A code for transcription elongation speed
Open accessAbstract
The two major steps of gene expression are transcription and translation. While hundreds of studies regarding the effect of sequence features on the translation elongation process have been published, very few connect sequence features to the transcription elongation rate. We suggest, for the first time, that short transcript sub-sequences have a typical effect on RNA polymerase (RNAP) speed: we show that nucleotide 5-mers tend to have typical RNAP speed (or transcription rate), which is consistent along different parts of genes and among different groups of genes with high correlation. We also demonstrate that relative RNAP speed correlates with mRNA levels of endogenous and heterologous genes. Furthermore, we show that the estimated transcription and translation elongation rates correlate in endogenous genes. Finally, we demonstrate that our results are consistent for different high resolution experimental measurements of RNAP densities. These results suggest for the first time that transcription elongation is partly encoded in the transcript, affected by the codon-usage, and optimized by evolution with a significant effect on gene expression and organismal fitness.
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No correlation has been found between the GC content of short DNA sub-sequences and RNA Pol II speed in budding yeast.
“However, here, no correlation has been found between the GC content of these short sequences and RNA Pol II speed (Cohen et al, 2018).”
From RNA polymerase II speed: a key player in controlling and adapting transcriptome composition (2021) · cites this paper as Cohen et al, 2018
The Results (page unavailable) report a very low but statistically significant correlation between 5-mer GC score and RNAP density score (r = 0.097, p < 1.8 × 10⁻³), rather than no correlation. The paper concludes that GC content alone cannot explain the 5-mer differences, which is a narrower finding. (LLM, conf=0.88)
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Experimental — accuracy not yet measuredGenes with the highest measured RNA Pol II velocity generate more mRNAs.
“Indeed, genes with the highest measured RNA Pol II velocity have been shown to generate more mRNAs (Danko et al, 2013; Jonkers et al, 2014; Cohen et al, 2018).”
From RNA polymerase II speed: a key player in controlling and adapting transcriptome composition (2021) · cites this paper as Cohen et al, 2018
The comparative-analysis section (page not provided) reports that genes with higher estimated transcription elongation rates tend to have higher mRNA levels. It does not establish that the highest *measured* RNA Pol II velocities cause genes to generate more mRNAs; the Discussion treats a direct causal effect as one possible explanation for the correlation. (LLM, conf=0.85)
Experimental — accuracy not yet measuredShort DNA sub-sequences influence the frequency and duration of pauses in budding yeast.
“Short DNA sub‐sequences have also been shown to influence the frequency and duration of pauses in budding yeast.”
From RNA polymerase II speed: a key player in controlling and adapting transcriptome composition (2021) · cites this paper as Cohen et al, 2018
The Conclusions (page unavailable) say that short DNA sub-sequences affect transcription elongation, including in the paper’s yeast analysis. But the Discussion says the study estimates typical elongation speed rather than relatively rare pauses; it does not establish an effect on pause frequency or duration. (LLM, conf=0.87)
Experimental — accuracy not yet measured
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Cites
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849 · see citing works
Sharp, P. M., & Li, W.-H. (1987) · Nucleic Acids Research · doi:10.1093/nar/15.3.1281 · Cited by 3,992 · 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 efficient algorithm for large-scale detection of protein families
Enright, A. J. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.7.1575 · Cited by 3,536 · see citing works
GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists
Eden, E., Navon, R., Steinfeld, I., Lipson, D., & Yakhini, Z. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-48 · Cited by 3,356 · see citing works
Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation
Merico, D., Isserlin, R., Stueker, O., Emili, A., & Bader, G. D. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013984 · Cited by 2,235 · see citing works
Nascent RNA Sequencing Reveals Widespread Pausing and Divergent Initiation at Human Promoters
Core, L. J., Waterfall, J. J., & Lis, J. T. (2008) · Science · doi:10.1126/science.1162228 · Cited by 1,986 · see citing works
Ikemura, T. (1981) · Journal of Molecular Biology · doi:10.1016/0022-2836(81)90003-6 · Cited by 1,423 · see citing works
Bidirectional promoters generate pervasive transcription in yeast
Xu, Z., Wei, W., Gagneur, J., Perocchi, F., Clauder‐Münster, S., Camblong, J., Guffanti, E., Stutz, F., Huber, W. K., & Steinmetz, L. M. (2009) · Nature · doi:10.1038/nature07728 · Cited by 926 · see citing works
An evolutionarily conserved mechanism for controlling the efficiency of protein translation
Tuller, T., Carmi, A., Vestsigian, K., Navon, S., Dorfan, Y., Zaborske, J., Pan, T., Dahan, O., Furman, I., & Pilpel, Y. (2010) · Cell · doi:10.1016/j.cell.2010.03.031 · Cited by 874 · see citing works
Self-citationPrecise Maps of RNA Polymerase Reveal How Promoters Direct Initiation and Pausing
Kwak, H., Fuda, N. J., Core, L. J., & Lis, J. T. (2013) · Science · doi:10.1126/science.1229386 · Cited by 849 · see citing works
Coupling mRNA processing with transcription in time and space
Bentley, D. L. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3662 · Cited by 828 · see citing works
Nascent transcript sequencing visualizes transcription at nucleotide resolution
Churchman, L. S., & Weissman, J. S. (2011) · Nature · doi:10.1038/nature09652 · Cited by 823 · see citing works
Codon Bias as a Means to Fine-Tune Gene Expression
Quax, T. E. F., Claassens, N. J., Söll, D. G., & John van der, O. O. S. T. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.05.035 · Cited by 779 · see citing works
Genome-wide measurement of RNA secondary structure in yeast
Kertesz, M., Wan, Y., Mazor, E., Rinn, J. L., Nutter, R. C., Chang, H. Y., & Segal, E. (2010) · Nature · doi:10.1038/nature09322 · Cited by 765 · see citing works
Solving the riddle of codon usage preferences: a test for translational selection
dos Reis, M. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh834 · Cited by 745 · see citing works
Vogel, C., de Sousa Abreu, R., Ko, D., Le, S., Shapiro, B. A., Burns, S. C., Sandhu, D., Boutz, D. R., Marcotte, E. M., & Penalva, L. O. (2010) · Molecular Systems Biology · doi:10.1038/msb.2010.59 · Cited by 608 · see citing works
High-Resolution View of the Yeast Meiotic Program Revealed by Ribosome Profiling
Brar, G. A., Yassour, M., Friedman, N., Regev, A., Ingolia, N. T., & Weissman, J. S. (2012) · Science · doi:10.1126/science.1215110 · Cited by 533 · see citing works
Mahat, D. B., Kwak, H., Booth, G. T., Jonkers, I. H., Danko, C. G., Patel, R. K., Waters, C. T., Munson, K. M., Core, L. J., & Lis, J. T. (2016) · Nature Protocols · doi:10.1038/nprot.2016.086 · Cited by 492 · see citing works
Genome-Scale Proteomics Reveals Arabidopsis thaliana Gene Models and Proteome Dynamics
Baerenfaller, K., Grossmann, J., Grobei, M. A., Hull, R., Hirsch‐Hoffmann, M., Yalovsky, S., Zimmermann, P., Grossniklaus, U., Gruissem, W., & Baginsky, S. (2008) · Science · doi:10.1126/science.1157956 · Cited by 483 · see citing works
A map of nucleosome positions in yeast at base-pair resolution
Brogaard, K., Xi, L., Wang, J.-P., & Widom, J. (2012) · Nature · doi:10.1038/nature11142 · Cited by 452 · see citing works
Regulation of Alternative Splicing Through Coupling with Transcription and Chromatin Structure
Naftelberg, S., Schor, I. E., Ast, G., & Kornblihtt, A. R. (2015) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-060614-034242 · Cited by 437 · see citing works
Nucleosomal Fluctuations Govern the Transcription Dynamics of RNA Polymerase II
Hodges, H. C., Bintu, L., Lubkowska, L., Kashlev, M., & Bustamante, C. (2009) · Science · doi:10.1126/science.1172926 · Cited by 368 · see citing works
Mathews, D. H. (2004) · RNA · doi:10.1261/rna.7650904 · Cited by 365 · see citing works
Synonymous Codon Usage in Escherichia coli: Selection for Translational Accuracy
Stoletzki, N., & Eyre‐Walker, A. (2006) · Molecular Biology and Evolution · doi:10.1093/molbev/msl166 · Cited by 340 · see citing works
Backtracking determines the force sensitivity of RNAP II in a factor-dependent manner
Galburt, E. A., Grill, S. W., Wiedmann, A., Lubkowska, L., Choy, J., Nogales, E., Kashlev, M., & Bustamante, C. (2007) · Nature · doi:10.1038/nature05701 · Cited by 284 · see citing works
Exonic Transcription Factor Binding Directs Codon Choice and Affects Protein Evolution
Stergachis, A. B., Haugen, E., Shafer, A., Fu, W., Vernot, B., Reynolds, A. P., Raubitschek, A., Ziegler, S. F., LeProust, E. M., Akey, J. M., et al. (2013) · Science · doi:10.1126/science.1243490 · Cited by 280 · see citing works
The effect of tRNA levels on decoding times of mRNA codons
Dana, A., & Tuller, T. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku646 · Cited by 257 · see citing works
Self-citationMeasurement of average decoding rates of the 61 sense codons in vivo
Gardin, J., Yeasmin, R., Yurovsky, A., Cai, Y., Skiena, S., & Futcher, B. (2014) · eLife · doi:10.7554/elife.03735 · Cited by 208 · see citing works
Multiple roles of the coding sequence 5′ end in gene expression regulation
Tuller, T., & Zur, H. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1313 · Cited by 206 · see citing works
Self-citationPosttranscriptional expression regulation: what determines translation rates?
Brockmann, R., Beyer, A., Heinisch, J. J., & Wilhelm, T. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030057 · Cited by 201 · see citing works
Composite effects of gene determinants on the translation speed and density of ribosomes
Tuller, T., Veksler‐Lublinsky, I., Gazit, N., Kupiec, M., Ruppin, E., & Ziv-Ukelson, M. (2011) · Genome biology · doi:10.1186/gb-2011-12-11-r110 · Cited by 191 · see citing works
Self-citationWobble base-pairing slows in vivo translation elongation in metazoans
Stadler, M. R., & Fire, A. Z. (2011) · RNA · doi:10.1261/rna.02890211 · Cited by 188 · see citing works
Patterns of intron sequence evolution in Drosophila are dependent upon length and GC content
Haddrill, P. R., Charlesworth, B., Halligan, D. L., & Andolfatto, P. (2005) · Genome biology · doi:10.1186/gb-2005-6-8-r67 · Cited by 169 · see citing works
How the Sequence of a Gene Can Tune Its Translation
Fredrick, K., & Ibba, M. (2010) · Cell · doi:10.1016/j.cell.2010.03.033 · Cited by 156 · see citing works
Maximizing Transcription Efficiency Causes Codon Usage Bias
Xia, X. (1996) · Genetics · doi:10.1093/genetics/144.3.1309 · Cited by 132 · see citing works
Dissecting eukaryotic translation and its control by ribosome density mapping
Arava, Y. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki331 · Cited by 129 · see citing works
A model for competition for ribosomes in the cell
Raveh, A., Michael Margaliot, A., Sontag, E. D., & Tuller, T. (2016) · Journal of The Royal Society Interface · doi:10.1098/rsif.2015.1062 · Cited by 125 · see citing works
Self-citationGenes adopt non‐optimal codon usage to generate cell cycle‐dependent oscillations in protein levels
Frenkel‐Morgenstern, M., Danon, T., Christian, T., Igarashi, T., Cohen, L., Hou, Y., & Jensen, L. J. (2012) · Molecular Systems Biology · doi:10.1038/msb.2012.3 · Cited by 124 · see citing works
Nascent RNA structure modulates the transcriptional dynamics of RNA polymerases
Zamft, B., Bintu, L., Ishibashi, T., & Bustamante, C. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1205063109 · Cited by 117 · see citing works
Booth, G. T., Wang, I. X., Cheung, V. G., & Lis, J. T. (2016) · Genome Research · doi:10.1101/gr.204578.116 · Cited by 92 · see citing works
Native Elongating Transcript Sequencing (NET‐seq)
Churchman, L. S., & Weissman, J. S. (2012) · Current Protocols in Molecular Biology · doi:10.1002/0471142727.mb0414s98 · Cited by 88 · see citing works
Molecular Mechanisms of Transcription through Single-Molecule Experiments
Dangkulwanich, M., Ishibashi, T., Bintu, L., & Bustamante, C. (2014) · Chemical Reviews · doi:10.1021/cr400730x · Cited by 81 · see citing works
Tripathi, T., & Chowdhury, D. (2008) · Physical Review E · doi:10.1103/physreve.77.011921 · Cited by 80 · see citing works
Selection on codon bias in yeast: a transcriptional hypothesis
Trotta, E. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt740 · Cited by 76 · see citing works
Pathway analysis of dilated cardiomyopathy using global proteomic profiling and enrichment maps
Isserlin, R., Merico, D., Alikhani‐Koupaei, R., Gramolini, A., Bader, G. D., & Emili, A. (2010) · PROTEOMICS · doi:10.1002/pmic.200900412 · Cited by 63 · see citing works
Properties of untranslated regions of the S. cerevisiae genome
Tuller, T., Ruppin, E., & Kupiec, M. (2009) · BMC Genomics · doi:10.1186/1471-2164-10-391 · Cited by 62 · see citing works
Self-citationvan Dijk, D., Dhar, R., Missarova, A. M., Espinar, L., Blevins, W. R., Lehner, B., & Carey, L. B. (2015) · Nature Communications · doi:10.1038/ncomms8972 · Cited by 48 · see citing works
Rationally designed, heterologousS. cerevisiaetranscripts expose novel expression determinants
Ben-Yehezkel, T., Atar, S., Zur, H., Diament, A., Goz, E., Marx, T., Cohen, R., Dana, A., Feldman, A., Shapiro, E. Y., et al. (2015) · RNA Biology · doi:10.1080/15476286.2015.1071762 · Cited by 47 · see citing works
Self-citationThe relationship among gene expression, folding free energy and codon usage bias in Escherichia coli
Jia, M., & Li, Y. (2005) · FEBS Letters · doi:10.1016/j.febslet.2005.08.059 · Cited by 43 · see citing works
Accurate, Model-Based Tuning of Synthetic Gene Expression Using Introns in S. cerevisiae
Yofe, I., Zafrir, Z., Blau, R., Schuldiner, M., Tuller, T., Shapiro, E. Y., & Ben-Yehezkel, T. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004407 · Cited by 40 · see citing works
Self-citationJordán‐Pla, A., Gupta, I., de Miguel-Jiménez, L., Steinmetz, L. M., Chávez, S., Pelechano, V., & Pérez-Ortín, J. E. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1349 · Cited by 35 · see citing works
Widespread signatures of local mRNA folding structure selection in four Dengue virus serotypes
Goz, E., & Tuller, T. (2015) · BMC Genomics · doi:10.1186/1471-2164-16-s10-s4 · Cited by 33 · see citing works
Self-citationTrotta, E. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021590 · Cited by 24 · see citing works
The RNA Polymerase Flow Model of Gene Transcription
Edri, S., Gazit, E., Cohen, E., & Tuller, T. (2014) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2013.2290063 · Cited by 22 · see citing works
Self-citationSPARCS: a web server to analyze (un)structured regions in coding RNA sequences
Zhang, Y., Ponty, Y., Blanchette, M., Lécuyer, É., & Waldispühl, J. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt461 · Cited by 17 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Alberts B, Johnson A, Lewis J, Morgan D, Raff M, Roberts K, Walter P. Molecular biology of the cell. (6th Edition). New York (NY) and Abingdon: Garland Science; 2015.
Cited by
Sorted by citations
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“However, here, no correlation has been found between the GC content of these short sequences and RNA Pol II speed (Cohen et al , 2018).”
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Elitzur, S. B., & Tuller, T. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.04.042 · Cited by 108 · see citing works
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Oelschlaeger, P. (2024) · Biomolecules · doi:10.3390/biom14010132 · Cited by 48 · see citing works
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Lai, R. W., Lu, R. J., Danthi, P. S., Bravo, J. I., Goumba, A., Sampathkumar, N. K., & Benayoun, B. A. (2019) · BMB Reports · doi:10.5483/bmbrep.2019.52.1.296 · Cited by 42 · see citing works
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Bergman, S., & Tuller, T. (2020) · Physical Biology · doi:10.1088/1478-3975/ab7083 · Cited by 37 · see citing works
Self-citationSlobodin, B., & Dikstein, R. (2020) · EMBO Reports · doi:10.15252/embr.202050799 · Cited by 30 · see citing works
The impact of biological sex on alternative splicingPreprint
Karlebach, G., Veiga, D. F. T., Mays, A. D., Chatzipantsiou, C., Barja, P. P., Chatzou, M., Kesarwani, A. K., Daniš, D., Kararigas, G., Zhang, X., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/490904 · Cited by 28 · see citing works
Mioduser, O., Goz, E., & Tuller, T. (2017) · BMC Genomics · doi:10.1186/s12864-017-4248-7 · Cited by 24 · see citing works
Self-citationNetworks of ribosome flow models for modeling and analyzing intracellular traffic
Nanikashvili, I., Zarai, Y., Ovseevich, A. I., Tuller, T., & Michael Margaliot, A. (2019) · Scientific Reports · doi:10.1038/s41598-018-37864-1 · Cited by 23 · see citing works
Self-citationEstimating the predictive power of silent mutations on cancer classification and prognosis
Gutman, T., Goren, G., Efroni, O., & Tuller, T. (2021) · npj Genomic Medicine · doi:10.1038/s41525-021-00229-1 · Cited by 20 · see citing works
Self-citationWeiner, I. N., Feldman, Y., Shahar, N., Yacoby, I., & Tuller, T. (2020) · Algal Research · doi:10.1016/j.algal.2019.101788 · Cited by 18 · see citing works
Self-citationLiu, K., Ouyang, Y., Lin, R. X., Ge, C., & Zhou, M. (2021) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2021.11.001 · Cited by 18 · see citing works
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Ding, L., Wang, M., Sun, D., & Li, A. (2017) · Molecular BioSystems · doi:10.1039/c7mb00485k · Cited by 13 · see citing works
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Peterman, E. L., Ploessl, D. S., Love, K. S., Sanabria, V., Daniels, R. F., Johnstone, C. P., Godavarti, D. R., Kabaria, S. R., Oakes, C. G., Pai, A. A., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf528 · Cited by 12 · see citing works
Shahar, N., Weiner, I. N., Stotsky‐Oterin, L., Tuller, T., & Yacoby, I. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz151 · Cited by 11 · see citing works
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Villada, J. C., Duran, M. F., & Lee, P. K. H. (2020) · mSystems · doi:10.1128/msystems.00613-20 · Cited by 9 · see citing works
Modeling and Analyzing the Flow of Molecular Machines in Gene Expression
Zarai, Y., Michael Margaliot, A., & Tuller, T. (2018) · RNA technologies · doi:10.1007/978-3-319-92967-5_14 · Cited by 6 · see citing works
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Harmon, L. M., Miller, J. B., Whiting, M. F., Kauwe, J. S. K., & Ridge, P. G. (2021) · Scientific Reports · doi:10.1038/s41598-020-78803-3 · Cited by 5 · see citing works
Liu, L., Zhao, Y., Hassett, R., Toneyan, S., Koo, P. K., & Siepel, A. C. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf092 · Cited by 3 · see citing works
DNA-sequence and epigenomic determinants of local rates of transcription elongationPreprint
Liu, L., Zhao, Y., & Siepel, A. C. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.21.572932 · Cited by 2 · see citing works
Wienecke, A., Barry, M. L., & Pollard, D. A. (2023) · Genetics · doi:10.1093/genetics/iyad113 · Cited by 1 · see citing works
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Galili, M., & Tuller, T. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3479-9 · Cited by 1 · see citing works
Self-citationGedeon, T., Davis, L., Weber, K., & Thorenson, J. (2021) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720021500207
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Abrhámová, K., Groušlová, M., Valentová, A., Hao, X., Liu, B., Převorovský, M., Gahura, O., Půta, F., Sunnerhagen, P., & Folk, P. (2024) · RNA Biology · doi:10.1080/15476286.2024.2348896
- Authors
- Cohen, E., Zafrir, Z., & Tuller, T.
- Affiliations
- Tel Aviv University (IL)
- Corresponding author
- Tuller, Tamir
- Venue
- RNA Biology
- Publisher
- Informa UK Limited
- Published
- 2017-11-22
- Volume
- 15
- Issue
- 1
- Pages
- 81-94
- ISSN
- 1547-6286 (ISSN-L)
- 1555-8584
- PMC
- PMC5785986
- OpenAlex
- W2769217290
- Cited by
- 29
- References
- 56
- Citations by year
- 2025: 2 · 2024: 4 · 2023: 2 · 2021: 7 · 2020: 6 · 2019: 4 · 2018: 2 · 2017: 2
- FWCI
- 1.56
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- RNA Biology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Edmond j. Safra center for bioinfomatics at Tel Aviv University (N/A)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Keywords
- codon usage, mRNA levels, translation elongation rate, gene expression, gene expression evolution
- MeSH terms
- Codon, Escherichia coli, Peptide Chain Elongation, Translational, Ribosomes, DNA-Directed RNA Polymerases, RNA, Messenger, Transcription, Genetic, Genetic Fitness
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