est_trna_weight compute the tRNA weight per codon for TAI calculation.
This weight reflects relative tRNA availability for each codon.
Usage
est_trna_weight(
trna_level,
codon_table = get_codon_table(),
domain = "Eukarya",
s = NULL
)Arguments
- trna_level,
named vector of tRNA level (or gene copy numbers), one value for each anticodon. vector names are anticodons.
- codon_table
a table of genetic code derived from
get_codon_tableorcreate_codon_table.- domain
The taxonomic domain of interest. "Eukarya" (default), "Bacteria" or "Archaea". Specify either the parameter "domain" or "s".
- s
list of non-Waston-Crick pairing panelty. Specify either the parameter "domain" or "s".
References
dos Reis M, Savva R, Wernisch L. 2004. Solving the riddle of codon usage preferences: a test for translational selection. Nucleic Acids Res 32:5036-5044.
Sabi R, Tuller T. 2014. Modelling the efficiency of codon-tRNA interactions based on codon usage bias. DNA Res 21:511-526.
Examples
# estimate codon tRNA weight for yeast
yeast_trna_w <- est_trna_weight(yeast_trna_gcn)
print(yeast_trna_w)
#> aa_code amino_acid codon subfam anticodon trna_id ac_level W
#> <char> <char> <char> <char> <char> <char> <table> <num>
#> 1: F Phe TTT Phe_TT AAA Phe-AAA 0 2.1390
#> 2: F Phe TTC Phe_TT GAA Phe-GAA 10 10.0000
#> 3: L Leu TTA Leu_TT TAA Leu-TAA 7 7.0000
#> 4: L Leu TTG Leu_TT CAA Leu-CAA 10 12.5935
#> 5: S Ser TCT Ser_TC AGA Ser-AGA 11 11.0000
#> 6: S Ser TCC Ser_TC GGA Ser-GGA 0 5.8751
#> 7: S Ser TCA Ser_TC TGA Ser-TGA 3 4.0175
#> 8: S Ser TCG Ser_TC CGA Ser-CGA 1 2.1115
#> 9: Y Tyr TAT Tyr_TA ATA Tyr-ATA 0 1.7112
#> 10: Y Tyr TAC Tyr_TA GTA Tyr-GTA 8 8.0000
#> 11: C Cys TGT Cys_TG ACA Cys-ACA 0 0.8556
#> 12: C Cys TGC Cys_TG GCA Cys-GCA 4 4.0000
#> 13: W Trp TGG Trp_TG CCA Trp-CCA 6 6.0000
#> 14: L Leu CTT Leu_CT AAG Leu-AAG 0 0.2139
#> 15: L Leu CTC Leu_CT GAG Leu-GAG 1 1.0000
#> 16: L Leu CTA Leu_CT TAG Leu-TAG 3 3.0000
#> 17: L Leu CTG Leu_CT CAG Leu-CAG 0 1.1115
#> 18: P Pro CCT Pro_CC AGG Pro-AGG 2 2.0000
#> 19: P Pro CCC Pro_CC GGG Pro-GGG 0 1.0682
#> 20: P Pro CCA Pro_CC TGG Pro-TGG 10 10.1850
#> 21: P Pro CCG Pro_CC CGG Pro-CGG 0 3.7050
#> 22: H His CAT His_CA ATG His-ATG 0 1.4973
#> 23: H His CAC His_CA GTG His-GTG 7 7.0000
#> 24: Q Gln CAA Gln_CA TTG Gln-TTG 9 9.0000
#> 25: Q Gln CAG Gln_CA CTG Gln-CTG 1 4.3345
#> 26: R Arg CGT Arg_CG ACG Arg-ACG 6 6.0000
#> 27: R Arg CGC Arg_CG GCG Arg-GCG 0 3.2046
#> 28: R Arg CGA Arg_CG TCG Arg-TCG 0 0.5550
#> 29: R Arg CGG Arg_CG CCG Arg-CCG 1 1.0000
#> 30: I Ile ATT Ile_AT AAT Ile-AAT 13 13.0000
#> 31: I Ile ATC Ile_AT GAT Ile-GAT 0 6.9433
#> 32: I Ile ATA Ile_AT TAT Ile-TAT 2 3.2025
#> 33: M Met ATG Met_AT CAT Met-CAT 5 5.0000
#> 34: T Thr ACT Thr_AC AGT Thr-AGT 11 11.0000
#> 35: T Thr ACC Thr_AC GGT Thr-GGT 0 5.8751
#> 36: T Thr ACA Thr_AC TGT Thr-TGT 4 5.0175
#> 37: T Thr ACG Thr_AC CGT Thr-CGT 1 2.4820
#> 38: N Asn AAT Asn_AA ATT Asn-ATT 0 2.1390
#> 39: N Asn AAC Asn_AA GTT Asn-GTT 10 10.0000
#> 40: K Lys AAA Lys_AA TTT Lys-TTT 7 7.0000
#> 41: K Lys AAG Lys_AA CTT Lys-CTT 14 16.5935
#> 42: S Ser AGT Ser_AG ACT Ser-ACT 0 0.4278
#> 43: S Ser AGC Ser_AG GCT Ser-GCT 2 2.0000
#> 44: R Arg AGA Arg_AG TCT Arg-TCT 11 11.0000
#> 45: R Arg AGG Arg_AG CCT Arg-CCT 1 5.0755
#> 46: V Val GTT Val_GT AAC Val-AAC 14 14.0000
#> 47: V Val GTC Val_GT GAC Val-GAC 0 7.4774
#> 48: V Val GTA Val_GT TAC Val-TAC 2 3.2950
#> 49: V Val GTG Val_GT CAC Val-CAC 2 2.7410
#> 50: A Ala GCT Ala_GC AGC Ala-AGC 11 11.0000
#> 51: A Ala GCC Ala_GC GGC Ala-GGC 0 5.8751
#> 52: A Ala GCA Ala_GC TGC Ala-TGC 5 6.0175
#> 53: A Ala GCG Ala_GC CGC Ala-CGC 0 1.8525
#> 54: D Asp GAT Asp_GA ATC Asp-ATC 0 3.4224
#> 55: D Asp GAC Asp_GA GTC Asp-GTC 16 16.0000
#> 56: E Glu GAA Glu_GA TTC Glu-TTC 14 14.0000
#> 57: E Glu GAG Glu_GA CTC Glu-CTC 2 7.1870
#> 58: G Gly GGT Gly_GG ACC Gly-ACC 0 3.4224
#> 59: G Gly GGC Gly_GG GCC Gly-GCC 16 16.0000
#> 60: G Gly GGA Gly_GG TCC Gly-TCC 3 3.0000
#> 61: G Gly GGG Gly_GG CCC Gly-CCC 2 3.1115
#> aa_code amino_acid codon subfam anticodon trna_id ac_level W
#> w
#> <num>
#> 1: 0.12890590
#> 2: 0.60264561
#> 3: 0.42185193
#> 4: 0.75894175
#> 5: 0.66291018
#> 6: 0.35406032
#> 7: 0.24211288
#> 8: 0.12724862
#> 9: 0.10312472
#> 10: 0.48211649
#> 11: 0.05156236
#> 12: 0.24105825
#> 13: 0.36158737
#> 14: 0.01289059
#> 15: 0.06026456
#> 16: 0.18079368
#> 17: 0.06698406
#> 18: 0.12052912
#> 19: 0.06437460
#> 20: 0.61379456
#> 21: 0.22328020
#> 22: 0.09023413
#> 23: 0.42185193
#> 24: 0.54238105
#> 25: 0.26121674
#> 26: 0.36158737
#> 27: 0.19312381
#> 28: 0.03344683
#> 29: 0.06026456
#> 30: 0.78343930
#> 31: 0.41843493
#> 32: 0.19299726
#> 33: 0.30132281
#> 34: 0.66291018
#> 35: 0.35406032
#> 36: 0.30237744
#> 37: 0.14957664
#> 38: 0.12890590
#> 39: 0.60264561
#> 40: 0.42185193
#> 41: 1.00000000
#> 42: 0.02578118
#> 43: 0.12052912
#> 44: 0.66291018
#> 45: 0.30587278
#> 46: 0.84370386
#> 47: 0.45062223
#> 48: 0.19857173
#> 49: 0.16518516
#> 50: 0.66291018
#> 51: 0.35406032
#> 52: 0.36264200
#> 53: 0.11164010
#> 54: 0.20624944
#> 55: 0.96423298
#> 56: 0.84370386
#> 57: 0.43312140
#> 58: 0.20624944
#> 59: 0.96423298
#> 60: 0.18079368
#> 61: 0.18751318
#> w