Detecting common copy number variants in high-throughput sequencing data by using JointSLM algorithm

The discovery of genomic structural variants (SVs), such as copy number variants (CNVs), is essential to understand genetic variation of human populations and complex diseases. Over recent years, the advent of new high-throughput sequencing (HTS) platforms has opened many opportunities for SVs discovery, and a very promising approach consists in measuring the depth of coverage (DOC) of reads aligned to the human reference genome. At present, few computational methods have been developed for the analysis of DOC data and all of these methods allow to analyse only one sample at time. For these reasons, we developed a novel algorithm (JointSLM) that allows to detect common CNVs among individuals by analysing DOC data from multiple samples simultaneously. We test JointSLM performance on synthetic and real data and we show its unprecedented resolution that enables the detection of recurrent CNV regions as small as 500 bp in size. When we apply JointSLM to analyse chromosome one of eight genomes with different ancestry, we identify 3000 regions with recurrent CNVs of different frequency and size: hierarchical clustering on these regions segregates the eight individuals in two groups that reflect their ancestry, demonstrating the potential utility of JointSLM for population genetics studies

Discovering chimeric transcripts in paired-end RNA-seq data by using EricScript

Abstract Motivation: The discovery of novel gene fusions can lead to a better comprehension of cancer progression and development. The emergence of deep sequencing of trancriptome, known as RNA-seq, has opened many opportunities for the identification of this class of genomic alterations, leading to the discovery of novel chimeric transcripts in melanomas, breast cancers and lymphomas. Nowadays, few computational approaches have been developed for the detection of chimeric transcripts. Although all of these computational methods show good sensitivity, much work remains to reduce the huge number of false-positive calls that arises from this analysis. Results: We proposed a novel computational framework, named chimEric tranScript detection algorithm (EricScript), for the identification of gene fusion products in paired-end RNA-seq data. Our simulation study on synthetic data demonstrates that EricScript enables to achieve higher sensitivity and specificity than existing methods with noticeably lower running times. We also applied our method to publicly available RNA-seq tumour datasets, and we showed its capability in rediscovering known gene fusions. Availability: The EricScript package is freely available under GPL v3 license at http://ericscript.sourceforge.net. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

Charting differentially methylated regions in cancer with Rocker-meth

Matteo Benelli et al. present Rocker-meth, a new Hidden Markov Model (HMM)-based method, to robustly identify differentially methylated regions (DMRs). They use Rocker-meth to analyse more than 6000 methylation profiles across 14 cancer types, providing a catalog of tumor-specific and shared DMRs

EX-HOM (EXome HOMozygosity): A Proof of Principle

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Distribuição espacial de cárie dentária em crianças pré-escolares de Canoas, sul do Brasil

The aims of this study were to analyze the spatial distribution of dental caries among preschool children and create equiprobable scenarios of its occurrence in the city of Canoas, Southern Brazil. Trained, calibrated dentists examined 1,100 children enrolled at public preschools to determine dental caries experience following World Health Organization criteria. The ArcGis 10.0 Geographic Information System was used to analyze spatial and non-spatial data. Geostatistical Modeling Software was used in geostatistical analyses to detect spatial continuity and create maps using stochastic simulation. Overall prevalence of dental caries was 25% with intraurban differentials in distribution. The findings enabled the generation of 100 equiprobable scenarios and maps with the best and worst scenarios. The highest concentration of dental caries occurrence was found in the western portion of the city, while the lowest probability of occurrence was found in the northern and southern portions. Identifying spatial inequalities in health conditions and visualizing them through the creation of maps can help to qualify and organize public health interventions and provide information to gain better understanding of the influence of the surrounding environment on adverse health conditions.O objetivo do estudo foi analisar a distribuição espacial de cárie dentária entre crianças préescolares e criar cenários equiprováveis da ocorrência deste agravo na cidade de Canoas, sul do Brasil. Exame clínico para detecção da experiência de cárie dentária de acordo com o critério da Organização Mundial da Saúde foi realizado por cirurgiõesdentistas treinados e calibrados em uma amostra de 1.100 crianças matriculadas em escolas de educação infantil. Utilizouse o Sistema de Informação Geográfica ArcGis 10.0 para a inserção de dados espaciais e não espaciais. O programa GeoMS foi utilizado nas análises geoestatísticas para a detecção da continuidade espacial e construção de mapas através da simulação estocástica. A prevalência de cárie dentária foi 25%, com diferenciais intraurbanos na sua distribuição. Os resultados permitiram a construção de 100 cenários equiprováveis e de mapas com os melhores e piores cenários no município. Uma maior concentração de ocorrên cias foi encontrada na região oeste da cidade, enquanto que as regiões norte e sul tiveram a menor probabilidade de ocorrência de cárie dentária. A identificação de desigualdades espaciais em condições de saúde e a sua visualização por meio de mapas pode auxiliar na qualificação e organização de intervenções de saúde pública, assim como fornecer subsídios que ajudem no entendimento da influência do meio ambiente sobre as condições adversas de saúde

Frequency-dependent tuning of the human vestibular "sixth sense" by transcranial oscillatory currents

Objective: The vestibular cortex is a multisensory associative region that, in neuroimaging investigations, is activated by slow-frequency (1-2 Hz) galvanic stimulation of peripheral receptors. We aimed to directly activate the vestibular cortex with biophysically modeled transcranial oscillatory current stimulation (tACS) in the same frequency range. Methods: Thirty healthy subjects and one rare patient with chronic bilateral vestibular deafferentation underwent, in a randomized, double-blind, controlled trial, to tACS at slow (1 or 2 Hz) or higher (10 Hz) frequency and sham stimulations, over the Parieto-Insular Vestibular Cortex (PIVC), while standing on a stabilometric platform. Subjective symptoms of motion sickness were scored by Simulator Sickness Questionnaire and subjects' postural sways were monitored on the platform. Results: tACS at 1 and 2 Hz induced symptoms of motion sickness, oscillopsia and postural instability, that were supported by posturographic sway recordings. Both 10 Hz-tACS and sham stimulation on the vestibular cortex did not affect vestibular function. As these effects persisted in a rare patient with bilateral peripheral vestibular areflexia documented by the absence of the Vestibular-Ocular Reflex, the possibility of a current spread toward peripheral afferents is unlikely. Conversely, the 10 Hz-tACS significantly reduced his chronic vestibular symptoms in this patient. Conclusions: Weak electrical oscillations in a frequency range corresponding to the physiological cortical activity of the vestibular system may generate motion sickness and postural sways, both in healthy subjects and in the case of bilateral vestibular deafferentation. Significance: This should be taken into account as a new side effect of tACS in future studies addressing cognitive functions. Higher frequencies of stimulation applied to the vestibular cortex may represent a new interventional option to reduce motion sickness in different scenarios

WNP: A Novel Algorithm for Gene Products Annotation from Weighted Functional Networks

Predicting the biological function of all the genes of an organism is one of the fundamental goals of computational system biology. In the last decade, high-throughput experimental methods for studying the functional interactions between gene products (GPs) have been combined with computational approaches based on Bayesian networks for data integration. The result of these computational approaches is an interaction network with weighted links representing connectivity likelihood between two functionally related GPs. The weighted network generated by these computational approaches can be used to predict annotations for functionally uncharacterized GPs. Here we introduce Weighted Network Predictor (WNP), a novel algorithm for function prediction of biologically uncharacterized GPs. Tests conducted on simulated data show that WNP outperforms other 5 state-of-the-art methods in terms of both specificity and sensitivity and that it is able to better exploit and propagate the functional and topological information of the network. We apply our method to Saccharomyces cerevisiae yeast and Arabidopsis thaliana networks and we predict Gene Ontology function for about 500 and 10000 uncharacterized GPs respectively