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Bowles, H.

Publications and source records attributed to Bowles, H..

2 recordsLinked to original sources

RetroSnake: a Modular End-to-End Pipeline for Detection of Human Endogenous Retrovirus (HERV) Transposable Elements in Next Generation Sequencing (NGS) Data

Human Endogenous Retroviruses (HERVs) integrated into the genome of vertebrates as a result of ancient exogenous infections and currently comprise [~]8% of our genome. The majority of these elements have accumulated mutations rendering them inactive. The most recently acquired members, HERV-K have potential to produce viral particles and have been linked to a wide range of diseases including cancer and neurodegeneration. Although a range of tools for HERV discovery exist, most of them lack wet-lab validation of their results and are not end-to-end as they do not cover all steps of the analysis. These factors greatly limit their use. Here we describe RetroSnake, an end-to-end, modular, computationally efficient and customisable pipeline for the discovery of HERVs in short-read NGS data. RetroSnake presents important advantages with respect to other available tools. For instance, it is the only pipeline based on an extensively wet-lab validated protocol, and it is the most complete transposable elements detection pipeline, producing annotated insertions presented as an interactive html file, easy enough to use by life scientists without substantial computational training. Availability and implementationThe Pipeline and an extensive documentation are available at https://github.com/KHP-Informatics/RetroSnake Contactalfredo.iacoangeli@kcl.ac.uk

genomics↗

An assessment of bioinformatics tools for the detection of human endogenous retroviral insertions in short-read genome sequencing data

There is a growing interest in the study of human endogenous retroviruses (HERVs) given the substantial body of evidence that implicates them in many human diseases. Although their genomic characterization presents numerous technical challenges, next-generation sequencing (NGS) has shown potential to detect HERV insertions and their polymorphisms in humans, and a number of computational tools to detect them in short-read NGS data exist. In order to design optimal analysis pipelines, an independent evaluation of the currently available tools is required. We evaluated the performance of a set of such tools using a variety of experimental designs and types of NGS datasets. These included 50 human short read whole-genome sequencing samples, matching long and short read NGS data, and simulated short-read NGS data. Our results highlight the performance variability of the tools across the datasets and suggest that different tools might be suitable for different study designs. Using multiple tools and a consensus approach is advisable if computationally feasible and wet-lab validation via PCR is advisable where biological samples are available.

bioinformatics↗