The DOI matches its Crossref record; 208 of the 208 references it cites were found in a registry or an index.
Review of applications of high-throughput sequencing in personalized medicine: barriers and facilitators of future progress in research and clinical application
Open accessAbstract
There has been an exponential growth in the performance and output of sequencing technologies (omics data) with full genome sequencing now producing gigabases of reads on a daily basis. These data may hold the promise of personalized medicine, leading to routinely available sequencing tests that can guide patient treatment decisions. In the era of high-throughput sequencing (HTS), computational considerations, data governance and clinical translation are the greatest rate-limiting steps. To ensure that the analysis, management and interpretation of such extensive omics data is exploited to its full potential, key factors, including sample sourcing, technology selection and computational expertise and resources, need to be considered, leading to an integrated set of high-performance tools and systems. This article provides an up-to-date overview of the evolution of HTS and the accompanying tools, infrastructure and data management approaches that are emerging in this space, which, if used within in a multidisciplinary context, may ultimately facilitate the development of personalized medicine.
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“High-throughput generated omics data (Lightbody et al., 2019) has played a pivotal role in developing precision medicine biomarkers for diseases such as Alzheimer's (Hampel et al., 2017(Hampel et al., , 2016;;Kovacs, 2016), diabetes (Capobianco, 2017;McCarthy, 2017;Mutie et al., 2017), cancer (Borad and LoRusso, 2017;Senft et al., 2017), hypertension (Barnes et al., 2016;Dominiczak et al., 2017),”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Lightbody et al., 2019
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“Such an amount of data poses significant computational challenges, in particular when considering that NGS throughput exponentially increases over the years, and the reduction of sequencing costs per genome outweighs that of the costs of computational power, as described by the Moore’s law [132].”
From Quantum computing algorithms: getting closer to critical problems in computational biology (2022) · cites this paper as [132]
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Items 201–208 of 208
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Cited by
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Using machine learning approaches for multi-omics data analysis: A review
Reel, P. S., Reel, S., Pearson, E., Trucco, E., & Jefferson, E. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107739 · Cited by 903
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Quantum computing algorithms: getting closer to critical problems in computational biology
Marchetti, L., Nifosì, R., Martelli, P. L., Da Pozzo, E., Cappello, V., Banterle, F., Trincavelli, M. L., Martini, C., & D’Elia, M. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac437 · Cited by 47
Metadata
- Authors
- Lightbody, G., Haberland, V., Browne, F., Taggart, L., Zheng, H., Parkes, E., & Blayney, J. K.
- Affiliations
- University of Ulster (GB)
- University of Bristol (GB)
- Almac (United Kingdom) (GB)
- Queen's University Belfast (GB)
- Corresponding author
- Lightbody, Gaye
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-06-14
- Volume
- 20
- Issue
- 5
- Pages
- 1795-1811
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- PMC
- PMC6917217
- OpenAlex
- W2885591122
- Cited by
- 173
- References
- 208
- Citations by year
- 2026: 11 · 2025: 20 · 2024: 26 · 2023: 27 · 2022: 34 · 2021: 25 · 2020: 21 · 2019: 8
- FWCI
- 1.92
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Briefings in Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Brunel University Research Archive (BURA) (Brunel University London) (submittedVersion, cc-by)
- Research Portal (Queen's University Belfast) (submittedVersion, cc-by)
- Ulster University Research Portal (Ulster University) (submittedVersion, cc-by)
- Explore Bristol Research (submittedVersion, cc-by)
- Bristol Research (University of Bristol) (submittedVersion, cc-by)
- UEA Digital Repository (University of East Anglia) (submittedVersion)
- Funders
- Academy of Medical Sciences (SGL017\1064)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genetics, Bioinformatics, and Biomedical Research
- Keywords
- next-generation sequencing, personalized medicine, clinical translation, data governance, computational genomics, genomic data interpretation, bioinformatics infrastructure
- MeSH terms
- Cloud Computing, Ethics, Computer Security, Computational Biology, Biomedical Research, Precision Medicine, High-Throughput Nucleotide Sequencing
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