A bioinformatic pipeline for simulating viral integration data

Suzanne Scott; Susanna Grigson; Felix Hartkopf; Claus V Hallwirth; Ian E Alexander; Denis C Bauer; Laurence O W Wilson

doi:10.1016/j.dib.2022.108161

A bioinformatic pipeline for simulating viral integration data

Data Brief. 2022 Apr 10:42:108161. doi: 10.1016/j.dib.2022.108161. eCollection 2022 Jun.

Authors

Suzanne Scott^{1

2

3}, Susanna Grigson⁴, Felix Hartkopf⁵, Claus V Hallwirth^{2

3}, Ian E Alexander^{2

3

6}, Denis C Bauer^{1

7

8}, Laurence O W Wilson^{1

8}

Affiliations

¹ Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, North Ryde, Australia.
² Gene Therapy Research Unit, Children's Medical Research Institute, Westmead, Australia.
³ The Sydney Children's Hospitals Network, Faculty of Medicine and Health, The University of Sydney, Westmead, Australia.
⁴ College of Science and Engineering, Flinders University, Adelaide, Australia.
⁵ Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany.
⁶ Discipline of Child and Adolescent Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, SA.
⁷ Macquarie University, Department of Biomedical Sciences, Faculty of Medicine and Health Science, Macquarie Park, SA.
⁸ Macquarie University, Applied BioSciences, Faculty of Science and Engineering, Macquarie Park, SA.

Abstract

Viral integration is a complex biological process, and it is useful to have a reference integration dataset with known properties to compare experimental data against, or for comparing with the results from computational tools that detect integration. To generate these data, we developed a pipeline for simulating integrations of a viral or vector genome into a host genome. Our method reproduces more complex characteristics of vector and viral integration, including integration of sub-genomic fragments, structural variation of the integrated genomes, and deletions from the host genome at the integration site. Our method [1] takes the form of a snakemake [2] pipeline, consisting of a Python [3] script using the Biopython [4] module that simulates integrations of a viral reference into a host reference. This produces a reference containing integrations, from which sequencing reads are simulated using ART [5]. The IDs of the reads crossing integration junctions are then annotated using another python script to produce the final output, consisting of the simulated reads and a table of the locations of those integrations and the reads crossing each integration junction. To illustrate our method, we provide simulated reads, integration locations, as well as the code required to simulate integrations using any virus and host reference. This simulation method was used to investigate the performance of viral integration tools in our research [6].

Keywords: Gene therapy; In silico; Integration; Vector; Virus.