Translation efficiency covariation identifies conserved coordination patterns across cell types
Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).
Google Scholar
Nagalakshmi, U. et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320, 1344–1349 (2008).
Google Scholar
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).
Google Scholar
Schena, M., Shalon, D., Davis, R. W. & Brown, P. O. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470 (1995).
Google Scholar
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).
Google Scholar
Combs, P. A. & Eisen, M. B. Sequencing mRNA from cryo-sliced Drosophila embryos to determine genome-wide spatial patterns of gene expression. PLoS ONE 8, e71820 (2013).
Google Scholar
Achim, K. et al. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat. Biotechnol. 33, 503–509 (2015).
Google Scholar
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).
Google Scholar
Eisen, M. B., Spellman, P. T., Brown, P. O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA 95, 14863–14868 (1998).
Google Scholar
Skinnider, M. A., Squair, J. W. & Foster, L. J. Evaluating measures of association for single-cell transcriptomics. Nat. Methods 16, 381–386 (2019).
Google Scholar
Stuart, J. M., Segal, E., Koller, D. & Kim, S. K. A gene-coexpression network for global discovery of conserved genetic modules. Science 302, 249–255 (2003).
Google Scholar
Marcotte, E. M., Pellegrini, M., Thompson, M. J., Yeates, T. O. & Eisenberg, D. A combined algorithm for genome-wide prediction of protein function. Nature 402, 83–86 (1999).
Google Scholar
DeRisi, J. L., Iyer, V. R. & Brown, P. O. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278, 680–686 (1997).
Google Scholar
Jansen, R., Greenbaum, D. & Gerstein, M. Relating whole-genome expression data with protein–protein interactions. Genome Res. 12, 37–46 (2002).
Google Scholar
Szklarczyk, D. et al. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 51, D638–D646 (2023).
Google Scholar
Tavazoie, S., Hughes, J. D., Campbell, M. J., Cho, R. J. & Church, G. M. Systematic determination of genetic network architecture. Nat. Genet. 22, 281–285 (1999).
Google Scholar
Roth, F. P., Hughes, J. D., Estep, P. W. & Church, G. M. Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation. Nat. Biotechnol. 16, 939–945 (1998).
Google Scholar
Nusinow, D. P. et al. Quantitative proteomics of the Cancer Cell Line Encyclopedia. Cell 180, 387–402 (2020).
Google Scholar
Gonçalves, E. et al. Pan-cancer proteomic map of 949 human cell lines. Cancer Cell 40, 835–849 (2022).
Google Scholar
Ryan, C. J., Kennedy, S., Bajrami, I., Matallanas, D. & Lord, C. J. A compendium of co-regulated protein complexes in breast cancer reveals collateral loss events. Cell Syst. 5, 399–409 (2017).
Google Scholar
Singh, G., Pratt, G., Yeo, G. W. & Moore, M. J. The clothes make the mRNA: past and present trends in mRNP fashion. Annu. Rev. Biochem. 84, 325–354 (2015).
Google Scholar
Keene, J. D. & Tenenbaum, S. A. Eukaryotic mRNPs may represent posttranscriptional operons. Mol. Cell 9, 1161–1167 (2002).
Google Scholar
Keene, J. D. RNA regulons: coordination of post-transcriptional events. Nat. Rev. Genet. 8, 533–543 (2007).
Google Scholar
Li, G.-W., Burkhardt, D., Gross, C. & Weissman, J. S. Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources. Cell 157, 624–635 (2014).
Google Scholar
Taggart, J. C. & Li, G.-W. Production of protein-complex components is stoichiometric and lacks general feedback regulation in eukaryotes. Cell Syst. 7, 580–589 (2018).
Google Scholar
Amirbeigiarab, S. et al. Invariable stoichiometry of ribosomal proteins in mouse brain tissues with aging. Proc. Natl Acad. Sci. USA 116, 22567–22572 (2019).
Google Scholar
Soto, I. et al. Balanced mitochondrial and cytosolic translatomes underlie the biogenesis of human respiratory complexes. Genome Biol. 23, 170 (2022).
Google Scholar
Natan, E. et al. Cotranslational protein assembly imposes evolutionary constraints on homomeric proteins. Nat. Struct. Mol. Biol. 25, 279–288 (2018).
Google Scholar
Li, G.-W., Oh, E. & Weissman, J. S. The anti-Shine–Dalgarno sequence drives translational pausing and codon choice in bacteria. Nature 484, 538–541 (2012).
Google Scholar
Bertolini, M. et al. Interactions between nascent proteins translated by adjacent ribosomes drive homomer assembly. Science 371, 57–64 (2021).
Google Scholar
Ozadam, H., Geng, M. & Cenik, C. RiboFlow, RiboR and RiboPy: an ecosystem for analyzing ribosome profiling data at read length resolution. Bioinformatics 36, 2929–2931 (2020).
Google Scholar
Gerashchenko, M. V. & Gladyshev, V. N. Ribonuclease selection for ribosome profiling. Nucleic Acids Res. 45, e6 (2017).
Google Scholar
Mohammad, F., Green, R. & Buskirk, A. R. A systematically-revised ribosome profiling method for bacteria reveals pauses at single-codon resolution. eLife 8, e42591 (2019).
Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. S. & Weissman, J. S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218–223 (2009).
Google Scholar
Larsson, O., Sonenberg, N. & Nadon, R. Identification of differential translation in genome wide studies. Proc. Natl Acad. Sci. USA 107, 21487–21492 (2010).
Google Scholar
van den Boogaart, K. G., Filzmoser, P., Hron, K., Templ, M. & Tolosana-Delgado, R. Classical and robust regression analysis with compositional data. Math. Geosci. 53, 823–858 (2021).
Quinn, T. P. et al. A field guide for the compositional analysis of any-omics data. Gigascience 8, giz107 (2019).
Quinn, T. P., Richardson, M. F., Lovell, D. & Crowley, T. M.propr: an R-package for identifying proportionally abundant features using compositional data analysis. Sci. Rep. 7, 16252 (2017).
Google Scholar
Sudmant, P. H., Alexis, M. S. & Burge, C. B. Meta-analysis of RNA-seq expression data across species, tissues and studies. Genome Biol. 16, 287 (2015).
Google Scholar
Wang, Z.-Y. et al. Transcriptome and translatome co-evolution in mammals. Nature 588, 642–647 (2020).
Google Scholar
Lu, P., Takai, K., Weaver, V. M. & Werb, Z. Extracellular matrix degradation and remodeling in development and disease. Cold Spring Harb. Perspect. Biol. 3, a005058 (2011).
Artieri, C. G. & Fraser, H. B. Evolution at two levels of gene expression in yeast. Genome Res. 24, 411–421 (2014).
Google Scholar
McManus, C. J., May, G. E., Spealman, P. & Shteyman, A. Ribosome profiling reveals post-transcriptional buffering of divergent gene expression in yeast. Genome Res. 24, 422–430 (2014).
Google Scholar
Breschi, A., Gingeras, T. R. & Guigó, R. Comparative transcriptomics in human and mouse. Nat. Rev. Genet. 18, 425–440 (2017).
Google Scholar
Crow, M., Suresh, H., Lee, J. & Gillis, J. Coexpression reveals conserved gene programs that co-vary with cell type across kingdoms. Nucleic Acids Res. 50, 4302–4314 (2022).
Google Scholar
Thoreen, C. C. et al. A unifying model for mTORC1-mediated regulation of mRNA translation. Nature 485, 109–113 (2012).
Google Scholar
Wurth, L. et al. UNR/CSDE1 drives a post-transcriptional program to promote melanoma invasion and metastasis. Cancer Cell 36, 337 (2019).
Google Scholar
Pierson, E. et al. Sharing and specificity of co-expression networks across 35 human tissues. PLoS Comput. Biol. 11, e1004220 (2015).
Google Scholar
Kershaw, C. J. et al. Translation factor and RNA binding protein mRNA interactomes support broader RNA regulons for posttranscriptional control. J. Biol. Chem. 299, 105195 (2023).
Google Scholar
Hentze, M. W., Castello, A., Schwarzl, T. & Preiss, T. A brave new world of RNA-binding proteins. Nat. Rev. Mol. Cell Biol. 19, 327–341 (2018).
Google Scholar
Liu, Y. The number of genes whose TE significantly correlates with an RBP’s expression. Zenodo https://doi.org/10.5281/zenodo.11359114 (2024).
Korbel, J. O., Jensen, L. J., von Mering, C. & Bork, P. Analysis of genomic context: prediction of functional associations from conserved bidirectionally transcribed gene pairs. Nat. Biotechnol. 22, 911–917 (2004).
Google Scholar
Szklarczyk, R. et al. WeGET: predicting new genes for molecular systems by weighted co-expression. Nucleic Acids Res. 44, D567–D573 (2016).
Google Scholar
Zhang, M. et al. RNA-binding protein IMP3 is a novel regulator of MEK1/ERK signaling pathway in the progression of colorectal cancer through the stabilization of MEKK1 mRNA. J. Exp. Clin. Cancer Res. 40, 200 (2021).
Google Scholar
Bodén, M. & Bailey, T. L. Associating transcription factor-binding site motifs with target GO terms and target genes. Nucleic Acids Res. 36, 4108–4117 (2008).
Google Scholar
Machanick, P. & Bailey, T. L. MEME-ChIP: motif analysis of large DNA datasets. Bioinformatics 27, 1696–1697 (2011).
Google Scholar
Eichhorn, S. W. et al. mRNA destabilization is the dominant effect of mammalian microRNAs by the time substantial repression ensues. Mol. Cell 56, 104–115 (2014).
Google Scholar
Bartel, D. P. Metazoan microRNAs. Cell 173, 20–51 (2018).
Google Scholar
Mecham, R. The Extracellular Matrix: An Overview (Springer Science & Business Media, 2011).
Kagan, H. M. & Li, W. Lysyl oxidase: properties, specificity, and biological roles inside and outside of the cell. J. Cell. Biochem. 88, 660–672 (2003).
Google Scholar
Kikuchi, A. et al. Structural basis for activation of DNMT1. Nat. Commun. 13, 7130 (2022).
Google Scholar
Wu, Y.-Y. et al. The hTERT-p50 homodimer inhibits PLEKHA7 expression to promote gastric cancer invasion and metastasis. Oncogene 42, 1144–1156 (2023).
Google Scholar
Kurita, S., Yamada, T., Rikitsu, E., Ikeda, W. & Takai, Y. Binding between the junctional proteins afadin and PLEKHA7 and implication in the formation of adherens junction in epithelial cells. J. Biol. Chem. 288, 29356–29368 (2013).
Google Scholar
Pulimeno, P., Paschoud, S. & Citi, S. A role for ZO-1 and PLEKHA7 in recruiting paracingulin to tight and adherens junctions of epithelial cells. J. Biol. Chem. 286, 16743–16750 (2011).
Google Scholar
Jeung, H.-C. et al. PLEKHA7 signaling is necessary for the growth of mutant KRAS driven colorectal cancer. Exp. Cell. Res. 409, 112930 (2021).
Google Scholar
Tavano, S. et al. Insm1 induces neural progenitor delamination in developing neocortex via downregulation of the adherens junction belt-specific protein Plekha7. Neuron 97, 1299–1314 (2018).
Google Scholar
Sukonina, V. et al. FOXK1 and FOXK2 regulate aerobic glycolysis. Nature 566, 279–283 (2019).
Google Scholar
Kobe, B. & Kajava, A. V. The leucine-rich repeat as a protein recognition motif. Curr. Opin. Struct. Biol. 11, 725–732 (2001).
Google Scholar
Evans, R. et al. Protein complex prediction with AlphaFold-Multimer. Preprint at bioRxiv https://doi.org/10.1101/2021.10.04.463034 (2021).
Carlsson, P. & Mahlapuu, M. Forkhead transcription factors: key players in development and metabolism. Dev. Biol. 250, 1–23 (2002).
Google Scholar
Lambert, S. A. et al. The human transcription factors. Cell 172, 650–665 (2018).
Google Scholar
Kustatscher, G. et al. Co-regulation map of the human proteome enables identification of protein functions. Nat. Biotechnol. 37, 1361–1371 (2019).
Google Scholar
Szklarczyk, D. et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452 (2015).
Google Scholar
Shiber, A. et al. Cotranslational assembly of protein complexes in eukaryotes revealed by ribosome profiling. Nature 561, 268–272 (2018).
Google Scholar
Ewing, R. M. et al. Large-scale mapping of human protein–protein interactions by mass spectrometry. Mol. Syst. Biol. 3, 89 (2007).
Google Scholar
Drew, K., Wallingford, J. B. & Marcotte, E. M. hu.MAP 2.0: integration of over 15,000 proteomic experiments builds a global compendium of human multiprotein assemblies. Mol. Syst. Biol. 17, e10016 (2021).
Google Scholar
Heider, M. R. et al. Subunit connectivity, assembly determinants and architecture of the yeast exocyst complex. Nat. Struct. Mol. Biol. 23, 59–66 (2016).
Google Scholar
Kee, Y. et al. Subunit structure of the mammalian exocyst complex. Proc. Natl Acad. Sci. USA 94, 14438–14443 (1997).
Google Scholar
Lalanne, J.-B. et al. Evolutionary convergence of pathway-specific enzyme expression stoichiometry. Cell 173, 749–761 (2018).
Google Scholar
Bicknell, A. A. et al. Attenuating ribosome load improves protein output from mRNA by limiting translation-dependent mRNA decay. Cell Rep. 43, 114098 (2024).
Google Scholar
Liu, T.-Y. et al. Time-resolved proteomics extends ribosome profiling-based measurements of protein synthesis dynamics. Cell Syst. 4, 636–644 (2017).
Google Scholar
Wang, M., Herrmann, C. J., Simonovic, M., Szklarczyk, D. & von Mering, C. Version 4.0 of PaxDb: protein abundance data, integrated across model organisms, tissues, and cell-lines. Proteomics 15, 3163–3168 (2015).
Google Scholar
Piepoli, A. et al. The expression of leucine-rich repeat gene family members in colorectal cancer. Exp. Biol. Med. 237, 1123–1128 (2012).
Google Scholar
Liu, Y. et al. Identification of differential expression of genes in hepatocellular carcinoma by suppression subtractive hybridization combined cDNA microarray. Oncol. Rep. 18, 943–951 (2007).
Google Scholar
Chen, H. et al. miR-218 contributes to drug resistance in multiple myeloma via targeting LRRC28. J. Cell. Biochem. 122, 305–314 (2021).
Google Scholar
Vander Heiden, M. G., Cantley, L. C. & Thompson, C. B. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029–1033 (2009).
Liu, Y. et al. Histone H2AX promotes metastatic progression by preserving glycolysis via hexokinase-2. Sci. Rep. 12, 3758 (2022).
Google Scholar
Zheng, D. et al. Predicting the translation efficiency of messenger RNA in mammalian cells. Nat. Bio. https://doi.org/10.1038/s41587-025-02712-x (2025).
Rodriguez, J. M. et al. APPRIS: annotation of principal and alternative splice isoforms. Nucleic Acids Res. 41, D110–D117 (2013).
Google Scholar
Rao, S. et al. Genes with 5′ terminal oligopyrimidine tracts preferentially escape global suppression of translation by the SARS-CoV-2 Nsp1 protein. RNA 27, 1025–1045 (2021).
Google Scholar
Mills, E. W. & Green, R. Ribosomopathies: there’s strength in numbers. Science 358, eaan2755 (2017).
Ozadam, H. et al. Single-cell quantification of ribosome occupancy in early mouse development. Nature 618, 1057–1064 (2023).
Google Scholar
VanInsberghe, M., van den Berg, J., Andersson-Rolf, A., Clevers, H. & van Oudenaarden, A. Single-cell Ribo-seq reveals cell cycle-dependent translational pausing. Nature 597, 561–565 (2021).
Google Scholar
Benoit Bouvrette, L. P., Bovaird, S., Blanchette, M. & Lécuyer, E. oRNAment: a database of putative RNA binding protein target sites in the transcriptomes of model species. Nucleic Acids Res. 48, D166–D173 (2020).
Google Scholar
Krismer, K. et al. Transite: a computational motif-based analysis platform that identifies RNA-binding proteins modulating changes in gene expression. Cell Rep. 32, 108064 (2020).
Google Scholar
Van Nostrand, E. L. et al. A large-scale binding and functional map of human RNA-binding proteins. Nature 583, 711–719 (2020).
Google Scholar
Hou, Y., Xie, T., He, L., Tao, L. & Huang, J. Topological links in predicted protein complex structures reveal limitations of AlphaFold. Commun. Biol. 6, 1098 (2023).
Google Scholar
Burke, D. F. et al. Towards a structurally resolved human protein interaction network. Nat. Struct. Mol. Biol. 30, 216–225 (2023).
Google Scholar
Bryant, P., Pozzati, G. & Elofsson, A. Improved prediction of protein-protein interactions using AlphaFold2. Nat. Commun. 13, 1265 (2022).
Google Scholar
National Center for Biotechnology Information. SRA Tools. GitHub https://github.com/ncbi/sra-tools (2018).
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Google Scholar
Liu, Y. HeLa ribosome profiling data. Zenodo https://doi.org/10.5281/zenodo.15660080 (2024).
Gerashchenko, M. V. & Gladyshev, V. N. Translation inhibitors cause abnormalities in ribosome profiling experiments. Nucleic Acids Res. 42, e134 (2014).
Google Scholar
Wu, C. C.-C., Zinshteyn, B., Wehner, K. A. & Green, R. High-resolution ribosome profiling defines discrete ribosome elongation states and translational regulation during cellular stress. Mol. Cell 73, 959–970 (2019).
Google Scholar
Wolin, S. L. & Walter, P. Ribosome pausing and stacking during translation of a eukaryotic mRNA. EMBO J. 7, 3559–3569 (1988).
Google Scholar
Sharma, J. et al. A small molecule that induces translational readthrough of CFTR nonsense mutations by eRF1 depletion. Nat. Commun. 12, 4358 (2021).
Google Scholar
Tukey, J. W. The future of data analysis. Ann. Math. Stat. 33, 1–67 (1962).
Zhang, X.-O., Yin, Q.-F., Chen, L.-L. & Yang, L. Gene expression profiling of non-polyadenylated RNA-seq across species. Genom. Data 2, 237–241 (2014).
Google Scholar
Yang, L., Duff, M. O., Graveley, B. R., Carmichael, G. G. & Chen, L.-L. Genomewide characterization of non-polyadenylated RNAs. Genome Biol. 12, R16 (2011).
Google Scholar
van den Boogaart, K. G. & Tolosano-Delgado, R. Analyzing Compositional Data with R (Springer, 2013).
Cenik, C. et al. Integrative analysis of RNA, translation, and protein levels reveals distinct regulatory variation across humans. Genome Res. 25, 1610–1621 (2015).
Google Scholar
Greenacre, M. Compositional data analysis. Annu. Rev. Stat. Appl. 8, 271–299 (2021).
Ramsköld, D., Wang, E. T., Burge, C. B. & Sandberg, R. An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data. PLoS Comput. Biol. 5, e1000598 (2009).
Google Scholar
Csárdi, G., Franks, A., Choi, D. S., Airoldi, E. M. & Drummond, D. A. Accounting for experimental noise reveals that mRNA levels, amplified by post-transcriptional processes, largely determine steady-state protein levels in yeast. PLoS Genet. 11, e1005206 (2015).
Google Scholar
Schilder, B. M. & Skene, N. G. orthogene: An R package for easy mapping of orthologous genes across hundreds of species. R package version 3.21 https://doi.org/10.18129/B9.bioc.orthogene (2022).
van den Boogaart, K. G. & Tolosana-Delgado, R. ‘compositions’: a unified R package to analyze compositional data. Comput. Geosci. 34, 320–338 (2008).
Kim, S. ppcor: an R package for a fast calculation to semi-partial correlation coefficients. Commun. Stat. Appl. Methods 22, 665–674 (2015).
Google Scholar
Berriz, G. F., Beaver, J. E., Cenik, C., Tasan, M. & Roth, F. P. Next generation software for functional trend analysis. Bioinformatics 25, 3043–3044 (2009).
Google Scholar
Buttrey, S. & Whitaker, L. TreeClust: an R package for tree-based clustering dissimilarities. R J. 7, 227 (2015).
Wainberg, M. et al. A genome-wide atlas of co-essential modules assigns function to uncharacterized genes. Nat. Genet. 53, 638–649 (2021).
Google Scholar
Gene Ontology Consortium et al. The Gene Ontology knowledgebase in 2023. Genetics 224, iyad031 (2023).
Philippe, L., van den Elzen, A. M. G., Watson, M. J. & Thoreen, C. C. Global analysis of LARP1 translation targets reveals tunable and dynamic features of 5′ TOP motifs. Proc. Natl Acad. Sci. USA 117, 5319–5328 (2020).
Google Scholar
Ballouz, S., Weber, M., Pavlidis, P. & Gillis, J. EGAD: ultra-fast functional analysis of gene networks. Bioinformatics 33, 612–614 (2017).
Google Scholar
Carlson, M. org.Mm.eg.db: Genome wide annotation for mouse. R package version 3.21 https://doi.org/10.18129/B9.bioc.org.Mm.eg.db (2025).
Carlson, M. org.Hs.eg.db: Genome wide annotation for human. R package version 3.21 https://doi.org/10.18129/B9.bioc.org.Hs.eg.db (2025).
Liu, Y. Intermediate data for TE calculation. Zenodo https://doi.org/10.5281/zenodo.10373032 (2024).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Google Scholar
The UniProt Consortium. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res. 51, D523–D531 (2023).
Hu, Y. et al. Paralog Explorer: a resource for mining information about paralogs in common research organisms. Comput. Struct. Biotechnol. J. 20, 6570–6577 (2022).
Google Scholar
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Google Scholar
Ho, D., Imai, K., King, G. & Stuart, E. A. MatchIt: nonparametric preprocessing for parametric causal inference. J. Stat. Softw. https://doi.org/10.18637/jss.v042.i08 (2011).
Sanson, K. R. et al. Optimized libraries for CRISPR–Cas9 genetic screens with multiple modalities. Nat. Commun. 9, 5416 (2018).
Google Scholar
Sanjana, N. E., Shalem, O. & Zhang, F. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783–784 (2014).
Google Scholar
Liu, Y. KO_validation_RiboBase. Zenodo https://doi.org/10.5281/zenodo.11388478 (2024).
Yue, L. coTE_paper: code and to generate main figures. Zenodo https://doi.org/10.5281/zenodo.15337774 (2025).
