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

Alessandro Vasciaveo; Ivana Velevska; Gianfranco Politano; Alessandro Savino; Manfred Schmidt; Raffaele Fronza

doi:10.1016/j.csbj.2015.11.004

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

Comput Struct Biotechnol J. 2015 Nov 29:14:87-90. doi: 10.1016/j.csbj.2015.11.004. eCollection 2016.

Authors

Alessandro Vasciaveo¹, Ivana Velevska², Gianfranco Politano³, Alessandro Savino³, Manfred Schmidt², Raffaele Fronza²

Affiliations

¹ Department of Translational Oncology, National Center for Tumor Diseases and German Cancer Research Center, Im Neuenheimer Feld 581, 69120 Heidelberg, Germany; Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy.
² Department of Translational Oncology, National Center for Tumor Diseases and German Cancer Research Center, Im Neuenheimer Feld 581, 69120 Heidelberg, Germany.
³ Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy.

Abstract

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, ISRTCGD and ISHIV, which have been previously discussed. Moreover, we show in more detail the workflow design that originates the datasets.