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Bibliometric Analysis on Methods and Tools Developed for DGE Analysis: Current Trends and Future Perspectives

Cilt: 30 Sayı: 1 29 Nisan 2025
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Bibliometric Analysis on Methods and Tools Developed for DGE Analysis: Current Trends and Future Perspectives

Öz

Differential gene expression (DGE) analysis has gained significant attention with the advent of next-generation sequencing technologies, leading to the development of a wide range of methods and tools for DGE analysis. We performed bibliometric analysis using Biblioshiny and VOSviewer software to investigate the trends over the investigated period. Relevant papers with differential gene expression related terms as the subjects from 2005 to 2023 were retrieved from the Web of Science database. Network maps were generated using Biblioshiny and VOSviewer software to illustrate the published trends over the investigated period. A total of 729 studies were examined to reveal trends in the DGE analysis methodologies, tools, and packages. In the analysis, co-authorship, bibliographic coupling, and co-occurrence analyses were conducted for country, institution, source, author, and keyword productivity. It was found that the output and citation numbers increased after 2005. During the study period, the USA and China emerged as the leading contributors to the field. The temporal study revealed a significant increase in publications at certain times, followed by period of slight decrease. The greatest fall was observed between 2008 and 2010. Despite these decreases, DGE analysis remains a critical topic in genomics due to its essential role in understanding the mechanisms of any disease, gene function, and therapeutic targets. This trend suggests that current methods and tools are considered sufficiently powerful for identifying key informative genes associated with diverse diseases.

Anahtar Kelimeler

Bibliometric analysis, Differential expression analysis, Differential gene expression, Gene expression, RNA-seq

Kaynakça

  1. Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
  2. Bai, J. P. F., Alekseyenko, A. V., Statnikov, A., Wang, I. M., & Wong, P. H. (2013). Strategic applications of gene expression: From drug discovery/development to bedside. The AAPS Journal, 15(2), 427-437. https://doi.org/10.1208/s12248-012-9447-1
  3. Cephe, A., Koçhan, N., Ertürk Zararsız, G., Eldem, V., & Zararsız, G. (2023). Class discovery, comparison, and prediction methods for RNA-Seq data. In J. Wang (Ed.), Encyclopedia of Data Science and Machine Learning (pp. 2060-2084). IGI Global. https://doi.org/10.4018/978-1-7998-9220-5.ch123
  4. Chowdhury, H. A., Bhattacharyya, D. K., & Kalita, J. K. (2020). (Differential) Co-expression analysis of gene expression: A survey of best practices. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(4), 1154-1173. https://doi.org/10.1109/TCBB.2019.2893170
  5. Clark, A. J., & Lillard, J. W., Jr. (2024). A comprehensive review of bioinformatics tools for genomic biomarker discovery driving precision oncology. Genes, 15(8), 1036. https://doi.org/10.3390/genes15081036
  6. Costa-Silva, J., Domingues, D. S., Menotti, D., Hungria, M., & Lopes, F. M. (2022). Temporal progress of gene expression analysis with RNA-Seq data: A review on the relationship between computational methods. Computational and Structural Biotechnology Journal, 21, 86-98. https://doi.org/10.1016/j.csbj.2022.11.051
  7. Dhillon, A., Singh, A., & Bhalla, V. K. (2023). A systematic review on biomarker identification for cancer diagnosis and prognosis in multi-omics: From computational needs to machine learning and deep learning. Archives of Computational Methods in Engineering, 30, 917-949. https://doi.org/10.1007/s11831-022-09821-9
  8. Di, Y., Schafer, D. W., Cumbie, J. S., & Chang, J. H. (2011). The NBP negative binomial model for assessing differential gene expression from RNA-Seq. Statistical Applications in Genetics and Molecular Biology, 10(1). https://doi.org/10.2202/1544-6115.1637
  9. Hardcastle, T. J., & Kelly, K. A. (2010). baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics, 11, 422. https://doi.org/10.1186/1471-2105-11-422
  10. Kebschull, M., Fittler, M. J., Demmer, R. T., & Papapanou, P. N. (2017). Differential expression and functional analysis of high-throughput-omics data using open source tools. Methods in Molecular Biology, 1537, 327-345. https://doi.org/10.1007/978-1-4939-6685-1_19

Kaynak Göster

APA
Koçhan, N. (2025). Bibliometric Analysis on Methods and Tools Developed for DGE Analysis: Current Trends and Future Perspectives. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 30(1), 78-91. https://doi.org/10.53433/yyufbed.1591489
AMA
1.Koçhan N. Bibliometric Analysis on Methods and Tools Developed for DGE Analysis: Current Trends and Future Perspectives. YYUFBED. 2025;30(1):78-91. doi:10.53433/yyufbed.1591489
Chicago
Koçhan, Necla. 2025. “Bibliometric Analysis on Methods and Tools Developed for DGE Analysis: Current Trends and Future Perspectives”. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi 30 (1): 78-91. https://doi.org/10.53433/yyufbed.1591489.
EndNote
Koçhan N (01 Nisan 2025) Bibliometric Analysis on Methods and Tools Developed for DGE Analysis: Current Trends and Future Perspectives. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi 30 1 78–91.
IEEE
[1]N. Koçhan, “Bibliometric Analysis on Methods and Tools Developed for DGE Analysis: Current Trends and Future Perspectives”, YYUFBED, c. 30, sy 1, ss. 78–91, Nis. 2025, doi: 10.53433/yyufbed.1591489.
ISNAD
Koçhan, Necla. “Bibliometric Analysis on Methods and Tools Developed for DGE Analysis: Current Trends and Future Perspectives”. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi 30/1 (01 Nisan 2025): 78-91. https://doi.org/10.53433/yyufbed.1591489.
JAMA
1.Koçhan N. Bibliometric Analysis on Methods and Tools Developed for DGE Analysis: Current Trends and Future Perspectives. YYUFBED. 2025;30:78–91.
MLA
Koçhan, Necla. “Bibliometric Analysis on Methods and Tools Developed for DGE Analysis: Current Trends and Future Perspectives”. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi, c. 30, sy 1, Nisan 2025, ss. 78-91, doi:10.53433/yyufbed.1591489.
Vancouver
1.Necla Koçhan. Bibliometric Analysis on Methods and Tools Developed for DGE Analysis: Current Trends and Future Perspectives. YYUFBED. 01 Nisan 2025;30(1):78-91. doi:10.53433/yyufbed.1591489