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Firenze, Italy

Magi A.,University of Florence | Tattini L.,University of Florence | Tattini L.,G Gaslini Institute | Cifola I.,National Research Council Italy | And 15 more authors.
Genome Biology | Year: 2013

We developed a novel software tool, EXCAVATOR, for the detection of copy number variants (CNVs) from whole-exome sequencing data. EXCAVATOR combines a three-step normalization procedure with a novel heterogeneous hidden Markov model algorithm and a calling method that classifies genomic regions into five copy number states. We validate EXCAVATOR on three datasets and compare the results with three other methods. These analyses show that EXCAVATOR outperforms the other methods and is therefore a valuable tool for the investigation of CNVs in largescale projects, as well as in clinical research and diagnostics. EXCAVATOR is freely available at http://sourceforge.net/projects/excavatortool/. © 2013 Magi et al.; licensee BioMed Central Ltd.


Magi A.,University of Florence | Tattini L.,University of Florence | Pippucci T.,University of Bologna | Torricelli F.,Diagnostic Genetic Unit | And 3 more authors.
Bioinformatics | Year: 2012

Motivation: The advent of high-throughput sequencing technologies is revolutionizing our ability in discovering and genotyping DNA copy number variants (CNVs). Read count-based approaches are able to detect CNV regions with an unprecedented resolution. Although this computational strategy has been recently introduced in literature, much work has been already done for the preparation, normalization and analysis of this kind of data. Results: Here we face the many aspects that cover the detection of CNVs by using read count approach. We first study the characteristics and systematic biases of read count distributions, focusing on the normalization methods designed for removing these biases. Subsequently, we compare the algorithms designed to detect the boundaries of CNVs and we investigate the ability of read count data to predict the exact number of DNA copy. Finally, we review the tools publicly available for analysing read count data. To better understand the state of the art of read count approaches, we compare the performance of the three most widely used sequencing technologies (Illumina Genome Analyzer, Roche 454 and Life Technologies SOLiD) in all the analyses that we perform. © The Author 2011. Published by Oxford University Press. All rights reserved.


Striano P.,University of Genoa | Coppola A.,University of Naples Federico II | Paravidino R.,Laboratory of Genetics | Malacarne M.,Laboratory of Genetics | And 29 more authors.
Archives of Neurology | Year: 2012

Objective: To perform an extensive search for genomic rearrangements by microarray-based comparative genomic hybridization in patients with epilepsy. Design: Prospective cohort study. Setting: Epilepsy centers in Italy. Patients: Two hundred seventy-nine patients with unexplained epilepsy, 265 individuals with nonsyndromic mental retardation but no epilepsy, and 246 healthy control subjects were screened by microarray-based comparative genomic hybridization. Main Outcomes Measures: Identification of copy number variations (CNVs) and gene enrichment. Results: Rare CNVs occurred in 26 patients (9.3%) and 16 healthy control subjects (6.5%) (P=.26). The CNVs identified in patients were larger (P=.03) and showed higher gene content (P=.02) than those in control subjects. The CNVslarger than 1 megabase (P=.002) and including more than 10 genes (P=.005) occurred more frequently in patients than in control subjects. Nine patients (34.6%) among those harboring rare CNVs showed rearrangements associatedwith emerging microdeletion or microduplication syndromes. Mental retardation and neuropsychiatric features were associated with rare CNVs (P=.004), whereas epilepsy type was not. The CNV rate in patients with epilepsy and mental retardation or neuropsychiatric features is not different from that observed in patients with mental retardation only. Moreover, significant enrichment of genes involved in ion transport was observed within CNVs identified in patients with epilepsy. Conclusions: Patients with epilepsy show a significantly increased burden of large, rare, gene-rich CNVs, particularly when associated with mental retardation and neuropsychiatric features. The limited overlap betweenCNVsobserved in the epilepsy group and those observed in the group with mental retardation only as well as the involvement of specific (ion channel) genes indicate a specific association between the identified CNVs and epilepsy. Screening for CNVs should be performed for diagnostic purposes preferentially in patients with epilepsy and mental retardation or neuropsychiatric features. ©2012 American Medical Association. All rights reserved.


Magi A.,Diagnostic Genetic Unit | Magi A.,University of Florence | Benelli M.,Diagnostic Genetic Unit | Benelli M.,University of Florence | And 6 more authors.
Nucleic Acids Research | Year: 2011

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 500bp 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. © 2011 The Author(s).

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