Research Article
BibTex RIS Cite

DGE Analizi için Geliştirilen Yöntemler ve Araçlar Üzerine Bibliyometrik Analiz: Güncel Eğilimler ve Gelecek Perspektifleri

Year 2025, Volume: 30 Issue: 1, 78 - 91, 29.04.2025

Necla Koçhan

https://doi.org/10.53433/yyufbed.1591489

Abstract

Diferansiyel gen ekspresyonu (DGE) analizi, yeni nesil dizileme teknolojilerinin ortaya çıkışıyla önemli bir ilgi kazanmıştır. Bu durum, DGE analizi için çeşitli yöntemlerin ve araçların geliştirilmesine yol açmıştır. Bu çalışmada, Biblioshiny ve VOSviewer yazılımları kullanılarak, incelenen dönem boyunca eğilimleri araştırmak amacıyla bibliyometrik analiz yapılmıştır. 2005-2023 yılları arasında Web of Science veri tabanından, diferansiyel gen ekspresyonu ile ilgili terimleri konu alan ilgili makaleler taranmıştır. İncelenen dönem boyunca yayımlanan eğilimleri göstermek için Biblioshiny ve VOSviewer yazılımları kullanılarak ağ haritaları oluşturulmuştur. Toplamda 729 çalışma, DGE analizi metodolojilerindeki, araçlarındaki ve paketlerindeki eğilimleri ortaya koymak amacıyla incelenmiştir. Bu amaçla, ülke, kurum, kaynak, yazar ve anahtar kelime üretkenliği açısından eş-yazarlık, bibliyografik eşleşme ve eş-oluşum analizleri yapılmıştır. 2005 yılından sonra çıktı ve atıf sayılarında artış gözlenmiştir. Çalışma süresince ABD ve Çin, DGE analizine en çok katkı sağlayan ülkeler olarak öne çıkmıştır. Zamansal çalışmalar, belirli aralıklarla bir miktar azalma olmakla birlikte, zaman içinde yayınlarda önemli bir artış olduğunu ortaya koymuştur. En büyük düşüş, 2008 ile 2010 yılları arasında gözlenmiştir. Bu düşüşlere rağmen, DGE analizi, herhangi bir hastalığın mekanizmalarını, gen işlevlerini ve terapötik hedefleri anlamadaki temel rolü nedeniyle genomikte kritik bir konu olmaya devam etmektedir. Bu eğilim, mevcut yöntemlerin ve araçların, çeşitli hastalıklarla ilişkili anahtar bilgilendirici genleri tanımlamak için yeterince güçlü kabul edildiğini göstermektedir.

Keywords

Bibliyometrik analiz Diferansiyel dizileme analizi Diferansiyel gen ekspresyonu Gen ifadesi RNA-dizileme

References

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Kvam, V. M., Liu, P., & Si, Y. (2012). A comparison of statistical methods for detecting differentially expressed genes from RNA‐seq data. American Journal of Botany, 99(2), 248-256. https://doi.org/10.3732/ajb.1100340
  • Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014). Voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology, 15(2), R29. https://doi.org/10.1186/gb-2014-15-2-r29
  • Li, J., & Tibshirani, R. (2013). Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data. Statistical Methods in Medical Research, 22(5), 519-536. https://doi.org/10.1177/0962280211428386
  • Liñares Blanco, J., Gestal, M., Dorado, J., & Fernandez-Lozano, C. (2019). Differential gene expression analysis of RNA-seq data using machine learning for cancer research. In G. A. Tsihrintzis, M. Virvou, E. Sakkopoulos, & L. Jain (Eds.), Machine Learning Paradigms (Vol. 1, pp. 43–63). Springer, Cham. https://doi.org/10.1007/978-3-030-15628-2_3
  • Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15, 550. https://doi.org/10.1186/s13059-014-0550-8
  • Mahendran, N., Vincent, P. M. D. R., Srinivasan, K., & Chang, C. (2020). Machine learning based computational gene selection models: a survey, performance evaluation, open issues, and future research directions. Frontiers in Genetics, 11. https://doi.org/10.3389/fgene.2020.603808
  • Melouane, A., Ghanemi, A., Aubé, S., Yoshioka, M., & St-Amand, J. (2018). Differential gene expression analysis in ageing muscle and drug discovery perspectives. Ageing Research Reviews, 41, 53-63. https://doi.org/10.1016/j.arr.2017.10.006
  • Rapaport, F., Khanin, R., Liang, Y., Pirun, M., Krek, A., Zumbo, P., Mason, C. E., Socci, N. D., & Betel, D. (2013). Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biology, 14, 3158. https://doi.org/10.1186/gb-2013-14-9-r95
  • Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2009). edgeR: Empirical analysis of digital gene expression data in R. Bioconductor. https://bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf
  • Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010). edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139-140. https://doi.org/10.1093/bioinformatics/btp616
  • Robles, J. A., Qureshi, S. E., Stephen, S. J., Wilson, S. R., Burden, C. J., & Taylor, J. M. (2012). Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing. BMC Genomics, 13(1), 484. https://doi.org/10.1186/1471-2164-13-484
  • Rosati, D., Palmieri, M., Brunelli, G., Morrione, A., Iannelli, F., Frullanti, E., & Giordano, A. (2024). Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review. Computational and Structural Biotechnology Journal, 23, 1154-1168. https://doi.org/10.1016/j.csbj.2024.02.018
  • Seyednasrollah, F., Laiho, A., & Elo, L. L. (2015). Comparison of software packages for detecting differential expression in RNA-seq studies. Briefings in Bioinformatics, 16(1), 59-70. https://doi.org/10.1093/bib/bbt086
  • van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. https://doi.org/10.1007/s11192-009-0146-3

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

Year 2025, Volume: 30 Issue: 1, 78 - 91, 29.04.2025

Necla Koçhan

https://doi.org/10.53433/yyufbed.1591489

Abstract

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.

Keywords

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

References

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Kvam, V. M., Liu, P., & Si, Y. (2012). A comparison of statistical methods for detecting differentially expressed genes from RNA‐seq data. American Journal of Botany, 99(2), 248-256. https://doi.org/10.3732/ajb.1100340
  • Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014). Voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology, 15(2), R29. https://doi.org/10.1186/gb-2014-15-2-r29
  • Li, J., & Tibshirani, R. (2013). Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data. Statistical Methods in Medical Research, 22(5), 519-536. https://doi.org/10.1177/0962280211428386
  • Liñares Blanco, J., Gestal, M., Dorado, J., & Fernandez-Lozano, C. (2019). Differential gene expression analysis of RNA-seq data using machine learning for cancer research. In G. A. Tsihrintzis, M. Virvou, E. Sakkopoulos, & L. Jain (Eds.), Machine Learning Paradigms (Vol. 1, pp. 43–63). Springer, Cham. https://doi.org/10.1007/978-3-030-15628-2_3
  • Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15, 550. https://doi.org/10.1186/s13059-014-0550-8
  • Mahendran, N., Vincent, P. M. D. R., Srinivasan, K., & Chang, C. (2020). Machine learning based computational gene selection models: a survey, performance evaluation, open issues, and future research directions. Frontiers in Genetics, 11. https://doi.org/10.3389/fgene.2020.603808
  • Melouane, A., Ghanemi, A., Aubé, S., Yoshioka, M., & St-Amand, J. (2018). Differential gene expression analysis in ageing muscle and drug discovery perspectives. Ageing Research Reviews, 41, 53-63. https://doi.org/10.1016/j.arr.2017.10.006
  • Rapaport, F., Khanin, R., Liang, Y., Pirun, M., Krek, A., Zumbo, P., Mason, C. E., Socci, N. D., & Betel, D. (2013). Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biology, 14, 3158. https://doi.org/10.1186/gb-2013-14-9-r95
  • Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2009). edgeR: Empirical analysis of digital gene expression data in R. Bioconductor. https://bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf
  • Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010). edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139-140. https://doi.org/10.1093/bioinformatics/btp616
  • Robles, J. A., Qureshi, S. E., Stephen, S. J., Wilson, S. R., Burden, C. J., & Taylor, J. M. (2012). Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing. BMC Genomics, 13(1), 484. https://doi.org/10.1186/1471-2164-13-484
  • Rosati, D., Palmieri, M., Brunelli, G., Morrione, A., Iannelli, F., Frullanti, E., & Giordano, A. (2024). Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review. Computational and Structural Biotechnology Journal, 23, 1154-1168. https://doi.org/10.1016/j.csbj.2024.02.018
  • Seyednasrollah, F., Laiho, A., & Elo, L. L. (2015). Comparison of software packages for detecting differential expression in RNA-seq studies. Briefings in Bioinformatics, 16(1), 59-70. https://doi.org/10.1093/bib/bbt086
  • van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. https://doi.org/10.1007/s11192-009-0146-3
There are 24 citations in total.

Details

Primary Language English
Subjects Biostatistics, Statistical Analysis
Journal Section Natural Sciences and Mathematics / Fen Bilimleri ve Matematik
Authors

Necla Koçhan 0000-0003-2355-4826

Publication Date April 29, 2025
Submission Date November 27, 2024
Acceptance Date February 24, 2025
Published in Issue Year 2025 Volume: 30 Issue: 1

Cite

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
 Download Cover Image