crucial step in developing safer vectors for therapeutic use. Here we present VISMapper, a vector integration site analysis web server, to analyze next-generation sequencing data for retroviral vector integration sites. VISMapper can be found at: http://vismapper.babelomics.org . Because it uses novel mapping algorithms VISMapper is remarkably faster than previous available programs. It also provides a useful graphical interface to analyze the integration sites found in the genomic context.

Genomic technologies allow laboratories to produce large-scale data sets, either through the use of next-generation sequencing or microarray platforms. To explore these data sets and obtain maximum value from the data, researchers view their results alongside all the known features of a given reference genome. To study transcriptional changes that occur under a given condition, researchers search for regions of the genome that are differentially expressed between different experimental conditions. In order to identify these regions several algorithms have been developed over the years, along with some bioinformatic platforms that enable their use. However, currently available applications for comparative microarray analysis exclusively focus on changes in gene expression within known transcribed regions of predicted protein-coding genes, the changes that occur in non-predictable genetic elements, such as non-coding RNAs. Here, we present a web application for the visualization of strand-specific tiling microarray or next-generation sequencing data that allows customized detection of differentially expressed regions all along the genome in an unspecific manner, that allows identification of all RNA sequences, predictable or not. Availability and implementation: The web application is freely accessible at http://tilingscan.uv.es/. TilingScan is implemented in PHP and JavaScript. Contact: vicente.arnau@uv.es Supplementary information: Supplementary data are available at Bioinformatics online.