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Search Results: 1 - 10 of 331 matches for " Armand Valsesia "
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The Growing Importance of CNVs: New Insights for Detection and Clinical Interpretation
Armand Valsesia,Aurélien Macé,Jacques S. Beckmann
Frontiers in Genetics , 2013, DOI: 10.3389/fgene.2013.00092
Abstract: Differences between genomes can be due to single nucleotide variants, translocations, inversions, and copy number variants (CNVs, gain or loss of DNA). The latter can range from sub-microscopic events to complete chromosomal aneuploidies. Small CNVs are often benign but those larger than 500 kb are strongly associated with morbid consequences such as developmental disorders and cancer. Detecting CNVs within and between populations is essential to better understand the plasticity of our genome and to elucidate its possible contribution to disease. Hence there is a need for better-tailored and more robust tools for the detection and genome-wide analyses of CNVs. While a link between a given CNV and a disease may have often been established, the relative CNV contribution to disease progression and impact on drug response is not necessarily understood. In this review we discuss the progress, challenges, and limitations that occur at different stages of CNV analysis from the detection (using DNA microarrays and next-generation sequencing) and identification of recurrent CNVs to the association with phenotypes. We emphasize the importance of germline CNVs and propose strategies to aid clinicians to better interpret structural variations and assess their clinical implications.
Identification and validation of copy number variants using SNP genotyping arrays from a large clinical cohort
Armand Valsesia, Brian J Stevenson, Dawn M Waterworth, Vincent Mooser, Peter Vollenweider, Gérard Waeber, C Victor Jongeneel, Jacques S Beckmann, Zoltan Kutalik, Sven Bergmann
BMC Genomics , 2012, DOI: 10.1186/1471-2164-13-241
Abstract: Here we present several advances to facilitate the latter approach. First, we introduce a novel CNV detection method based on a Gaussian Mixture Model. Second, we propose a new algorithm, PCA merge, for combining copy-number profiles from many individuals into consensus regions. We applied both our new methods as well as existing ones to data from 5612 individuals from the CoLaus study who were genotyped on Affymetrix 500K arrays. We developed a number of procedures in order to evaluate the performance of the different methods. This includes comparison with previously published CNVs as well as using a replication sample of 239 individuals, genotyped with Illumina 550K arrays. We also established a new evaluation procedure that employs the fact that related individuals are expected to share their CNVs more frequently than randomly selected individuals. The ability to detect both rare and common CNVs provides a valuable resource that will facilitate association studies exploring potential phenotypic associations with CNVs.Our new methodologies for CNV detection and their evaluation will help in extracting additional information from the large amount of SNP-genotyping data on various cohorts and use this to explore structural variants and their impact on complex traits.
An Analysis of Interference as a Source for Diffraction  [PDF]
Armand Waksberg
Journal of Electromagnetic Analysis and Applications (JEMAA) , 2010, DOI: 10.4236/jemaa.2010.210079
Abstract: An analysis is made of interference of two plane waves which in turn give rise to subsequent diffraction phenomena. This is done using two different approaches. One of them is straight forward but is difficult to interpret. The second is less conventional but more intuitive thus giving more insight into the process. The geometry is kept very simple to focus on the process itself. Both approaches give rise to the same results thus offering a choice in tackling such prob-lems.
Breaking the waves: improved detection of copy number variation from microarray-based comparative genomic hybridization
John C Marioni, Natalie P Thorne, Armand Valsesia, Tomas Fitzgerald, Richard Redon, Heike Fiegler, T Daniel Andrews, Barbara E Stranger, Andrew G Lynch, Emmanouil T Dermitzakis, Nigel P Carter, Simon Tavaré, Matthew E Hurles
Genome Biology , 2007, DOI: 10.1186/gb-2007-8-10-r228
Abstract: We describe the presence of a genome-wide technical artifact, spatial autocorrelation or 'wave', which occurs in a large dataset used to determine the location of CNV across the genome. By removing this artifact we are able to obtain both a more biologically meaningful clustering of the data and an increase in the number of CNVs identified by current calling methods without a major increase in the number of false positives detected. Moreover, removing this artifact is critical for the development of a novel model-based CNV calling algorithm - CNVmix - that uses cross-sample information to identify regions of the genome where CNVs occur. For regions of CNV that are identified by both CNVmix and current methods, we demonstrate that CNVmix is better able to categorize samples into groups that represent copy number gains or losses.Removing artifactual 'waves' (which appear to be a general feature of array comparative genomic hybridization (aCGH) datasets) and using cross-sample information when identifying CNVs enables more biological information to be extracted from aCGH experiments designed to investigate copy number variation in normal individuals.Copy number variation (CNV) throughout the human genome has recently been the focus of much interest and array comparative genomic hybridization (aCGH) technology has been instrumental in identifying regions of the genome where CNVs occur. It is believed that such variation may explain the presence and development of adverse phenotypes ranging from HIV-1 infection to Alzheimer's and Parkinson's disease [1]. To analyze the experimental aCGH data and to identify the location of CNVs, specific statistical tools for normalization and CNV calling (segmentation) are undergoing continual refinement and development.There are many algorithms [2,3] for segmenting aCGH data into classes with differing numbers of copies. The vast majority of these algorithms identify CNVs by identifying outlier regions within a single genome (cross-gen
Compressive Signal Processing with Circulant Sensing Matrices
Diego Valsesia,Enrico Magli
Mathematics , 2014,
Abstract: Compressive sensing achieves effective dimensionality reduction of signals, under a sparsity constraint, by means of a small number of random measurements acquired through a sensing matrix. In a signal processing system, the problem arises of processing the random projections directly, without first reconstructing the signal. In this paper, we show that circulant sensing matrices allow to perform a variety of classical signal processing tasks such as filtering, interpolation, registration, transforms, and so forth, directly in the compressed domain and in an exact fashion, \emph{i.e.}, without relying on estimators as proposed in the existing literature. The advantage of the techniques presented in this paper is to enable direct measurement-to-measurement transformations, without the need of costly recovery procedures.
Spatially Scalable Compressed Image Sensing with Hybrid Transform and Inter-layer Prediction Model
Diego Valsesia,Enrico Magli
Computer Science , 2013,
Abstract: Compressive imaging is an emerging application of compressed sensing, devoted to acquisition, encoding and reconstruction of images using random projections as measurements. In this paper we propose a novel method to provide a scalable encoding of an image acquired by means of compressed sensing techniques. Two bit-streams are generated to provide two distinct quality levels: a low-resolution base layer and full-resolution enhancement layer. In the proposed method we exploit a fast preview of the image at the encoder in order to perform inter-layer prediction and encode the prediction residuals only. The proposed method successfully provides resolution and quality scalability with modest complexity and it provides gains in the quality of the reconstructed images with respect to separate encoding of the quality layers. Remarkably, we also show that the scheme can also provide significant gains with respect to a direct, non-scalable system, thus accomplishing two features at once: scalability and improved reconstruction performance.
A Novel Rate Control Algorithm for Onboard Predictive Coding of Multispectral and Hyperspectral Images
Diego Valsesia,Enrico Magli
Computer Science , 2014, DOI: 10.1109/TGRS.2013.2296329
Abstract: Predictive coding is attractive for compression onboard of spacecrafts thanks to its low computational complexity, modest memory requirements and the ability to accurately control quality on a pixel-by-pixel basis. Traditionally, predictive compression focused on the lossless and near-lossless modes of operation where the maximum error can be bounded but the rate of the compressed image is variable. Rate control is considered a challenging problem for predictive encoders due to the dependencies between quantization and prediction in the feedback loop, and the lack of a signal representation that packs the signal's energy into few coefficients. In this paper, we show that it is possible to design a rate control scheme intended for onboard implementation. In particular, we propose a general framework to select quantizers in each spatial and spectral region of an image so as to achieve the desired target rate while minimizing distortion. The rate control algorithm allows to achieve lossy, near-lossless compression, and any in-between type of compression, e.g., lossy compression with a near-lossless constraint. While this framework is independent of the specific predictor used, in order to show its performance, in this paper we tailor it to the predictor adopted by the CCSDS-123 lossless compression standard, obtaining an extension that allows to perform lossless, near-lossless and lossy compression in a single package. We show that the rate controller has excellent performance in terms of accuracy in the output rate, rate-distortion characteristics and is extremely competitive with respect to state-of-the-art transform coding.
On the Differences in the Intraseasonal Rainfall Variability between Western and Eastern Central Africa: Case of 25 - 70-Day Oscillations  [PDF]
Alain Tchakoutio, Armand Nzeukou
Journal of Geoscience and Environment Protection (GEP) , 2016, DOI: 10.4236/gep.2016.47015
Abstract: The intraseasonal timescale is critical in Central Africa, because the resources of the region are highly rainfall dependent. In this paper, we use 1DD GPCP rainfall product to investigate the differences in the space-time structures of the 25 - 70-day intraseasonal variability of rainfall, over the Western Central Africa (WCA) and the Eastern Central Africa (ECA), with different climate features. The results of Empirical Orthogonal Functions (EOFs) analysis have shown that the amount of variance explained by the leading EOFs is greater in ECA (58.4%) than in WCA (49.8%). For both WCA and ECA, the power spectra of the Principal Components (PCs) peaked around 40 days, indicating a MJO signal. The seasonality of ISO is evident, but this seasonality is much noticeable in ECA where almost 80% of the total yearly ISO power occurs during November-April season, against only around 60% for WCA. Moreover, the lagged cross correlations computed between WCA and ECA PCs time series showed that most of the WCA PCs led ECA PCs time series with a timescale of 8 - 12 days, revealing that the eastward propagation could potentially be the relationship between WCA and ECA modes. The interannual variations in the ISO activity are weak in WCA, when compared with ECA where the signal exhibits larger interannual variations, quite linked with ENSO.
Network-Guided Analysis of Genes with Altered Somatic Copy Number and Gene Expression Reveals Pathways Commonly Perturbed in Metastatic Melanoma
Armand Valsesia,Donata Rimoldi,Danielle Martinet,Mark Ibberson,Paola Benaglio,Manfredo Quadroni,Patrice Waridel,Muriel Gaillard,Mireille Pidoux,Blandine Rapin,Carlo Rivolta,Ioannis Xenarios,Andrew J. G. Simpson,Stylianos E. Antonarakis,Jacques S. Beckmann,C. Victor Jongeneel,Christian Iseli,Brian J. Stevenson
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0018369
Abstract: Cancer genomes frequently contain somatic copy number alterations (SCNA) that can significantly perturb the expression level of affected genes and thus disrupt pathways controlling normal growth. In melanoma, many studies have focussed on the copy number and gene expression levels of the BRAF, PTEN and MITF genes, but little has been done to identify new genes using these parameters at the genome-wide scale. Using karyotyping, SNP and CGH arrays, and RNA-seq, we have identified SCNA affecting gene expression (‘SCNA-genes’) in seven human metastatic melanoma cell lines. We showed that the combination of these techniques is useful to identify candidate genes potentially involved in tumorigenesis. Since few of these alterations were recurrent across our samples, we used a protein network-guided approach to determine whether any pathways were enriched in SCNA-genes in one or more samples. From this unbiased genome-wide analysis, we identified 28 significantly enriched pathway modules. Comparison with two large, independent melanoma SCNA datasets showed less than 10% overlap at the individual gene level, but network-guided analysis revealed 66% shared pathways, including all but three of the pathways identified in our data. Frequently altered pathways included WNT, cadherin signalling, angiogenesis and melanogenesis. Additionally, our results emphasize the potential of the EPHA3 and FRS2 gene products, involved in angiogenesis and migration, as possible therapeutic targets in melanoma. Our study demonstrates the utility of network-guided approaches, for both large and small datasets, to identify pathways recurrently perturbed in cancer.
Trends on cerebrospinal fluid investigation: an overwiew
Arquivos de Neuro-Psiquiatria , 1999,
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