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Iacoangeli A, Al Khleifat A, Sproviero W, Shatunov A, Jones AR, Morgan SL, Pittman A, Dobson RJ, Newhouse SJ, Al-Chalabi A. DNAscan: personal computer compatible NGS analysis, annotation and visualisation. BMC Bioinformatics 2019; 20:213. [PMID: 31029080 PMCID: PMC6487045 DOI: 10.1186/s12859-019-2791-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 04/02/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Next Generation Sequencing (NGS) is a commonly used technology for studying the genetic basis of biological processes and it underpins the aspirations of precision medicine. However, there are significant challenges when dealing with NGS data. Firstly, a huge number of bioinformatics tools for a wide range of uses exist, therefore it is challenging to design an analysis pipeline. Secondly, NGS analysis is computationally intensive, requiring expensive infrastructure, and many medical and research centres do not have adequate high performance computing facilities and cloud computing is not always an option due to privacy and ownership issues. Finally, the interpretation of the results is not trivial and most available pipelines lack the utilities to favour this crucial step. RESULTS We have therefore developed a fast and efficient bioinformatics pipeline that allows for the analysis of DNA sequencing data, while requiring little computational effort and memory usage. DNAscan can analyse a whole exome sequencing sample in 1 h and a 40x whole genome sequencing sample in 13 h, on a midrange computer. The pipeline can look for single nucleotide variants, small indels, structural variants, repeat expansions and viral genetic material (or any other organism). Its results are annotated using a customisable variety of databases and are available for an on-the-fly visualisation with a local deployment of the gene.iobio platform. DNAscan is implemented in Python. Its code and documentation are available on GitHub: https://github.com/KHP-Informatics/DNAscan . Instructions for an easy and fast deployment with Docker and Singularity are also provided on GitHub. CONCLUSIONS DNAscan is an extremely fast and computationally efficient pipeline for analysis, visualization and interpretation of NGS data. It is designed to provide a powerful and easy-to-use tool for applications in biomedical research and diagnostic medicine, at minimal computational cost. Its comprehensive approach will maximise the potential audience of users, bringing such analyses within the reach of non-specialist laboratories, and those from centres with limited funding available.
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Affiliation(s)
- A Iacoangeli
- Department of Biostatistics and Health Informatics, King's College London, London, UK. .,Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, London, UK.
| | - A Al Khleifat
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, London, UK
| | - W Sproviero
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, London, UK
| | - A Shatunov
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, London, UK
| | - A R Jones
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, London, UK
| | - S L Morgan
- Department of Molecular Neuroscience, UCL, Institute of Neurology, London, UK
| | - A Pittman
- Department of Molecular Neuroscience, UCL, Institute of Neurology, London, UK
| | - R J Dobson
- Department of Biostatistics and Health Informatics, King's College London, London, UK.,Farr Institute of Health Informatics Research, UCL Institute of Health Informatics, University College London, London, UK.,National Institute for Health Research (NIHR) Biomedical Research Centre and Dementia Unit at South London and Maudsley NHS Foundation Trust and King's College London, London, UK
| | - S J Newhouse
- Department of Biostatistics and Health Informatics, King's College London, London, UK.,Farr Institute of Health Informatics Research, UCL Institute of Health Informatics, University College London, London, UK.,National Institute for Health Research (NIHR) Biomedical Research Centre and Dementia Unit at South London and Maudsley NHS Foundation Trust and King's College London, London, UK
| | - A Al-Chalabi
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, London, UK.,King's College Hospital, Bessemer Road, London, SE5 9RS, UK
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Czell D. [Genetics of Amyotrophic Lateral Sclerosis]. PRAXIS 2019; 108:37-44. [PMID: 30621546 DOI: 10.1024/1661-8157/a003153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Genetics of Amyotrophic Lateral Sclerosis Abstract. In recent years, the knowledge of gene mutation that can lead to amyotrophic lateral sclerosis (ALS), frontotemporal dementia, or a FTD-ALS complex has been drastically improvedwith the help of next-generation sequencing (NGS), so that many new genes and their molecular genetic mechanisms and symptoms of the patients could be described. For example it was found that in the same family with the same gene mutation patients with ALS, FTD or another neurodegenerative disease can be found. The knowledge about the genes does not only contribute to the understanding of these diseases, but should help develop new therapeutic possibilities.
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Affiliation(s)
- David Czell
- 1 Klinik für Innere Medizin/Neurologie, Spital Linth, Uznach
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