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Common integration sites of published datasets identified using a graph-based framework

With next-generation sequencing, the genomic data available for the characterization of integration sites (IS) has dramatically increased. At present, in a single experiment, several thousand viral integration genome targets can be investigated to define genomic hot spots. In a previous article, we...

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Detalles Bibliográficos
Autores principales: Vasciaveo, Alessandro, Velevska, Ivana, Politano, Gianfranco, Savino, Alessandro, Schmidt, Manfred, Fronza, Raffaele
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4874583/
https://www.ncbi.nlm.nih.gov/pubmed/27257471
http://dx.doi.org/10.1016/j.csbj.2015.11.004
Descripción
Sumario:With next-generation sequencing, the genomic data available for the characterization of integration sites (IS) has dramatically increased. At present, in a single experiment, several thousand viral integration genome targets can be investigated to define genomic hot spots. In a previous article, we renovated a formal CIS analysis based on a rigid fixed window demarcation into a more stretchy definition grounded on graphs. Here, we present a selection of supporting data related to the graph-based framework (GBF) from our previous article, in which a collection of common integration sites (CIS) was identified on six published datasets. In this work, we will focus on two datasets, IS(RTCGD) and IS(HIV), which have been previously discussed. Moreover, we show in more detail the workflow design that originates the datasets.