Genomics & Informatics

Korea Genome Organization (한국유전체학회)
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STADIUM: Species-Specific tRNA Adaptive Index Compendium

  • Yoon, Jonghwan (Department of Biomedicine & Health Sciences, Graduate School, The Catholic University of Korea) ;
  • Chung, Yeun-Jun (Department of Microbiology, College of Medicine, The Catholic University of Korea) ;
  • Lee, Minho (Catholic Precision Medicine Research Center, College of Medicine, The Catholic University of Korea)
  • Received : 2018.10.29
  • Accepted : 2018.11.05
  • Published : 2018.12.31
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Abstract

Due to the increasing interest in synonymous codons, several codon bias-related terms were introduced. As one measure of them, the tRNA adaptation index (tAI) was invented about a decade ago. The tAI is a measure of translational efficiency for a gene and is calculated based on the abundance of intracellular tRNA and the binding strength between a codon and a tRNA. The index has been widely used in various fields of molecular evolution, genetics, and pharmacology. Afterwards, an improved version of the index, named specific tRNA adaptation index (stAI), was developed by adapting tRNA copy numbers in species. Although a subsequently developed webserver (stAIcalc) provided tools that calculated stAI values, it was not available to access pre-calculated values. In addition to about 100 species in stAIcalc, we calculated stAI values for whole coding sequences in 148 species. To enable easy access to this index, we constructed a novel web database, named STADIUM (Species-specific tRNA adaptive index compendium). STADIUM provides not only the stAI value of each gene but also statistics based on pathway-based classification. The database is expected to help researchers who have interests in codon optimality and the role of synonymous codons. STADIUM is freely available at http://stadium.pmrc.re.kr.

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References

  1. Kimchi-Sarfaty C, Oh JM, Kim IW, Sauna ZE, Calcagno AM, Ambudkar SV, et al. A “silent” polymorphism in the MDR1 gene changes substrate specificity. Science 2007;315:525-528. https://doi.org/10.1126/science.1135308
  2. Hunt RC, Simhadri VL, Iandoli M, Sauna ZE, Kimchi-Sarfaty C. Exposing synonymous mutations. Trends Genet 2014;30:308-321. https://doi.org/10.1016/j.tig.2014.04.006
  3. Sauna ZE, Kimchi-Sarfaty C. Understanding the contribution of synonymous mutations to human disease. Nat Rev Genet 2011;12:683-691.
  4. Im EH, Choi SS. Synonymous codon usage controls various molecular aspects. Genomics Inform 2017;15:123-127. https://doi.org/10.5808/GI.2017.15.4.123
  5. Nackley AG, Shabalina SA, Tchivileva IE, Satterfield K, Korchynskyi O, Makarov SS, et al. Human catechol-O-methyltransferase haplotypes modulate protein expression by altering mRNA secondary structure. Science 2006;314:1930-1933. https://doi.org/10.1126/science.1131262
  6. Czech A, Fedyunin I, Zhang G, Ignatova Z. Silent mutations in sight: co-variations in tRNA abundance as a key to unravel consequences of silent mutations. Mol Biosyst 2010;6:1767-1772. https://doi.org/10.1039/c004796c
  7. Chamary JV, Parmley JL, Hurst LD. Hearing silence: non-neutral evolution at synonymous sites in mammals. Nat Rev Genet 2006;7:98-108. https://doi.org/10.1038/nrg1770
  8. Wang Y, Qiu C, Cui Q. A large-scale analysis of the relationship of synonymous SNPs changing microRNA regulation with functionality and disease. Int J Mol Sci 2015;16:23545-23555. https://doi.org/10.3390/ijms161023545
  9. Novoa EM, Ribas de Pouplana L. Speeding with control: codon usage, tRNAs, and ribosomes. Trends Genet 2012;28:574-581. https://doi.org/10.1016/j.tig.2012.07.006
  10. Drummond DA, Wilke CO. Mistranslation-induced protein misfolding as a dominant constraint on coding-sequence evolution. Cell 2008;134:341-352. https://doi.org/10.1016/j.cell.2008.05.042
  11. dos Reis M, Savva R, Wernisch L. Solving the riddle of codon usage preferences: a test for translational selection. Nucleic Acids Res 2004;32:5036-5044. https://doi.org/10.1093/nar/gkh834
  12. Sharp PM, Li WH. The codon Adaptation Index: a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res 1987;15:1281-1295. https://doi.org/10.1093/nar/15.3.1281
  13. Sabi R, Tuller T. Modelling the efficiency of codon-tRNA interactions based on codon usage bias. DNA Res 2014;21:511-526. https://doi.org/10.1093/dnares/dsu017
  14. Ciandrini L, Stansfield I, Romano MC. Ribosome traffic on mRNAs maps to gene ontology: genome-wide quantification of translation initiation rates and polysome size regulation. PLoS Comput Biol 2013;9:e1002866. https://doi.org/10.1371/journal.pcbi.1002866
  15. Gingold H, Pilpel Y. Determinants of translation efficiency and accuracy. Mol Syst Biol 2011;7:481. https://doi.org/10.1038/msb.2011.14
  16. Goodman DB, Church GM, Kosuri S. Causes and effects of N-terminal codon bias in bacterial genes. Science 2013;342:475-479. https://doi.org/10.1126/science.1241934
  17. Sabi R, Volvovitch Daniel R, Tuller T. stAIcalc: tRNA adaptation index calculator based on species-specific weights. Bioinformatics 2017;33:589-591.
  18. Zerbino DR, Achuthan P, Akanni W, Amode M R, Barrell D, Bhai J, et al. Ensembl 2018. Nucleic Acids Res 2018;46:D754-D761. https://doi.org/10.1093/nar/gkx1098
  19. Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank. Nucleic Acids Res 2005;33:D34-D38. https://doi.org/10.1093/nar/gni032
  20. Chan PP, Lowe TM. GtRNAdb 2.0: an expanded database of transfer RNA genes identified in complete and draft genomes. Nucleic Acids Res 2016;44:D184-D189. https://doi.org/10.1093/nar/gkv1309
  21. Federhen S. The NCBI Taxonomy database. Nucleic Acids Res 2012;40:D136-D143. https://doi.org/10.1093/nar/gkr1178
  22. Chen SL, Lee W, Hottes AK, Shapiro L, McAdams HH. Codon usage between genomes is constrained by genome-wide mutational processes. Proc Natl Acad Sci U S A 2004;101:3480-3485. https://doi.org/10.1073/pnas.0307827100
  23. Knight RD, Freeland SJ, Landweber LF. A simple model based on mutation and selection explains trends in codon and amino-acid usage and GC composition within and across genomes. Genome Biol 2001;2:RESEARCH0010.