http://www.cnr.it/ontology/cnr/individuo/prodotto/ID322648
G-CNV: A GPU-based Tool for Preparing Data to Detect CNVs with Read Depth Methods (Articolo in rivista)
- Type
- Label
- 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
- Pagina fine
- Rivista
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
- 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)
- Prodotto di
- Autore CNR
- Insieme di parole chiave
Incoming links:
- Autore CNR di
- Prodotto
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi
- Insieme di parole chiave di