Browse > Article
http://dx.doi.org/10.5808/GI.2018.16.4.e28

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)
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.
Keywords
codon optimality; KEGG pathway; Shiny; tRNA adaptive index;
Citations & Related Records
연도 인용수 순위
  • Reference
1 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.   DOI
2 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.
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.   DOI
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.   DOI
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.   DOI
7 Drummond DA, Wilke CO. Mistranslation-induced protein misfolding as a dominant constraint on coding-sequence evolution. Cell 2008;134:341-352.   DOI
8 Chamary JV, Parmley JL, Hurst LD. Hearing silence: non-neutral evolution at synonymous sites in mammals. Nat Rev Genet 2006;7:98-108.   DOI
9 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.   DOI
10 Novoa EM, Ribas de Pouplana L. Speeding with control: codon usage, tRNAs, and ribosomes. Trends Genet 2012;28:574-581.   DOI
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.   DOI
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.   DOI
13 Sabi R, Tuller T. Modelling the efficiency of codon-tRNA interactions based on codon usage bias. DNA Res 2014;21:511-526.   DOI
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.   DOI
15 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.   DOI
16 Gingold H, Pilpel Y. Determinants of translation efficiency and accuracy. Mol Syst Biol 2011;7:481.   DOI
17 Goodman DB, Church GM, Kosuri S. Causes and effects of N-terminal codon bias in bacterial genes. Science 2013;342:475-479.   DOI
18 Sabi R, Volvovitch Daniel R, Tuller T. stAIcalc: tRNA adaptation index calculator based on species-specific weights. Bioinformatics 2017;33:589-591.
19 Zerbino DR, Achuthan P, Akanni W, Amode M R, Barrell D, Bhai J, et al. Ensembl 2018. Nucleic Acids Res 2018;46:D754-D761.   DOI
20 Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank. Nucleic Acids Res 2005;33:D34-D38.   DOI
21 Federhen S. The NCBI Taxonomy database. Nucleic Acids Res 2012;40:D136-D143.   DOI
22 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.   DOI
23 Hunt RC, Simhadri VL, Iandoli M, Sauna ZE, Kimchi-Sarfaty C. Exposing synonymous mutations. Trends Genet 2014;30:308-321.   DOI