Comparative Analysis of Codon Usage Bias in Transcriptomes of Eight Species of Formicidae
Abstract
1. Introduction
2. Methods
2.1. Sample Collection and RNA Extraction
2.2. mRNA-Seq Library Construction, Illumina Sequencing, Assembly, and Annotation
2.3. Ortholog Identification and Phylogenetic Analysis
2.4. Index of Codon Usage Bias
2.5. Factors Influencing Codon Usage Bias
2.6. Functional Enrichment
3. Results
3.1. Transcriptome Assembly, Quality Assessment, and Annotation
3.2. Orthogroup Identification and Phylogenetic Analysis
3.3. Nucleotide Composition and Codon Positions in CDSs
3.4. Analysis of Codon Usage Indicator
3.5. PR2 Plot Analysis of Eight Species
3.6. Differential Analysis of Synonymous Codon Usage
3.7. Neutrality Plot Analysis of Eight Species
3.8. GO Enrichment of Ants Occupying Different Desert Niches
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Galtier, N.; Roux, C.; Rousselle, M.; Romiguier, J.; Figuet, E.; Glémin, S.; Bierne, N.; Duret, L. Codon usage bias in animals: Disentangling the effects of natural selection, effective population size, and GC-biased gene conversion. Mol. Biol. Evol. 2018, 35, 1092–1103. [Google Scholar] [CrossRef] [PubMed]
- Bulmer, M. The selection-mutation-drift theory of synonymous codon usage. Genetics 1991, 129, 897–907. [Google Scholar] [CrossRef]
- Duret, L.; Semon, M.; Piganeau, G.; Mouchiroud, D.; Galtier, N. Vanishing GC-rich isochores in mammalian genomes. Genetics 2002, 162, 1837–1847. [Google Scholar] [CrossRef]
- Marais, G. Biased gene conversion: Implications for genome and sex evolution. Trends Genet. 2003, 19, 330–338. [Google Scholar] [CrossRef]
- Sharp, P.M.; Li, W.-H. The rate of synonymous substitution in enterobacterial genes is inversely related to codon usage bias. Mol. Biol. Evol. 1987, 4, 222–230. [Google Scholar] [PubMed]
- Sharp, P.M.; Li, W.-H. An evolutionary perspective on synonymous codon usage in unicellular organisms. J. Mol. Evol. 1986, 24, 28–38. [Google Scholar] [CrossRef] [PubMed]
- Zhao, Y.; Zhang, S. Comparative Analysis of Codon Usage Bias in Six Eimeria Genomes. Int. J. Mol. Sci. 2024, 25, 8398. [Google Scholar] [CrossRef]
- Shi, A.; Li, C.; Farhan, M.; Xu, C.; Zhang, Y.; Qian, H.; Zhang, S.; Jing, T. Characterization, Codon Usage Pattern and Phylogenetic Implications of the Waterlily Aphid Rhopalosiphum nymphaeae (Hemiptera: Aphididae) Mitochondrial Genome. Int. J. Mol. Sci. 2024, 25, 11336. [Google Scholar] [CrossRef]
- Guo, M.; Wang, J.; Li, H.; Yu, K.; Yang, Y.; Li, M.; Smagghe, G.; Dai, R. Mitochondrial genomes of Macropsini (Hemiptera: Cicadellidae: Eurymelinae): Structural features, codon usage patterns, and phylogenetic implications. Ecol. Evol. 2024, 14, e70268. [Google Scholar] [CrossRef]
- Hao, J.; Liang, Y.; Wang, T.; Su, Y. Correlations of gene expression, codon usage bias, and evolutionary rates of the mitochondrial genome show tissue differentiation in Ophioglossum vulgatum. BMC Plant Biol. 2025, 25, 134. [Google Scholar] [CrossRef]
- Kotari, I.; Kosiol, C.; Borges, R. The patterns of codon usage between chordates and arthropods are different but co-evolving with mutational biases. Mol. Biol. Evol. 2024, 41, msae080. [Google Scholar] [CrossRef]
- Wu, X.; Xu, M.; Yang, J.-R.; Lu, J. Genome-wide impact of codon usage bias on translation optimization in Drosophila melanogaster. Nat. Commun. 2024, 15, 8329. [Google Scholar] [CrossRef]
- Zhan, H.; Cao, Q.; Yang, X. Phylogenetic and Codon Usage Bias Analysis Based on mt-DNA of Cyphochilus crataceus (Coleoptera: Melolonthinae) and Its Neighboring Species. Genes 2025, 16, 111. [Google Scholar] [CrossRef]
- Ruden, D.M. GC Content in Nuclear-Encoded Genes and Effective Number of Codons (ENC) Are Positively Correlated in AT-Rich Species and Negatively Correlated in GC-Rich Species. Genes 2025, 16, 432. [Google Scholar] [CrossRef] [PubMed]
- Lei, T.; Zheng, X.; Song, C.; Jin, H.; Chen, L.; Qi, X. Limited Variation in Codon Usage across Mitochondrial Genomes of Non-Biting Midges (Diptera: Chironomidae). Insects 2024, 15, 752. [Google Scholar] [CrossRef] [PubMed]
- Zhao, R.; Li, H.; Wu, G.; Wang, Y.-F. Codon usage bias analysis in the mitochondrial genomes of five Rhingia Scopoli (Diptera, Syrphidae, Eristalinae) species. Gene 2024, 917, 148466. [Google Scholar] [CrossRef] [PubMed]
- Garel, J.-P. Functional adaptation of tRNA population. J. Theor. Biol. 1974, 43, 211–225. [Google Scholar] [CrossRef]
- Duret, L. tRNA gene number and codon usage in the C. elegans genome are co-adapted for optimal translation of highly expressed genes. Trends Genet. 2000, 16, 287–289. [Google Scholar] [CrossRef]
- Presnyak, V.; Alhusaini, N.; Chen, Y.-H.; Martin, S.; Morris, N.; Kline, N.; Olson, S.; Weinberg, D.; Baker, K.E.; Graveley, B.R. Codon optimality is a major determinant of mRNA stability. Cell 2015, 160, 1111–1124. [Google Scholar] [CrossRef]
- Zhao, F.; Yu, C.-h.; Liu, Y. Codon usage regulates protein structure and function by affecting translation elongation speed in Drosophila cells. Nucleic Acids Res. 2017, 45, 8484–8492. [Google Scholar] [CrossRef]
- Liu, Y. A code within the genetic code: Codon usage regulates co-translational protein folding. Cell Commun. Signal. 2020, 18, 145. [Google Scholar] [CrossRef] [PubMed]
- Moriyama, E.N.; Powell, J.R. Codon usage bias and tRNA abundance in Drosophila. J. Mol. Evol. 1997, 45, 514–523. [Google Scholar] [CrossRef] [PubMed]
- Jørgensen, F.G.; Schierup, M.H.; Clark, A.G. Heterogeneity in regional GC content and differential usage of codons and amino acids in GC-poor and GC-rich regions of the genome of Apis mellifera. Mol. Biol. Evol. 2007, 24, 611–619. [Google Scholar] [CrossRef]
- Vicario, S.; Moriyama, E.N.; Powell, J.R. Codon usage in twelve species of Drosophila. BMC Evol. Biol. 2007, 7, 226. [Google Scholar] [CrossRef] [PubMed]
- Behura, S.K.; Severson, D.W. Codon usage bias: Causative factors, quantification methods and genome-wide patterns: With emphasis on insect genomes. Biol. Rev. 2013, 88, 49–61. [Google Scholar] [CrossRef]
- Dennis, A.B.; Ballesteros, G.I.; Robin, S.; Schrader, L.; Bast, J.; Berghöfer, J.; Beukeboom, L.W.; Belghazi, M.; Bretaudeau, A.; Buellesbach, J. Functional insights from the GC-poor genomes of two aphid parasitoids, Aphidius ervi and Lysiphlebus fabarum. BMC Genom. 2020, 21, 376. [Google Scholar] [CrossRef]
- McVEAN, G.A.; Charlesworth, B. A population genetic model for the evolution of synonymous codon usage: Patterns and predictions. Genet. Res. 1999, 74, 145–158. [Google Scholar] [CrossRef]
- Banerjee, R.; Roy, D. Codon usage and gene expression pattern of Stenotrophomonas maltophilia R551-3 for pathogenic mode of living. Biochem. Biophys. Res. Commun. 2009, 390, 177–181. [Google Scholar] [CrossRef]
- Angov, E. Codon usage: Nature’s roadmap to expression and folding of proteins. Biotechnol. J. 2011, 6, 650–659. [Google Scholar] [CrossRef]
- Goodman, D.B.; Church, G.M.; Kosuri, S. Causes and effects of N-terminal codon bias in bacterial genes. Science 2013, 342, 475–479. [Google Scholar] [CrossRef]
- Bolton, B.; Alpert, G.; Ward, P.S.; Naskrecki, P. Bolton’s Catalogue of Ants of the World, 1758–2005; Harvard University Press: Cambridge, UK, 2006. [Google Scholar]
- Gibson, L.; New, T. Characterising insect diversity on Australia’s remnant native grasslands: Ants (Hymenoptera: Formicidae) and beetles (Coleoptera) at Craigieburn Grasslands Reserve, Victoria. J. Insect Conserv. 2007, 11, 409–413. [Google Scholar] [CrossRef]
- Wehner, R.; Hoinville, T.; Cruse, H.; Cheng, K. Steering intermediate courses: Desert ants combine information from various navigational routines. J. Comp. Physiol. 2016, 202, 459–472. [Google Scholar] [CrossRef]
- Nylander, W. Additamentum alterum adnotationum in monographiam formicarum borealium. Acta Soc. Sci. Fenn. 1849, 3, 25–48. [Google Scholar]
- Wheeler, W.M. New Ants from China and Japan. Psyche A J. Entomol. 1933, 40, 65–67. [Google Scholar] [CrossRef]
- Santschi, F. Fourmis du Japon et de Formose. Bull. Ann. Société Entomol. Belg. 1937, 77, 361–388. [Google Scholar]
- Emery, C. Descriptions of New Taxa: Messor barbarus Linn. var. lobulifera Emery n. var.; Formica nasuta Nyl. subspec. mongolica Emery n. subspec. 1901. Available online: https://www.antcat.org/references/124654 (accessed on 7 May 2025).
- Zhu, W.; Wu, L.; Duan, L.; Xu, S. A checklist of ants (Hymenoptera: Formicidae) in northern Shaanxi Province, China, with one new species of genus Proformica Ruzsky, 1902. J. Asia Pac. Entomol. 2022, 25, 101875. [Google Scholar] [CrossRef]
- Wheeler, W.M. Chinese ants collected by Professor S. F. Light and Professor N. Gist Gee. Am. Mus. Novit. 1927, 255, 1–12. [Google Scholar]
- Emery, C. Hymenoptera. Fam. Formicidae. Subfam. Formicinae. Genera Insectorum 1925, 183, 1–302. [Google Scholar]
- Chen, G.; Wang, C.; Shi, T. Overview of available methods for diverse RNA-Seq data analyses. Sci. China Life Sci. 2011, 54, 1121–1128. [Google Scholar] [CrossRef]
- Conesa, A.; Madrigal, P.; Tarazona, S.; Gomez-Cabrero, D.; Cervera, A.; McPherson, A.; Szcześniak, M.W.; Gaffney, D.J.; Elo, L.L.; Zhang, X. A survey of best practices for RNA-seq data analysis. Genome Biol. 2016, 17, 13. [Google Scholar] [CrossRef]
- Hrdlickova, R.; Toloue, M.; Tian, B. RNA-Seq methods for transcriptome analysis. Wiley Interdiscip. Rev. RNA 2017, 8, e1364. [Google Scholar] [CrossRef] [PubMed]
- McGettigan, P.A. Transcriptomics in the RNA-seq era. Curr. Opin. Chem. Biol. 2013, 17, 4–11. [Google Scholar] [CrossRef]
- Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef] [PubMed]
- Haas, B.J.; Papanicolaou, A.; Yassour, M.; Grabherr, M.; Blood, P.D.; Bowden, J.; Couger, M.B.; Eccles, D.; Li, B.; Lieber, M.; et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 2013, 8, 1494–1512. [Google Scholar] [CrossRef]
- Rombel, I.T.; Sykes, K.F.; Rayner, S.; Johnston, S.A. ORF-FINDER: A vector for high-throughput gene identification. Gene 2002, 282, 33–41. [Google Scholar] [CrossRef] [PubMed]
- Simão, F.A.; Waterhouse, R.M.; Ioannidis, P.; Kriventseva, E.V.; Zdobnov, E.M. BUSCO: Assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 2015, 31, 3210–3212. [Google Scholar] [CrossRef]
- Zhiliang, H.; Bao, J.; Reecy, J. CateGOrizer: A web-based program to batch analyze gene ontology classification categories. Online J. Bioinform. 2008, 9, 108–112. [Google Scholar]
- Ogata, H.; Goto, S.; Sato, K.; Fujibuchi, W.; Bono, H.; Kanehisa, M. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 1999, 27, 29–34. [Google Scholar] [CrossRef]
- Huerta-Cepas, J.; Szklarczyk, D.; Forslund, K.; Cook, H.; Heller, D.; Walter, M.C.; Rattei, T.; Mende, D.R.; Sunagawa, S.; Kuhn, M.; et al. eggNOG 4.5: A hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 2016, 44, D286–D293. [Google Scholar] [CrossRef]
- Emms, D.M.; Kelly, S. OrthoFinder: Solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy. Genome Biol. 2015, 16, 157. [Google Scholar] [CrossRef]
- Conway, J.R.; Lex, A.; Gehlenborg, N. UpSetR: An R package for the visualization of intersecting sets and their properties. Bioinformatics 2017, 33, 2938–2940. [Google Scholar] [CrossRef]
- Nguyen, L.T.; Schmidt, H.A.; von Haeseler, A.; Minh, B.Q. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 2015, 32, 268–274. [Google Scholar] [CrossRef] [PubMed]
- Hoang, D.T.; Chernomor, O.; von Haeseler, A.; Minh, B.Q.; Vinh, L.S. UFBoot2: Improving the Ultrafast Bootstrap Approximation. Mol. Biol. Evol. 2017, 35, 518–522. [Google Scholar] [CrossRef]
- Wright, F. The ‘effective number of codons’ used in a gene. Gene 1990, 87, 23–29. [Google Scholar] [CrossRef] [PubMed]
- Carbone, A.; Zinovyev, A.; Képes, F. Codon adaptation index as a measure of dominating codon bias. Bioinformatics 2003, 19, 2005–2015. [Google Scholar] [CrossRef]
- Hui, S.; Jing, L.; Tao, C.; Zhi-biao, N. Synonymous codon usage pattern in model legume Medicago truncatula. J. Integr. Agric. 2018, 17, 2074–2081. [Google Scholar]
- Niu, Y.; Luo, Y.; Wang, C.; Liao, W. Deciphering codon usage patterns in genome of Cucumis sativus in comparison with nine species of Cucurbitaceae. Agronomy 2021, 11, 2289. [Google Scholar] [CrossRef]
- Sharp, P.M.; Li, W.-H. The codon adaptation index-a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res. 1987, 15, 1281–1295. [Google Scholar] [CrossRef]
- Zeng, X.; Yi, Z.; Zhang, X.; Du, Y.; Li, Y.; Zhou, Z.; Chen, S.; Zhao, H.; Yang, S.; Wang, Y. Chromosome-level scaffolding of haplotype-resolved assemblies using Hi-C data without reference genomes. Nat. Plants 2024, 10, 1184–1200. [Google Scholar] [CrossRef]
- Liu, H.; He, R.; Zhang, H.; Huang, Y.; Tian, M.; Zhang, J. Analysis of synonymous codon usage in Zea mays. Mol. Biol. Rep. 2010, 37, 677–684. [Google Scholar] [CrossRef]
- Li, L.; Peng, J.; Wang, D.; Duan, A. Chloroplast genome phylogeny and codon preference of Docynia longiunguis. Sheng Wu Gong Cheng Xue Bao Chin. J. Biotechnol. 2022, 38, 328–342. [Google Scholar]
- Novembre, J.A. Accounting for background nucleotide composition when measuring codon usage bias. Mol. Biol. Evol. 2002, 19, 1390–1394. [Google Scholar] [CrossRef]
- Fuglsang, A. The ‘effective number of codons’ revisited. Biochem. Biophys. Res. Commun. 2004, 317, 957–964. [Google Scholar] [CrossRef] [PubMed]
- Wang, Z.; Cai, Q.; Wang, Y.; Li, M.; Wang, C.; Wang, Z.; Jiao, C.; Xu, C.; Wang, H.; Zhang, Z. Comparative analysis of codon Bias in the chloroplast genomes of Theaceae species. Front. Genet. 2022, 13, 824610. [Google Scholar] [CrossRef] [PubMed]
- Zhang, W.J.; Zhou, J.; Li, Z.F.; Wang, L.; Gu, X.; Zhong, Y. Comparative analysis of codon usage patterns among mitochondrion, chloroplast and nuclear genes in Triticum aestivum L. J. Integr. Plant Biol. 2007, 49, 246–254. [Google Scholar] [CrossRef]
- Theissinger, K.; Falckenhayn, C.; Blande, D.; Toljamo, A.; Gutekunst, J.; Makkonen, J.; Jussila, J.; Lyko, F.; Schrimpf, A.; Schulz, R.; et al. De Novo assembly and annotation of the freshwater crayfish Astacus astacus transcriptome. Mar. Genom. 2016, 28, 7–10. [Google Scholar] [CrossRef]
- Wahl, V.; Pfeffer, S.E.; Wittlinger, M. Walking and running in the desert ant Cataglyphis fortis. J. Comp. physiology. A Neuroethol. Sens. Neural Behav. Physiol. 2015, 201, 645–656. [Google Scholar] [CrossRef]
- Shahzadi, I.; Mehmood, F.; Ali, Z.; Ahmed, I.; Mirza, B. Chloroplast genome sequences of Artemisia maritima and Artemisia absinthium: Comparative analyses, mutational hotspots in genus Artemisia and phylogeny in family Asteraceae. Genomics 2020, 112, 1454–1463. [Google Scholar] [CrossRef]
- Wehner, R.; Marsh, A.C.; Wehner, S. Desert ants on a thermal tightrope. Nature 1992, 357, 586–587. [Google Scholar] [CrossRef]
- Näsvall, K.; Boman, J.; Talla, V.; Backström, N. Base composition, codon usage, and patterns of gene sequence evolution in butterflies. Genome Biol. Evol. 2023, 15, evad150. [Google Scholar] [CrossRef]
- Song, L.; Chen, X.; Li, X.; Guedes, R.N.C.; Dewer, Y.; Shang, S.; Zhou, J. Mitogenomic Phylogenetic Analyses Reveal New Insights into the Taxonomy and Evolution of Parnassiinae Swallowtail Butterflies (Lepidoptera: Papilionidae). Diversity 2024, 17, 19. [Google Scholar] [CrossRef]
- Yan, Z.-T.; Fan, Z.-H.; He, S.-L.; Wang, X.-Q.; Chen, B.; Luo, S.-T. Mitogenomes of eight Nymphalidae butterfly species and reconstructed phylogeny of Nymphalidae (Nymphalidae: Lepidoptera). Genes 2023, 14, 1018. [Google Scholar] [CrossRef] [PubMed]
- Behura, S.K.; Severson, D.W. Comparative analysis of codon usage bias and codon context patterns between dipteran and hymenopteran sequenced genomes. PLoS ONE 2012, 7, e43111. [Google Scholar] [CrossRef]
- Knight, R.D.; Freeland, S.J.; Landweber, L.F. Rewiring the keyboard: Evolvability of the genetic code. Nat. Rev. Genet. 2001, 2, 49–58. [Google Scholar] [CrossRef] [PubMed]
- Palidwor, G.A.; Perkins, T.J.; Xia, X. A general model of codon bias due to GC mutational bias. PLoS ONE 2010, 5, e13431. [Google Scholar] [CrossRef]
- Lamolle, G.; Fontenla, S.; Rijo, G.; Tort, J.F.; Smircich, P. Compositional analysis of flatworm genomes shows strong codon usage biases across all classes. Front. Genet. 2019, 10, 771. [Google Scholar] [CrossRef]
- Pandey, A.; Suman, S.; Chandna, S. Predictive role of mitochondrial genome in the stress resistance of insects and nematodes. Bioinformation 2010, 5, 21–27. [Google Scholar] [CrossRef]
C. aenescens | F. approximans | L. coloratus | P. mongolica | P. muusensis | T. geei | T. rectinotum | T. tsushimae | |
---|---|---|---|---|---|---|---|---|
Row reads (bp) | 6,811,039,804 | 8,326,823,594 | 7,065,179,209 | 7,174,050,294 | 8,181,571,346 | 7,158,103,337 | 7,552,263,822 | 7,217,089,193 |
GC% | 39.94 | 41.08 | 43.14 | 38.95 | 40.22 | 40.56 | 41.45 | 40.66 |
Assembly | ||||||||
Trinity Transcripts (n) | 68,389 | 64,382 | 78,659 | 66,227 | 70,312 | 77,719 | 64,130 | 52,223 |
Total unigenes (n) | 58,219 | 53,172 | 64,536 | 54,846 | 58,231 | 63,900 | 54,818 | 43,008 |
Maximum length (bp) | 35,719 | 29,880 | 33,377 | 28,270 | 29,773 | 27,646 | 24,454 | 18,282 |
Minimum length (bp) | 189 | 183 | 186 | 197 | 189 | 184 | 182 | 201 |
N50 | 4052 | 4450 | 4183 | 4214 | 4531 | 3775 | 3035 | 3430 |
GC% | 38.62 | 39.01 | 40.64 | 37.90 | 38.59 | 39.46 | 39.80 | 41.16 |
BUSCO (%) | ||||||||
Complete | 97.22 | 97.80 | 98.17 | 97.22 | 97.81 | 96.78 | 95.17 | 95.18 |
Fragmented | 1.10 | 0.44 | 0.44 | 1.02 | 0.73 | 1.10 | 2.34 | 2.63 |
Missing | 1.68 | 1.76 | 1.39 | 1.76 | 1.46 | 2.12 | 2.49 | 2.19 |
Species | CBI | FOP | L_aa | GC1% | GC2% | GC3% | GCall% |
---|---|---|---|---|---|---|---|
P. mongolica | −0.038 | 0.398 | 381 | 48.84 | 38.87 | 43.11 | 43.61 |
T. geei | −0.019 | 0.409 | 368 | 49.19 | 38.89 | 45.98 | 44.69 |
C. aenescens | −0.036 | 0.399 | 397 | 49.13 | 39.28 | 43.18 | 43.86 |
L. coloratus | −0.005 | 0.416 | 403 | 49.94 | 39.76 | 48.91 | 46.2 |
T. rectinotum | −0.014 | 0.412 | 361 | 49.4 | 39.01 | 46.34 | 44.91 |
P. muusensis | −0.033 | 0.4 | 398 | 49.03 | 39.14 | 43.8 | 43.99 |
F. approximans | −0.031 | 0.402 | 408 | 49.05 | 39.1 | 44.59 | 44.25 |
T. tsushimae | 0.005 | 0.422 | 364 | 50.09 | 39.58 | 48.46 | 46.04 |
Species | ENC | CAI | ENC < 35 | 35 ≤ ENC ≤ 50 | 50 < ENC |
---|---|---|---|---|---|
P. mongolica | 55.71 | 0.198 | 169 | 11,926 | 20,927 |
T. geei | 56.28 | 0.204 | 266 | 13,152 | 20,288 |
C. aenescens | 55.74 | 0.198 | 156 | 11,235 | 18,543 |
L. coloratus | 56.52 | 0.202 | 161 | 12,124 | 23,836 |
T. rectinotum | 56.31 | 0.205 | 259 | 10,948 | 16,751 |
P. muusensis | 55.93 | 0.198 | 160 | 12,360 | 22,237 |
F. approximans | 56.13 | 0.198 | 148 | 11,518 | 21,741 |
T. tsushimae | 56.74 | 0.208 | 247 | 7308 | 16,795 |
Species Name | [−0.25, −0.15) | [−0.15, −0.05) | [−0.05, 0.05) | [0.05, 0.15) | [0.15, 0.25) | [0.25, 0.35] | |
---|---|---|---|---|---|---|---|
P. mongolica | Frequency | 4352 | 20,241 | 7093 | 368 | 12 | 1 |
Frequencies | 0.14 | 0.63 | 0.22 | 0.01 | 0.00 | 0.00 | |
T. geei | Frequency | 4949 | 20,297 | 6874 | 395 | 14 | 1 |
Frequencies | 0.15 | 0.62 | 0.21 | 0.01 | 0.00 | 0.00 | |
C. aenescens | Frequency | 3967 | 18,379 | 6202 | 312 | 18 | 1 |
Frequencies | 0.14 | 0.64 | 0.21 | 0.01 | 0.00 | 0.00 | |
L. coloratus | Frequency | 4862 | 22,432 | 7246 | 387 | 4 | 2 |
Frequencies | 0.14 | 0.64 | 0.21 | 0.01 | 0.00 | 0.00 | |
T. rectinotum | Frequency | 4112 | 16,792 | 5753 | 301 | 9 | 1 |
Frequencies | 0.15 | 0.62 | 0.21 | 0.01 | 0.00 | 0.00 | |
P. muusensis | Frequency | 4541 | 21,222 | 7519 | 348 | 15 | 0 |
Frequencies | 0.13 | 0.63 | 0.22 | 0.01 | 0.00 | 0.00 | |
F. approximans | Frequency | 4166 | 20,643 | 7115 | 316 | 10 | 0 |
Frequencies | 0.13 | 0.64 | 0.22 | 0.01 | 0.00 | 0.00 | |
T. tsushimae | Frequency | 3312 | 14,355 | 5414 | 261 | 11 | 0 |
Frequencies | 0.14 | 0.61 | 0.23 | 0.01 | 0.00 | 0.00 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zhu, W.; Wang, J.; Wang, J.; Nie, L. Comparative Analysis of Codon Usage Bias in Transcriptomes of Eight Species of Formicidae. Genes 2025, 16, 749. https://doi.org/10.3390/genes16070749
Zhu W, Wang J, Wang J, Nie L. Comparative Analysis of Codon Usage Bias in Transcriptomes of Eight Species of Formicidae. Genes. 2025; 16(7):749. https://doi.org/10.3390/genes16070749
Chicago/Turabian StyleZhu, Wenhui, Jiawei Wang, Jing Wang, and Linlin Nie. 2025. "Comparative Analysis of Codon Usage Bias in Transcriptomes of Eight Species of Formicidae" Genes 16, no. 7: 749. https://doi.org/10.3390/genes16070749
APA StyleZhu, W., Wang, J., Wang, J., & Nie, L. (2025). Comparative Analysis of Codon Usage Bias in Transcriptomes of Eight Species of Formicidae. Genes, 16(7), 749. https://doi.org/10.3390/genes16070749