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Network analysis for estimating standardization trends in genomics using MEDLINE

BACKGROUND: Biotechnology in genomics, such as sequencing devices and gene quantification software, has proliferated and been applied to clinical settings. However, the lack of standards applicable to it poses practical problems in interoperability and reusability of the technology across various ap...

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Autores principales: Bae, Eun Bit, Nam, Sejin, Lee, Sungin, Ahn, Sun-Ju
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9540045/
https://www.ncbi.nlm.nih.gov/pubmed/36207671
http://dx.doi.org/10.1186/s12874-022-01740-4
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author Bae, Eun Bit
Nam, Sejin
Lee, Sungin
Ahn, Sun-Ju
author_facet Bae, Eun Bit
Nam, Sejin
Lee, Sungin
Ahn, Sun-Ju
author_sort Bae, Eun Bit
collection PubMed
description BACKGROUND: Biotechnology in genomics, such as sequencing devices and gene quantification software, has proliferated and been applied to clinical settings. However, the lack of standards applicable to it poses practical problems in interoperability and reusability of the technology across various application domains. This study aims to visualize and identify the standard trends in clinical genomics and to suggest areas on which standardization efforts must focus. METHODS: Of 16,538 articles retrieved from PubMed, published from 1975 to 2020, using search keywords “genomics and standard” and “clinical genomic sequence and standard”, terms were extracted from the abstracts and titles of 15,855 articles. Our analysis includes (1) network analysis of full phases (2) period analysis with five phases; (3) statistical analysis; (4) content analysis. RESULTS: Our research trend showed an increasing trend from 2003, years marked by the completion of the human genome project (2003). The content analysis showed that keywords related to such concepts as gene types for analysis, and analysis techniques were increased in phase 3 when US-FDA first approved the next-generation sequencer. During 2017–2019, oncology-relevant terms were clustered and contributed to the increasing trend in phase 4 of the content analysis. In the statistical analysis, all the categories showed high regression values (R(2) > 0.586) throughout the whole analysis period and phase-based statistical analysis showed significance only in the Genetics terminology category (P = .039(*)) at phase 4. CONCLUSIONS: Through comprehensive trend analysis from our study, we provided the trend shifts and high-demand items in standardization for clinical genetics. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01740-4.
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spelling pubmed-95400452022-10-08 Network analysis for estimating standardization trends in genomics using MEDLINE Bae, Eun Bit Nam, Sejin Lee, Sungin Ahn, Sun-Ju BMC Med Res Methodol Research BACKGROUND: Biotechnology in genomics, such as sequencing devices and gene quantification software, has proliferated and been applied to clinical settings. However, the lack of standards applicable to it poses practical problems in interoperability and reusability of the technology across various application domains. This study aims to visualize and identify the standard trends in clinical genomics and to suggest areas on which standardization efforts must focus. METHODS: Of 16,538 articles retrieved from PubMed, published from 1975 to 2020, using search keywords “genomics and standard” and “clinical genomic sequence and standard”, terms were extracted from the abstracts and titles of 15,855 articles. Our analysis includes (1) network analysis of full phases (2) period analysis with five phases; (3) statistical analysis; (4) content analysis. RESULTS: Our research trend showed an increasing trend from 2003, years marked by the completion of the human genome project (2003). The content analysis showed that keywords related to such concepts as gene types for analysis, and analysis techniques were increased in phase 3 when US-FDA first approved the next-generation sequencer. During 2017–2019, oncology-relevant terms were clustered and contributed to the increasing trend in phase 4 of the content analysis. In the statistical analysis, all the categories showed high regression values (R(2) > 0.586) throughout the whole analysis period and phase-based statistical analysis showed significance only in the Genetics terminology category (P = .039(*)) at phase 4. CONCLUSIONS: Through comprehensive trend analysis from our study, we provided the trend shifts and high-demand items in standardization for clinical genetics. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01740-4. BioMed Central 2022-10-07 /pmc/articles/PMC9540045/ /pubmed/36207671 http://dx.doi.org/10.1186/s12874-022-01740-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Bae, Eun Bit
Nam, Sejin
Lee, Sungin
Ahn, Sun-Ju
Network analysis for estimating standardization trends in genomics using MEDLINE
title Network analysis for estimating standardization trends in genomics using MEDLINE
title_full Network analysis for estimating standardization trends in genomics using MEDLINE
title_fullStr Network analysis for estimating standardization trends in genomics using MEDLINE
title_full_unstemmed Network analysis for estimating standardization trends in genomics using MEDLINE
title_short Network analysis for estimating standardization trends in genomics using MEDLINE
title_sort network analysis for estimating standardization trends in genomics using medline
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9540045/
https://www.ncbi.nlm.nih.gov/pubmed/36207671
http://dx.doi.org/10.1186/s12874-022-01740-4
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