GC4miRNA - a Pipeline for Examining Impact of GC Content in miRNA Seed Sequences on Expression in Tumor Samples
Adv. Sci. Technol. Eng. Syst. J. 11(1), 78–83 (2026);
DOI: 10.25046/aj110108
MicroRNAs (miRNAs) are small RNA molecules that play a crucial role in regulating gene expression by binding to and degrading targeted mRNAs. miRNAs targeting a specific mRNA have a region known as the “seed sequence”, which typically has a high affinity for its complementary sequence in the targeted mRNA. Single Nucleotide Polymorphisms (SNPs) are mutations that refer to the substitution of a single nucleotide. Alterations in the nucleotides of seed sequences can have a significant impact on the targeting strength between miRNAs and mRNAs, potentially resulting in dysregulation of genes, and causing various diseases, including cancers. It is crucial to assess the impact of SNPs will have on nucleotides in specific seed sequences by gauging a common factor (e.g. GC content) reflecting the binding affinity. GC content is an essential aspect in miRNA binding, since high GC content miRNAs are frequently more stable, and may have a stronger affinity for their targets. To reveal the GC content signature for cancer-associated miRNAs, we developed a bioinformatics pipeline called GC4miRNA, which can calculate GC percentage enrichment in a sequence and perform statistical analysis to compare the GC content between the seed sequence and whole sequence for targeting miRNAs using customized BASH and R scripts. The pipeline was implemented as an R Shiny application that carries out several tasks/modules, such as measuring similarities between miRNAs whose dysregulation is linked to cancer and identifying common patterns within miRNAs with greater GC content.
- S.Y. Ying, D.C. Chang, S.L. Lin, “The microRNA (miRNA): overview of the RNA genes that modulate gene function”, Molecular Biotechnology, 38(3):257-268, 2008, DOI: https://doi.org/10.1007/s12033-007-9013-8
- M. Marzec, “New insights into the function of mammalian Argonaute2”, PLoS Genetics, 16(11):e1009058, 2020, DOI: https://doi.org/10.1371/journal.pgen.1009058
- C. Li, C. Mou, M.D. Swartz, B. Yu, Y. Bai, Y. Tu, X. Liu, “dbMTS: A comprehensive database of putative human microRNA target site SNVs and their functional predictions”, Human Mutation, 41(6):1123-1130, 2020, DOI: https://doi.org/10.1002/humu.24020
- A. Auton, L.D. Brooks, R.M. Durbin, E.P. Garrison, H.M. Kang, J.O. Korbel, J.L. Marchini, S. McCarthy, G.A. McVean, G.R. Abecasis, “A global reference for human genetic variation”, Nature, 526(7571):68-74, 2015, DOI: https://doi.org/10.1038/nature15393
- T.L. Bailey, J. Johnson, C.E. Grant, W.S. Noble, “The MEME Suite”, Nucleic Acids Research, 43(W1):W39-W49, 2015, DOI: https://doi.org/10.1093/nar/gkv416
- Y. Bai, L. Ding, S. Baker, et al., “Dissecting the biological relationship between TCGA miRNA and mRNA sequencing data using MMiRNA-Viewer”, BMC Bioinformatics, 17(13):336, 2016, DOI: https://doi.org/10.1186/s12859-016-1219-y
- V. Agarwal, G.W. Bell, J.W. Nam, D.P. Bartel, “Predicting effective microRNA target sites in mammalian mRNAs”, eLife, 4:e05005, 2015, DOI: https://doi.org/10.7554/eLife.05005
- A. Oulas, N. Karathanasis, A. Louloupi, I. Iliopoulos, K. Kalantidis, P. Poirazi, “A new microRNA target prediction tool identifies a novel interaction of a putative miRNA with CCND2”, RNA Biology, 9(9):1196-1207, 2012, DOI: https://doi.org/10.4161/rna.21725
- B. John, A.J. Enright, A. Aravin, T. Tuschl, C. Sander, D.S. Marks, “Human MicroRNA targets”, PLoS Biology, 2(11):e363, 2004, DOI: https://doi.org/10.1371/journal.pbio.0020363
- G.E. Crooks, G. Hon, J.M. Chandonia, S.E. Brenner, “WebLogo: a sequence logo generator”, Genome Research, 14(6):1188-1190, 2004, DOI: https://doi.org/10.1101/gr.849004
- S. Griffiths-Jones, R.J. Grocock, S. van Dongen, A. Bateman, A.J. Enright, “miRBase: microRNA sequences, targets and gene nomenclature”, Nucleic Acids Research, 34:D140-D144, 2006, DOI: https://doi.org/10.1093/nar/gkj112
- VectorBuilder, “GC-content-Calculator”, VectorBuilder, 2025, URL: https://en.vectorbuilder.com/tool/gc-content-calculator.html
- Abbykatb, “Abby’s Amazing GC Calculator”, GitHub, 2025, URL: https://github.com/abbykatb/Abbys-Amazing-GC-Calculator
- Z. Yang, F. Ren, C. Liu, S. He, G. Sun, Q. Gao, L. Yao, Y. Zhang, R. Miao, Y. Cao, et al., “dbDEMC: a database of differentially expressed miRNAs in human cancers”, BMC Genomics, 11(4):S5, 2010, DOI: https://doi.org/10.1186/1471-2164-11-S4-S5
- L. Kolberg, U. Raudvere, I. Kuzmin, J. Vilo, H. Peterson, “gprofiler2 — an R package for gene list functional enrichment analysis and namespace conversion toolset”, F1000Research, 9:ELIXIR-709, 2020, DOI: https://doi.org/10.12688/f1000research.24956.2
- G. Bindea, B. Mlecnik, H. Hackl, P. Charoentong, M. Tosolini, A. Kirilovsky, W.H. Fridman, F. Pagès, Z. Trajanoski, J. Galon, “ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks”, Bioinformatics, 25(8):1091-1093, 2009, DOI: https://doi.org/10.1093/bioinformatics/btp101
- S. Yao, A. Bee, D. Brewer, A. Dodson, C. Beesley, Y. Ke, L. Ambroisine, G. Fisher, H. Møller, T. Dickinson, et al., “PRKC-ζ Expression Promotes the Aggressive Phenotype of Human Prostate Cancer Cells and Is a Novel Target for Therapeutic Intervention”, Genes & Cancer, 1(5):444-464, 2010, DOI: https://doi.org/10.1177/1947601910376079
- X. Wang, “Composition of seed sequence is a major determinant of microRNA targeting patterns”, Bioinformatics, 30(10):1377-1383, 2014, DOI: https://doi.org/10.1093/bioinformatics/btu045
- P. Agrawal, G. Olgun, A. Singh, V. Gopalan, S. Hannenhalli, “Characterizing the role of exosomal miRNAs in metastasis”, bioRxiv, 2024:2024.08.20.608894, 2024, DOI: https://doi.org/10.1101/2024.08.20.608894
- S. Gupta, J.A. Stamatoyannopoulos, T.L. Bailey, W.S. Noble, “Quantifying similarity between motifs”, Genome Biology, 8(2):R24, 2007, DOI: https://doi.org/10.1186/gb-2007-8-2-r24
- S.O. Heyliger, K.F.A. Soliman, M.D. Saulsbury, R.R. Reams, “The Identification of Zinc-Finger Protein 433 as a Possible Prognostic Biomarker for Clear-Cell Renal Cell Carcinoma”, Biomolecules, 11(8):1193, 2021, DOI: https://doi.org/10.3390/biom11081193
- L. Wang, Q. Li, Z. Ye, B. Qiao, “ZBTB7/miR-137 Autoregulatory Circuit Promotes the Progression of Renal Carcinoma”, Oncology Research, 27(9):1007-1014, 2019, DOI: https://doi.org/10.3727/096504018X15231148037228
- W. Wang, W. Gu, H. Tang, Z. Mai, H. Xiao, J. Zhao, J. Han, “The Emerging Role of MTHFD Family Genes in Regulating the Tumor Immunity of Oral Squamous Cell Carcinoma”, Journal of Oncology, 2022:4867730, 2022, DOI: https://doi.org/10.1155/2022/4867730
- L. Zhao, K. Liu, X. Pan, J. Quan, L. Zhou, Z. Li, C. Lin, J. Xu, W. Xu, X. Guan, H. Li, L. Ni, Y. Gui, Y. Lai, “miR-625-3p promotes migration and invasion and reduces apoptosis of clear cell renal cell carcinoma”, American Journal of Translational Research, 11(10):6475-6486, 2019.
- J. Zhang, R. Bajari, D. Andric, et al., “The International Cancer Genome Consortium Data Portal”, Nature Biotechnology, 37(4):367-369, 2019, DOI: https://doi.org/10.1038/s41587-019-0055-9