Biology subjects

Dobson, R. J.

Publications and source records attributed to Dobson, R. J..

6 recordsLinked to original sources

bioRxiv2025

Systematic comparative benchmarking of computational methods for the detection of transposable elements in long-read sequencing data

BackgroundMobile element insertions, particularly transposable elements (TEs) such as Alu, LINE-1 (L1), SVA, and endogenous retroviruses (ERVs), represent a major source of human genetic variation and have been implicated in evolution, genomic instability, and disease. Although long-read sequencing generally outperforms short-read sequencing for the characterisation of such elements, their accurate detection with long-reads remains challenging, with different computational tools adopting varying approaches and producing divergent call sets. As gold standards currently do not exist for TE detection, benchmarking these methods is essential to understand their strengths, limitations, and biases. Here, we systematically evaluate the performance of available state-of-the-art TE detection tools on both simulated and real human genome data using highly characterised samples from the Genome in a Bottle consortium, population level reference databases and an in-house collection for which matching short-read sequencing data are available. ResultsOur results show significant differences in calling strategies, leading to substantial variation in precision, recall, and the spectrum of TE families detected across tools. Our benchmark also displays the differences between short-read and long-read calls, highlighting the importance of appropriate method selection. ConclusionsThe benchmarking results presented here will aid TE researchers make better informed decisions on which tool to use in their long-read TE analyses. Strengths and limitations of different tools have been highlighted in depth as well as their computational requirements, which will result in less time spent finding the best tool for the job and promote faster TE research.

bioinformatics↗

bioRxiv2023

Mutafy: A webserver to identify high quality mutant protein structures in the Protein Data Bank

Changes in the amino acid sequence of proteins resulting from nonsynonymous variants in the genome, can have significant effects on protein folding, stability, dynamics, and function, which may ultimately lead to diseases. The analysis of large sets of disease associated variants is a common approach for the study of pathogenic mechanisms. In-silico mutagenesis experiments based on wildtype structures of target proteins are a common approach to this aim, however these do not account for the effect of variants on folding and might not accurately reflect conformational changes. A growing number of experimentally solved protein structures harbouring disease-associated mutations, including single amino acid variants, are deposited in the worldwide Protein Data Bank (PDB). Nevertheless, identifying high-quality structures for specific missense variants of interest remains challenging due to the growing number of deposited protein structures in the PDB, and the lack of a dedicated interface and annotation system to search and retrieve mutant protein structures. As a result, mutant protein structures in the PDB are a powerful source of information which is largely underused. To address these shortcomings, we have developed Mutafy, a publicly available webserver to identify high quality mutant protein structures. Given input human genes, the webserver finds structures of the corresponding coded wildtype proteins and their available solved mutants, selects high quality structures, annotates them with information from biomedical databases to favour their interpretation and selection, and allows for the interactive exploration of the results and 3D visualisation. Mutafy is publicly available without requiring user registration at https://mutafy.rosalind.kcl.ac.uk.

bioinformatics↗

bioRxiv2022

DNAscan2: a versatile, scalable, and user-friendly analysis pipeline for next-generation sequencing data

The current widespread adoption of next-generation sequencing (NGS) in all branches of basic and clinical genetics fields means that users with highly variable informatics skills, computing facilities and application purposes need to process, analyse, and interpret NGS data. In this landscape, versatility, scalability, and user-friendliness are key characteristics for an NGS analysis tool. We developed DNAscan2, a highly flexible, end-to-end pipeline for the analysis of NGS data, which (i) can be used for the detection of multiple variant types, including SNVs, small indels, transposable elements, short tandem repeats and other large structural variants; (ii) covers all steps of the analysis, from quality control of raw data to the generation of html reports for the interpretation and prioritisation of results; (iii) is highly adaptable and scalable as it can be deployed and run via either a graphic user interface for non-bioinformaticians, a command line tool for personal computer usage, or as a Snakemake workflow that facilitates parallel multi-sample execution for high-performance computing environments; (iv) is computationally efficient by minimising RAM and CPU time requirements. Availability and ImplementationDNAscan2 is implemented in Python3 and is available to download as a command-line tool and graphical-user interface at https://github.com/KHP-Informatics/DNAscanv2 or a Snakemake workflow at https://github.com/KHP-Informatics/DNAscanv2_snakemake.

bioinformatics↗

bioRxiv2022

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↗

bioRxiv2022

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↗

bioRxiv2018

ALSgeneScanner: a pipeline for the analysis and interpretation of DNA NGS data of ALS patients

Amyotrophic lateral sclerosis (ALS, MND) is a neurodegenerative disease of upper and lower motor neurons resulting in death from neuromuscular respiratory failure, typically within two years of first symptoms. Genetic factors are an important cause of ALS, with variants in more than 25 genes having strong evidence, and weaker evidence available for variants in more than 120 genes. With the increasing availability of Next-Generation sequencing data, non-specialists, including health care professionals and patients, are obtaining their genomic information without a corresponding ability to analyse and interpret it. Furthermore, the relevance of novel or existing variants in ALS genes is not always apparent. Here we present ALSgeneScanner, a tool that is easy to install and use, able to provide an automatic, detailed, annotated report, on a list of ALS genes from whole genome sequence data in a few hours and whole exome sequence data in about one hour on a readily available mid-range computer. This will be of value to non-specialists and aid in the interpretation of the relevance of novel and existing variants identified in DNA sequencing data.

bioinformatics↗