A Trans-Omics Comparison Reveals Common Gene Expression Strategies in Four Model Organisms and Exposes Similarities and Differences between Them
Open accessPublished version of a preprint
Abstract
The ultimate goal of gene expression regulation is on the protein level. However, because the amounts of mRNAs and proteins are controlled by their synthesis and degradation rates, the cellular amount of a given protein can be attained by following different strategies. By studying omics data for six expression variables (mRNA and protein amounts, plus their synthesis and decay rates), we previously demonstrated the existence of common expression strategies (CESs) for functionally related genes in the yeast Saccharomyces cerevisiae. Here we extend that study to two other eukaryotes: the yeast Schizosaccharomyces pombe and cultured human HeLa cells. We also use genomic data from the model prokaryote Escherichia coli as an external reference. We show that six-variable profiles (6VPs) can be constructed for every gene and that these 6VPs are similar for genes with similar functions in all the studied organisms. The differences in 6VPs between organisms can be used to establish their phylogenetic relationships. The analysis of the correlations among the six variables supports the hypothesis that most gene expression control occurs in actively growing organisms at the transcription rate level, and that translation plays a minor role. We propose that living organisms use CESs for the genes acting on the same physiological pathways, especially for those belonging to stable macromolecular complexes, but CESs have been modeled by evolution to adapt to the specific life circumstances of each organism.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
Global quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566 · cited 2× · see citing works
“In fact, this has been previously observed for the three eukaryotes studied herein [3,35,39,62].”
BackgroundREVIGO summarizes and visualizes long lists of gene ontology terms
Supek, F., Bošnjak, M., Škunca, N., & Šmuc, T. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021800 · Cited by 6,264 · see citing works
Insights into the regulation of protein abundance from proteomic and transcriptomic analyses
Vogel, C., & Marcotte, E. M. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3185 · Cited by 4,195 · see citing works
Genome-Wide Analysis in Vivo of Translation with Nucleotide Resolution Using Ribosome Profiling
Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. S., & Weissman, J. S. (2009) · Science · doi:10.1126/science.1168978 · Cited by 3,853 · see citing works
An Information-Theoretic Definition of Similarity
Lin, D. (1998) · Cited by 3,803 · see citing works
phangorn: phylogenetic analysis in R
Schliep, K. P. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq706 · Cited by 3,678 · see citing works
On the Dependency of Cellular Protein Levels on mRNA Abundance
Liu, Y., Beyer, A., & Aebersold, R. (2016) · Cell · doi:10.1016/j.cell.2016.03.014 · Cited by 3,221 · see citing works
Resnik, P. (1999) · Journal of Artificial Intelligence Research · doi:10.1613/jair.514 · Cited by 2,098 · see citing works
Kulak, N. A., Pichler, G., Paron, I., Nagaraj, N., & Mann, M. (2014) · Nature Methods · doi:10.1038/nmeth.2834 · Cited by 1,719 · see citing works
Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise
Newman, J. R. S., Ghaemmaghami, S., Ihmels, J., Breslow, D. K., Noble, M., DeRisi, J. L., & Weissman, J. S. (2006) · Nature · doi:10.1038/nature04785 · Cited by 1,483 · see citing works
Li, G., Burkhardt, D. H., Gross, C. A., & Weissman, J. S. (2014) · Cell · doi:10.1016/j.cell.2014.02.033 · Cited by 1,218 · see citing works
Crick, F. (1958) · PubMed · Cited by 1,188 · see citing works
Codon Optimality Is a Major Determinant of mRNA Stability
Presnyak, V., Alhusaini, N., Chen, Y.-H., Martin, S., Morris, N., Kline, N., Olson, S., Weinberg, D., Baker, K. E., Graveley, B. R., et al. (2015) · Cell · doi:10.1016/j.cell.2015.02.029 · Cited by 1,078 · see citing works
Deep proteome and transcriptome mapping of a human cancer cell line
Nagaraj, N., Wisniewski, J. R., Geiger, T., Cox, J., Kircher, M., Kelso, J., Pääbo, S., & Mann, M. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.81 · Cited by 979 · see citing works
clValid : An R Package for Cluster Validation
Brock, G. N., Pihur, V., Datta, S., & Datta, S. (2008) · Journal of Statistical Software · doi:10.18637/jss.v025.i04 · Cited by 879 · see citing works
Codon optimality, bias and usage in translation and mRNA decay
Hanson, G., & Coller, J. (2018) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm.2017.91 · Cited by 825 · see citing works
Noise in protein expression scales with natural protein abundance
Bar-Even, A., Paulsson, J., Maheshri, N., Carmi, M., O'Shea, E., Pilpel, Y., & Barkai, N. (2006) · Nature Genetics · doi:10.1038/ng1807 · Cited by 807 · see citing works
The quantitative and condition-dependent Escherichia coli proteome
Schmidt, A., Kochanowski, K., Vedelaar, S. R., Ahrné, E., Volkmer, B., Callipo, L., Knoops, K., Bauer, M., Aebersold, R. H., & Heinemann, M. (2015) · Nature Biotechnology · doi:10.1038/nbt.3418 · Cited by 737 · see citing works
Precision and functional specificity in mRNA decay
Wang, Y., Liu, C. L., Storey, J. D., Tibshirani, R. J., Herschlag, D., & Brown, P. O. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.092538799 · Cited by 708 · see citing works
Quantification of protein half-lives in the budding yeast proteome
Belle, A., Tanay, A., Bitincka, L., Shamir, R., & O’Shea, E. K. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0605420103 · Cited by 678 · see citing works
The timing of eukaryotic evolution: Does a relaxed molecular clock reconcile proteins and fossils?
Douzery, E. J. P., Snell, E. A., Bapteste, E., Delsuc, F., & Philippe, H. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0403984101 · Cited by 638 · see citing works
Schwartz, S., Agarwala, S. D., Mumbach, M. R., Jovanović, M., Mertins, P., Shishkin, A., Tabach, Y., Mikkelsen, T. S., Satija, R., Ruvkun, G., et al. (2013) · Cell · doi:10.1016/j.cell.2013.10.047 · Cited by 627 · see citing works
A hierarchical unsupervised growing neural network for clustering gene expression patterns
Herrero, J., Valencia, A., & Dopazo, J. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.2.126 · Cited by 622 · see citing works
Marguerat, S., Schmidt, A., Codlin, S., Chen, W., Aebersold, R., & Bähler, J. (2012) · Cell · doi:10.1016/j.cell.2012.09.019 · Cited by 571 · see citing works
The EcoCyc database: reflecting new knowledge aboutEscherichia coliK-12
Keseler, I. M., Mackie, A. M., Santos-Zavaleta, A., Billington, R., Bonavides-Martínez, C., Caspi, R., Fulcher, C. A., Gama‐Castro, S., Kothari, A., Krummenacker, M., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1003 · Cited by 525 · see citing works
Unification of Protein Abundance Datasets Yields a Quantitative Saccharomyces cerevisiae Proteome
Ho, B., Baryshnikova, A., & Brown, G. W. (2018) · Cell Systems · doi:10.1016/j.cels.2017.12.004 · Cited by 482 · see citing works
The Genomic and Transcriptomic Landscape of a HeLa Cell Line
Landry, J. J. M., Pyl, P. T., Rausch, T., Zichner, T., Tekkedil, M. M., Stütz, A. M., Jauch, A. S., Aiyar, R. S., Pau, G., Delhomme, N., et al. (2013) · G3 Genes Genomes Genetics · doi:10.1534/g3.113.005777 · Cited by 455 · see citing works
Tani, H., Onoguchi‐Mizutani, R., Salam, K. A., Tano, K., Ijiri, K., Wakamatsu, A., Isogai, T., Suzuki, Y., & Akimitsu, N. (2012) · Genome Research · doi:10.1101/gr.130559.111 · Cited by 451 · see citing works
Slobodin, B., Han, R., Calderone, V., Vrielink, J. A. F. O., Loayza-Puch, F., Elkon, R., & Agami, R. (2017) · Cell · doi:10.1016/j.cell.2017.03.031 · Cited by 434 · see citing works
Systematic humanization of yeast genes reveals conserved functions and genetic modularity
Kachroo, A. H., Laurent, J. M., Yellman, C. M., Meyer, A. G., Wilke, C. O., & Marcotte, E. M. (2015) · Science · doi:10.1126/science.aaa0769 · Cited by 423 · see citing works
Noise Minimization in Eukaryotic Gene Expression
Fraser, H. B., Hirsh, A. E., Giaever, G. N., Kumm, J., & Eisen, M. B. (2004) · PLoS Biology · doi:10.1371/journal.pbio.0020137 · Cited by 419 · see citing works
Dynamic transcriptome analysis measures rates of mRNA synthesis and decay in yeast
Miller, C., Schwalb, B., Maier, K. C., Schulz, D., Dümcke, S., Zacher, B., Mayer, A., Sydow, J. F., Marcinowski, L., Dölken, L., et al. (2011) · Molecular Systems Biology · doi:10.1038/msb.2010.112 · Cited by 393 · see citing works
Gene ontology semantic similarity analysis using GOSemSim
Yu, G. (2020) · Methods in Molecular Biology · doi:10.1007/978-1-0716-0301-7_11 · Cited by 392 · see citing works
Grigull, J., Mnaimneh, S., Pootoolal, J., Robinson, M. D., & Hughes, T. R. (2004) · Molecular and Cellular Biology · doi:10.1128/mcb.24.12.5534-5547.2004 · Cited by 342 · see citing works
Model Averaging and Its Use in Economics
Steel, M. F. J. (2020) · Journal of Economic Literature · doi:10.1257/jel.20191385 · Cited by 321 · see citing works
Dujon, B. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2811 · Cited by 316 · see citing works
Comparative analysis of the transcriptome across distant species
Gerstein, M., Rozowsky, J. S., Yan, K., Wang, D., Cheng, C., Brown, J. B., Davis, C., Hillier, L., Sisu, C., Li, J. J., et al. (2014) · Nature · doi:10.1038/nature13424 · Cited by 290 · see citing works
Sun, M., Schwalb, B., Schulz, D., Pirkl, N., Etzold, S., Larivière, L., Maier, K. C., Seizl, M., Tresch, A., & Cramer, P. (2012) · Genome Research · doi:10.1101/gr.130161.111 · Cited by 289 · see citing works
Li, J. J., Bickel, P. J., & Biggin, M. D. (2014) · PeerJ · doi:10.7717/peerj.270 · Cited by 279 · see citing works
Systems-wide Proteomic Analysis in Mammalian Cells Reveals Conserved, Functional Protein Turnover
Cambridge, S. B., Gnad, F., Nguyen, C., Bermejo, J. L., Krüger, M., & Mann, M. (2011) · Journal of Proteome Research · doi:10.1021/pr101183k · Cited by 277 · see citing works
Yeast Proteome Dynamics from Single Cell Imaging and Automated Analysis
Chong, Y. T., Koh, J. L. Y., Friesen, H., Kaluarachchi Duffy, S., Cox, M. J., Moses, A., Moffat, J., Boone, C., & Andrews, B. J. (2015) · Cell · doi:10.1016/j.cell.2015.04.051 · Cited by 274 · see citing works
Global Proteome Turnover Analyses of the Yeasts S. cerevisiae and S. pombe
Christiano, R., Nagaraj, N., Fröhlich, F., & Walther, T. C. (2014) · Cell Reports · doi:10.1016/j.celrep.2014.10.065 · Cited by 268 · see citing works
Extensive quantitative remodeling of the proteome between normal colon tissue and adenocarcinoma
Wiśniewski, J. R., Ostasiewicz, P., Duś, K., Zielińska, D. F., Gnad, F., & Mann, M. (2012) · Molecular Systems Biology · doi:10.1038/msb.2012.44 · Cited by 248 · see citing works
Where does fission yeast sit on the tree of life?
Sipiczki, M. (2000) · Genome biology · doi:10.1186/gb-2000-1-2-reviews1011 · Cited by 243 · see citing works
García‐Martínez, J., Aranda, A., & Pérez-Ortín, J. E. (2004) · Molecular Cell · doi:10.1016/j.molcel.2004.06.004 · Cited by 243 · see citing works
Self-citationThe Genomic Tree as Revealed from Whole Proteome ComparisonsPreprint
Tekaia, F., Lazcano, A., & Dujon, B. (1999) · Genome Research · doi:10.1101/gr.9.6.550 · Cited by 242 · see citing works
An Ancient Yeast for Young Geneticists: A Primer on the Schizosaccharomyces pombe Model System
Hoffman, C. S., Wood, V., & Fantes, P. A. (2015) · Genetics · doi:10.1534/genetics.115.181503 · Cited by 238 · see citing works
Protein synthesis rate is the predominant regulator of protein expression during differentiation
Kristensen, A. R., Gsponer, J., & Foster, L. J. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.47 · Cited by 217 · see citing works
Can we predict protein from mRNA levels?
Fortelny, N., Overall, C. M., Pavlidis, P., & Freue, G. V. C. (2017) · Nature · doi:10.1038/nature22293 · Cited by 217 · see citing works
Chan, L. Y., Mugler, C. F., Heinrich, S., Vallotton, P., & Weis, K. (2018) · eLife · doi:10.7554/elife.32536 · Cited by 214 · see citing works
A Network of Multiple Regulatory Layers Shapes Gene Expression in Fission Yeast
Lackner, D. H., Beilharz, T. H., Marguerat, S., Mata, J., Watt, S., Schubert, F., Preiss, T., & Bähler, J. (2007) · Molecular Cell · doi:10.1016/j.molcel.2007.03.002 · Cited by 204 · see citing works
Lahtvee, P.-J., Sánchez, B. J., Smialowska, A., Kasvandik, S., Elsemman, I. E., Gatto, F., & Nielsen, J. (2017) · Cell Systems · doi:10.1016/j.cels.2017.03.003 · Cited by 193 · see citing works
Csárdi, G., Franks, A., Choi, D. S., Airoldi, E. M., & Drummond, D. A. (2015) · PLOS Genetics · doi:10.1371/journal.pgen.1005206 · Cited by 181 · see citing works
Gene Ontology: Pitfalls, Biases, and Remedies
Gaudet, P., & Dessimoz, C. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-3743-1_14 · Cited by 170 · see citing works
Statistics requantitates the central dogma
Li, J. J., & Biggin, M. D. (2015) · Science · doi:10.1126/science.aaa8332 · Cited by 164 · see citing works
A Complete Set of Nascent Transcription Rates for Yeast Genes
Pelechano, V., Chávez, S., & Pérez-Ortín, J. E. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015442 · Cited by 162 · see citing works
Self-citationProtein abundances are more conserved than mRNA abundances across diverse taxa
Laurent, J. M., Vogel, C., Kwon, T., Craig, S. A., Boutz, D. R., Huse, H. K., Nozue, K., Walia, H., Whiteley, M., Ronald, P. C., et al. (2010) · PROTEOMICS · doi:10.1002/pmic.201000327 · Cited by 156 · see citing works
Transcriptome-wide Analysis of Roles for tRNA Modifications in Translational Regulation
Chou, H.-J., Donnard, E. R., Gustafsson, H. T., Garber, M. G., & Rando, O. J. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.11.002 · Cited by 148 · see citing works
Valgepea, K., Adamberg, K., Seiman, A., & Vilu, R. (2013) · Molecular BioSystems · doi:10.1039/c3mb70119k · Cited by 145 · see citing works
Transcriptomics resources of human tissues and organs
Uhlén, M., Hallström, B. M., Lindskog, C., Mardinoğlu, A., Pontén, F., & Nielsen, J. (2016) · Molecular Systems Biology · doi:10.15252/msb.20155865 · Cited by 144 · see citing works
Eukaryotic mRNA Decay: Methodologies, Pathways, and Links to Other Stages of Gene Expression
Pérez-Ortín, J. E., Alepuz, P. M., Chávez, S., & Choder, M. (2013) · Journal of Molecular Biology · doi:10.1016/j.jmb.2013.02.029 · Cited by 142 · see citing works
Self-citationDessimoz, C., & Škunca, N. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-3743-1 · Cited by 139 · see citing works
Using Homolog Groups to Create a Whole-Genomic Tree of Free-Living Organisms: An Update
House, C. H., & Fitz‐Gibbon, S. T. (2002) · Journal of Molecular Evolution · doi:10.1007/s00239-001-0054-5 · Cited by 128 · see citing works
Determination of in vivo RNA kinetics using RATE-seq
Neymotin, B., Athanasiadou, R., & Gresham, D. (2014) · RNA · doi:10.1261/rna.045104.114 · Cited by 126 · see citing works
Genomics and gene transcription kinetics in yeast
Pérez-Ortín, J. E., Alepuz, P. M., & Moreno, J. (2007) · Trends in Genetics · doi:10.1016/j.tig.2007.03.006 · Cited by 122 · see citing works
Self-citationMcManus, C. J., Cheng, Z., & Vogel, C. (2015) · Molecular BioSystems · doi:10.1039/c5mb00310e · Cited by 103 · see citing works
A comprehensive, quantitative, and genome-wide model of translation
Siwiak, M., & Zielenkiewicz, P. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000865 · Cited by 93 · see citing works
Esquerré, T., Laguerre, S., Turlan, C., Carpousis, A. J., Girbal, L., & Cocaign‐Bousquet, M. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1150 · Cited by 87 · see citing works
Multiplexed gene control reveals rapid mRNA turnover
Baudrimont, A., Voegeli, S., Viloria, E. C., Stritt, F., Lénon, M., Wada, T., Jaquet, V., & Becskei, A. (2017) · Science Advances · doi:10.1126/sciadv.1700006 · Cited by 85 · see citing works
Determinants of RNA metabolism in the Schizosaccharomyces pombe genome
Eser, P., Wachutka, L., Maier, K. C., Demel, C., Boroni, M., Iyer, S., Cramer, P., & Gagneur, J. (2016) · Molecular Systems Biology · doi:10.15252/msb.20156526 · Cited by 80 · see citing works
Analysis of the association between codon optimality and mRNA stability in Schizosaccharomyces pombe
Harigaya, Y., & Parker, R. (2016) · BMC Genomics · doi:10.1186/s12864-016-3237-6 · Cited by 74 · see citing works
Metabolic labeling and recovery of nascent RNA to accurately quantify mRNA stability
Russo, J., Heck, A. M., Wilusz, J., & Wilusz, C. J. (2017) · Methods · doi:10.1016/j.ymeth.2017.02.003 · Cited by 68 · see citing works
Contribution of RNA Degradation to Intrinsic and Extrinsic Noise in Gene Expression
Baudrimont, A., Jaquet, V., Wallerich, S., Voegeli, S., & Becskei, A. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.03.001 · Cited by 66 · see citing works
Global coordination of transcriptional control and mRNA decay during cellular differentiation
Amorim, M. J., Cotobal, C., Duncan, C. D. S., & Mata, J. (2010) · Molecular Systems Biology · doi:10.1038/msb.2010.38 · Cited by 65 · see citing works
Carneiro, R. L., Requião, R. D., Rossetto, S., Domitrovic, T., & Palhano, F. L. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz033 · Cited by 56 · see citing works
Dynamics of transcriptional and post-transcriptional regulation
Furlan, M., de Pretis, S., & Pelizzola, M. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa389 · Cited by 46 · see citing works
Li, J. J., Chew, G.-L., & Biggin, M. D. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx898 · Cited by 36 · see citing works
Homeostasis in the Central Dogma of molecular biology: the importance of mRNA instability
Pérez-Ortín, J. E., Tordera, V., & Chávez, S. (2019) · RNA Biology · doi:10.1080/15476286.2019.1655352 · Cited by 35 · see citing works
Self-citationCommon gene expression strategies revealed by genome-wide analysis in yeast
García‐Martínez, J., Gónzález‐Candelas, F., & Pérez-Ortín, J. E. (2007) · Genome biology · doi:10.1186/gb-2007-8-10-r222 · Cited by 33 · see citing works
Self-citationBayesian Testing, Variable Selection and Model Averaging in Linear Models using R with BayesVarSel
García‐Donato, G., & Forte, A. (2018) · The R Journal · doi:10.32614/rj-2018-021 · Cited by 32 · see citing works
Self-citationExtensive Mass Spectrometry-based Analysis of the Fission Yeast Proteome
Gunaratne, J., Schmidt, A., Quandt, A., Neo, S. P., Saraç, Ö. S., Gracia, T., Loguercio, S., Ahrné, E., Xia, R. L. H., Tan, K. H., et al. (2013) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m112.023754 · Cited by 29 · see citing works
Esquerré, T., Moisan, A., Chiapello, H., Arike, L., Vilu, R., Gaspin, C., Cocaign‐Bousquet, M., & Girbal, L. (2015) · BMC Genomics · doi:10.1186/s12864-015-1482-8 · Cited by 29 · see citing works
The Tree of Life Viewed Through the Contents of Genomes
House, C. H. (2009) · Methods in Molecular Biology · doi:10.1007/978-1-60327-853-9_8 · Cited by 18 · see citing works
Cancer Cell Line Panels Empower Genomics-Based Discovery of Precision Cancer Medicine
Kim, H. S., Sung, Y.-J., & Paik, S. (2015) · Yonsei Medical Journal · doi:10.3349/ymj.2015.56.5.1186 · Cited by 17 · see citing works
Eshleman, N. E., Luo, X.-X., Capaldi, A., & Buchan, J. (2019) · RNA: A publication of the RNA Society · doi:10.1261/rna.072892.119 · Cited by 17 · see citing works
What we can learn about Escherichia coli through application of Gene Ontology
Hu, J. C., Karp, P. D., Keseler, I. M., Krummenacker, M., & Siegele, D. A. (2009) · Trends in Microbiology · doi:10.1016/j.tim.2009.04.004 · Cited by 16 · see citing works
Briones, C., Manrubia, S. C., Lázaro, E., Lazcano, A., & Amils, R. (2004) · Molecular Phylogenetics and Evolution · doi:10.1016/j.ympev.2004.10.020 · Cited by 14 · see citing works
Wilhelm, M., Hahne, H., Savitski, M. M., Marx, H., Lemeer, S., Bantscheff, M., & Küster, B. (2017) · Nature · doi:10.1038/nature22294 · Cited by 8 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Faucillion, M.-L., Johansson, A., & Larsson, J. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac208 · Cited by 13 · see citing works
Kamala, K., & Sivaperumal, P. (2023) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2023.115464 · Cited by 7 · see citing works
Zhou, Y., Rashad, S., Ando, D., Kobayashi, Y., Tominaga, T., & Niizuma, K. (2024) · Journal of Cellular Physiology · doi:10.1002/jcp.31477 · Cited by 7 · see citing works
Pérez-Ortín, J. E., Jordán‐Pla, A., Zhang, Y., Moreno‐García, J., Bassot, C., Barba‐Aliaga, M., de Campos‐Mata, L., Choder, M., Díez, J., Piazza, I., et al. (2024) · Yeast · doi:10.1002/yea.3968 · Cited by 4 · see citing works
Self-citationProblems With GO Enrichment as an Endpoint for Ecological Omics Interpretation
Ahi, E. P., Karagic, N., & Papakostas, S. (2026) · Molecular Ecology · doi:10.1111/mec.70570 · Cited by 1 · see citing works
An Overview of 10th Anniversary of Cells—Advances in Cell Nuclei: Function, Transport and Receptors
Miyamoto, H. (2022) · Cells · doi:10.3390/cells12010055 · Cited by 1 · see citing works
- Authors
- Forés-Martos, J., Forte, A., García-Martínez, J., & Pérez-Ortín, J. E.
- Affiliations
- Universitat de València (ES)
- Instituto de Biotecnología y Biomedicina (Biotecmed), Universitat de València, C/Dr. Moliner 50, E46100 Burjassot, Spain
- Corresponding author
- Pérez-Ortín, José E.
- Venue
- Cells
- Publisher
- MDPI AG
- Published
- 2021-02-05
- Volume
- 10
- Issue
- 2
- Pages
- 334
- ISSN
- 2073-4409 (ISSN-L)
- PMC
- PMC7914595
- OpenAlex
- W3128917776
- Cited by
- 8
- References
- 89
- Citations by year
- 2026: 1 · 2024: 2 · 2023: 3 · 2022: 2
- FWCI
- 0.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cells (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Repository of Digital Objects for Teaching Research and Culture (University of Valencia) (submittedVersion, other-oa)
- Funders
- Ministerio de Ciencia, Innovación y Universidades (BFU2016-77728-C3-3-P)
- Generalitat Valenciana (AICO/2019/088)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fungal and yeast genetics research
- Keywords
- gene expression regulation, mRNA levels, protein abundance, mRNA synthesis rate, protein synthesis rate, protein degradation rate, phylogenetic relationships, Saccharomyces cerevisiae, Schizosaccharomyces pombe, HeLa cells, Escherichia coli, transcription rate, translation (biology), macromolecular complexes
- MeSH terms
- Humans, Saccharomyces cerevisiae, Transcription, Genetic, Gene Expression Regulation, Fungal, RNA Stability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review