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Motivation: Whole genome sequencing of paired-end reads can be applied to characterize the landscape of large somatic rearrangements of cancer genomes. Several methods for detecting structural variants with whole genome sequencing data have been developed. So far, none of these methods has combined information about abnormally mapped read pairs connecting rearranged regions and associated global copy number changes automatically inferred from the same sequencing data file. Our aim was to create a computational method that could use both types of information, i.e. normal and abnormal reads, and demonstrate that by doing so we can highly improve both sensitivity and specificity rates of structural variant prediction. Results: We developed a computational method, SV-Bay, to detect structural variants from whole genome sequencing mate-pair or paired-end data using a probabilistic Bayesian approach. This approach takes into account depth of coverage by normal reads and abnormalities in read pair mappings. To estimate the model likelihood, SV-Bay considers GC-content and read mappability of the genome, thus making important corrections to the expected read count. For the detection of somatic variants, SV-Bay makes use of a matched normal sample when it is available. We validated SV-Bay on simulated datasets and an experimental mate-pair dataset for the CLB-GA neuroblastoma cell line. The comparison of SV-Bay with several other methods for structural variant detection demonstrated that SV-Bay has better prediction accuracy both in terms of sensitivity and false-positive detection rate. Availability and implementation: https://github.com/InstitutCurie/SV-Bay. [ABSTRACT FROM AUTHOR]

Background The Sonic Hedgehog (SHH) signaling pathway plays an important role in neural crest cell fate during embryonic development and has been implicated in the progression of multiple cancers that include neuroblastoma, a neural crest cell-derived disease. While most of the SHH signaling is mediated by the well-described canonical pathway leading to the activation of Smoothened and Gli, it has recently been shown that cell-adhesion molecule-related/downregulated by oncogenes (CDON) serves as a receptor for SHH and contributes to SHH-induced signaling. CDON has also been recently described as a dependence receptor, triggering apoptosis in the absence of SHH. This CDON proapoptotic activity has been suggested to constrain tumor progression. Methods CDON expression was analyzed by quantitative-reverse transcription-polymerase chain reaction in a panel of 226 neuroblastoma patients and associated with stages, overall survival, and expression of miR181 family members using Kaplan Meier and Pearson correlation methods. Cell death assays were performed in neuroblastoma cell lines and tumor growth was investigated in the chick chorioallantoic model. All statistical tests were two-sided. Results CDON expression was inversely associated with neuroblastoma aggressiveness (P<.001). Moreover, re-expression of CDON in neuroblastoma cell lines was associated with apoptosis in vitro and tumor growth inhibition in vivo. We show that CDON expression is regulated by the miR181 miRNA family, whose expression is directly associated with neuroblastoma aggressiveness (survival: high miR181-b 73.2% vs low miR181-b 54.6%; P = .03). Conclusions Together, these data support the view that CDON acts as a tumor suppressor in neuroblastomas, and that CDON is tightly regulated by miRNAs. [ABSTRACT FROM AUTHOR]

Motivation: DNA copy number profiles characterize regions of chromosome gains, losses and breakpoints in tumor genomes. Although many models have been proposed to detect these alterations, it is not clear which model is appropriate before visual inspection the signal, noise and models for a particular profile.Results: We propose SegAnnDB, a Web-based computer vision system for genomic segmentation: first, visually inspect the profiles and manually annotate altered regions, then SegAnnDB determines the precise alteration locations using a mathematical model of the data and annotations. SegAnnDB facilitates collaboration between biologists and bioinformaticians, and uses the University of California, Santa Cruz genome browser to visualize copy number alterations alongside known genes.Availability and implementation: The breakpoints project on INRIA GForge hosts the source code, an Amazon Machine Image can be launched and a demonstration Web site is http://bioviz.rocq.inria.fr.Contact: toby@sg.cs.titech.ac.jpSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Summary: More and more cancer studies use next-generation sequencing (NGS) data to detect various types of genomic variation. However, even when researchers have such data at hand, single-nucleotide polymorphism arrays have been considered necessary to assess copy number alterations and especially loss of heterozygosity (LOH). Here, we present the tool Control-FREEC that enables automatic calculation of copy number and allelic content profiles from NGS data, and consequently predicts regions of genomic alteration such as gains, losses and LOH. Taking as input aligned reads, Control-FREEC constructs copy number and B-allele frequency profiles. The profiles are then normalized, segmented and analyzed in order to assign genotype status (copy number and allelic content) to each genomic region. When a matched normal sample is provided, Control-FREEC discriminates somatic from germline events. Control-FREEC is able to analyze overdiploid tumor samples and samples contaminated by normal cells. Low mappability regions can be excluded from the analysis using provided mappability tracks.Availability: C++ source code is available at: http://bioinfo.curie.fr/projects/freec/Contact: freec@curie.frSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Summary: We present a tool for control-free copy number alteration (CNA) detection using deep-sequencing data, particularly useful for cancer studies. The tool deals with two frequent problems in the analysis of cancer deep-sequencing data: absence of control sample and possible polyploidy of cancer cells. FREEC (control-FREE Copy number caller) automatically normalizes and segments copy number profiles (CNPs) and calls CNAs. If ploidy is known, FREEC assigns absolute copy number to each predicted CNA. To normalize raw CNPs, the user can provide a control dataset if available; otherwise GC content is used. We demonstrate that for Illumina single-end, mate-pair or paired-end sequencing, GC-contentr normalization provides smooth profiles that can be further segmented and analyzed in order to predict CNAs.Availability: Source code and sample data are available at http://bioinfo-out.curie.fr/projects/freec/.Contact: freec@curie.frSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Summary: We present SVDetect, a program designed to identify genomic structural variations from paired-end and mate-pair next-generation sequencing data produced by the Illumina GA and ABI SOLiD platforms. Applying both sliding-window and clustering strategies, we use anomalously mapped read pairs provided by current short read aligners to localize genomic rearrangements and classify them according to their type, e.g. large insertions–deletions, inversions, duplications and balanced or unbalanced inter-chromosomal translocations. SVDetect outputs predicted structural variants in various file formats for appropriate graphical visualization. [ABSTRACT FROM PUBLISHER]