SV-Bay: structural variant detection in cancer genomes using a Bayesian approach with correction for GC-content and read mappability.

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 CONTACT: valentina.boeva@inserm.fr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of potential early biomarkers of preeclampsia.

INTRODUCTION: It is thought that poor placental perfusion caused by inadequate remodelling of the maternal spiral arteries leads to preeclampsia (PE). To identify novel signalling pathways that contribute to PE pathogenesis and to create prerequisites for the non-invasive diagnosis of PE before clinical manifestations of the disease, this study aimed to evaluate miRNA expression levels in the placenta and blood plasma of pregnant women. METHODS: miRNA deep sequencing followed by real-time quantitative RT-PCR was applied to compare miRNA expression profiles in the placenta and blood plasma from women with early- and late-onset PE relative to the control group. RESULTS: A more than two-fold decrease in miR-532-5p, -423-5p, -127-3p, -539-5p, -519a-3p, and -629-5p and let-7c-5p expression levels was observed in the placenta, while a more than two-fold increase in miR-423-5p, 519a-3p, and -629-5p and let-7c-5p was observed in the blood plasma of pregnant women with PE. The above-listed miRNAs are associated with PE for the first time in this study, except for miR-519a-3p, whose role in PE has already been postulated. Using a logistic regression, plasma samples were classified into the early-onset PE group (probability p = 0.01, 80% specificity, 87.5% sensitivity and 87.5% precision) and showed increased miR-423-5p expression levels that were confirmed by the 9.8-fold up-regulation (р = 0.0002498) of miR-423-5p expression observed in the blood plasma at 11-13 GW by RT-PCR in a group of pregnant women manifesting severe PE clinical signs at 28-33 GW. CONCLUSIONS: miR-423-5p may be considered a potential candidate for the early diagnosis of PE during the targeted management of high-risk pregnancies.