G-CNV: A GPU-based Tool for Preparing Data to Detect CNVs with Read Depth Methods (Articolo in rivista)

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  • G-CNV: A GPU-based Tool for Preparing Data to Detect CNVs with Read Depth Methods (Articolo in rivista) (literal)
Anno
  • 2015-01-01T00:00:00+01:00 (literal)
Alternative label
  • Andrea Manconi, Emanuele Manca, Alessandro Orro, Giuliano Armano, Matteo Gnocchi, Marco Moscatelli and Luciano Milanesi (2015)
    G-CNV: A GPU-based Tool for Preparing Data to Detect CNVs with Read Depth Methods
    in Frontiers in Bioengineering and Biotechnology
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Andrea Manconi, Emanuele Manca, Alessandro Orro, Giuliano Armano, Matteo Gnocchi, Marco Moscatelli and Luciano Milanesi (literal)
Pagina inizio
  • 1 (literal)
Pagina fine
  • 20 (literal)
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Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 20 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 1Institute for Biomedical Technologies, National Research Council, Segrate (Mi), Italy 2Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari (Ca), Italy (literal)
Titolo
  • G-CNV: A GPU-based Tool for Preparing Data to Detect CNVs with Read Depth Methods (literal)
Abstract
  • Copy Number Variations (CNVs) are the most prevalent types of structural variations (SVs) in 4 the human genome and are involved in a wide range of common human diseases. Different 5 computational methods have been devised to detect this type of SVs and to study how they 6 are implicated in human diseases. Recently, computational methods based on high throughput 7 sequencing (HTS) are increasingly used. The majority of these methods focus on mapping 8 short-read sequences generated from a donor against a reference genome to detect signatures 9 distinctive of CNVs. In particular, read-depth based methods detect CNVs by analyzing genomic 10 regions with significantly different read-depth from the other ones. The pipeline analysis of these 11 methods consists of four main stages: i ) data preparation, ii ) data normalization, iii ) CNV regions 12 identification, and iv) copy number estimation. However, available tools do not support most of 13 the operations required at the first two stages of this pipeline. Typically, they start the analysis by 14 building the read-depth signal from pre-processed alignments. Therefore, third-party tools must 15 be used to perform most of the preliminary operations required to build the read-depth signal. 16 These data-intensive operations can be efficiently parallelized on Graphics Processing Units 17 (GPUs). In this article we present G-CNV, a GPU-based tool devised to perform the common 18 operations required at the first two stages of the analysis pipeline. G-CNV is able to filter 19 low quality read sequences, to mask low quality nucleotides, to remove adapter sequences, 20 to remove duplicated read sequences, to map the short-reads, to resolve multiple mapping 21 ambiguities, to build the read-depth signal, and to normalize it. G-CNV can be efficiently used 22 as a third-party tool able to prepare data for the subsequent read-depth signal generation and 23 analysis. Moreover, it can also be integrated in CNV detection tools to generate read-depth 24 signals. (literal)
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