Kabiljo, Renata 
ORCID: 0000-0002-5183-4844, Bowles, Harry, Marriott, Heather, Jones, Ashley R, Bouton, Clement R ORCID: 0000-0001-9607-6533, Dobson, Richard JB, Quinn, John P ORCID: 0000-0003-3551-7803, Al Khleifat, Ahmad ORCID: 0000-0002-7406-9831, Swanson, Chad M, Al-Chalabi, Ammar et al (show 1 more authors)
(2022)
RetroSnake: A modular pipeline to detect human endogenous retroviruses in genome sequencing data.
iScience, 25 (11).
105289-.
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RetroSnake_ A modular pipeline to detect human endogenous retroviruses in genome sequencing data.pdf - Published version Download (2MB) | Preview |
Abstract
Human endogenous retroviruses (HERVs) integrated into the human genome as a result of ancient exogenous infections and currently comprise ∼8% of our genome. The members of the most recently acquired HERV family, HERV-Ks, still retain the potential to produce viral molecules and have been linked to a wide range of diseases including cancer and neurodegeneration. Although a range of tools for HERV detection in NGS data exist, most of them lack wet lab validation and they do not cover all steps of the analysis. Here, we describe RetroSnake, an end-to-end, modular, computationally efficient, and customizable pipeline for the discovery of HERVs in short-read NGS data. RetroSnake is based on an extensively wet-lab validated protocol, it covers all steps of the analysis from raw data to the generation of annotated results presented as an interactive html file, and it is easy to use by life scientists without substantial computational training. Availability and implementation: The Pipeline and an extensive documentation are available on GitHub.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Biocomputational method, Bioinformatics, Sequence analysis |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 11 Nov 2022 09:37 |
| Last Modified: | 18 Jan 2023 19:43 |
| DOI: | 10.1016/j.isci.2022.105289 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3166145 |
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