The tRNA adaptation index
tAI is an R package (and two perl scripts) for the analysis of codon usage in DNA coding sequences. It implements the tRNA adaptation index (tAI) described in dos Reis et al. (2003, 2004). The tAI package is distributed WITHOUT WARRANTY under the terms of the GNU General Public License. See the file LICENSE for details.
This package is basically the same I used in my papers on tAI, with just a few minor modifications to make it a little friendlier. If you have any doubts, questions, bug reports, etc. please contact me at:
Mario dos Reis
School of Biological and Chemical Sciences
Queen Mary University of London
From the R prompt (assumming you have the devtools package installed) type
And that's it! The package has been installed!
You are assumed to know how to use a shell or command prompt, as well as
some basic knowledge of R.
$ denotes the shell prompt (so don't type
The tRNA adaptation index (tAI) measures the degree of co-adaptation
between a coding sequence and the tRNA pool of an organism (dos Reis 2003, 2004). File
misc/ecolik12.ffn contains 49 coding sequences extracted from the genome
of Escherichia coli K-12. We can use the files in the package to
calculate tAI for each one of the genes contained in this file. First we
need to calculate the frequencies of the 61 coding codons for every
sequence. Go to the
misc directory in this package and
type (note that perl must be installed in your system and in your path):
$ perl codonM ecolik12.ffn ecolik12.m
ecolik12.m contains the output of the codonM script: a
matrix of codon frequencies per ORF. It should look like:
11 19 10 13 11 10 6 9 ... 6 4 3 6 0 6 1 3 ... 13 11 5 12 0 2 3 5 ... 0 0 2 0 1 1 1 0 ... 8 8 2 6 0 4 2 1 ... ...
Each row represents one ORF or gene (in our case, there should be 49
rows in the file
ecolik12.m), and the columns represent each one of
the 61 coding codons, arranged in this fashion:
1 TTT 2 TTC 3 TTA 4 TTG 5 TCT 6 TCC 7 TCA 8 TCG 9 TAT 10 TAC 11 TGT 12 TGC 13 TGG 14 CTT 15 CTC 16 CTA 17 CTG 18 CCT 19 CCC 20 CCA 21 CCG 22 CAT 23 CAC 24 CAA 25 CAG 26 CGT 27 CGC 28 CGA 29 CGG 30 ATT 31 ATC 32 ATA 33 ATG 34 ACT 35 ACC 36 ACA 37 ACG 38 AAT 39 AAC 40 AAA 41 AAG 42 AGT 43 AGC 44 AGA 45 AGG 46 GTT 47 GTC 48 GTA 49 GTG 50 GCT 51 GCC 52 GCA 53 GCG 54 GAT 55 GAC 56 GAA 57 GAG 58 GGT 59 GGC 60 GGA 61 GGG
Notice that STOP codons have been excluded. codonM, also ignores the first codon in every sequence, this is because it is always a Methionine codon (even if its not coded by the canonical ATG).
Now start R in the same directory you have been working. (
the R prompt). Type:
This will load the necessary functions into R. The file
ecolik12.trna contains the gene copy number of every kind of tRNA in
the E. coli K-12 genome. We need this information in order to
> eco.trna <- scan("ecolik12.trna")
This file contains 64 rows, corresponding to the anticodon complements of each tRNA species (e.g. if the tRNA anticodon is GAA, the complement is TTC). The tRNAs are ordered according to their anticodon complement, in the same order as in codonM's output as indicated above (but with added STOP codon complements). Now we can calculate the relative adaptiveness values for each codon in the E. coli genes:
> eco.ws <- get.ws(tRNA=eco.trna, sking=1)
Now, lets load the output of codonM into R:
> eco.m <- matrix(scan("ecolik12.m"), ncol=61, byrow=TRUE)
We will ignore Methionine codons in our analysis (there is no automatic way to differentiate between 'START' Met-tRNA genes and normal Met-tRNAs in any genome):
> eco.m <- eco.m[,-33]
Now we can finally calculate tAI:
> eco.tai <- get.tai(eco.m, eco.ws) > hist(eco.tai)
The last command will plot an histogram of the tAI values. Highly expressed genes present high tAI values (> 0.4), which means that their codon usage resembles the genomic structure of tRNA genes.
Now a good question arises, how much of the total codon usage of these
genes (in terms of Nc) is due to adaptation to the tRNA gene pool? In
order to answer this question, we need to calculate Nc for every
ecolik12.ffn. You should have codonW (or other codon usage
package able to calculate Nc) installed. If you do have codonW, and it
is in your path, you can use codonZ to quickly and efficiently compute
a set of codon usage statistics. At the shell type:
$ codonZ ecolik12.ffn ecolik12.w
If you don't have codonW installed, file
ecolik12.w is already provided
misc directory. Now from the R prompt:
> df <- read.table("ecolik12.w", header=TRUE)
If you are using the codonW version distributed by me, the above command should work fine, if you are working with the original distribution, you should type instead:
> df <- read.table("ecolik12.w", header=TRUE, na.strings = "*****")
Lets plot the relationship between tAI and Nc:
> plot(eco.tai ~ df$Nc)
As you can see, genes with very low Nc values (highly biased), correlate with high tAI values (highly co-adapted to the tRNA gene pool).
> cor(eco.tai, df$Nc, use="p")
You should get a value of –0.9100338
Formally, we can calculate the correlation between tAI, and the corrected Nc, f(GC3s) – Nc, we call this correlation S, because it reflects the intensity of translational selection acting on our sample of genes:
> eco.s <- get.s(eco.tai, df$Nc, df$GC3s)
You should get a value of 0.9065442
For more details, read the references!
 dos Reis M, Savva R, and Wernisch L. (2003) Unexpected correlations between gene expression and codon usage bias from microarray data for the whole Escherichia coli K-12 genome. Nucleic Acids Research, 31: 6976–6985.
 dos Reis M, Wernisch L, and Savva R. (2004) Solving the riddle of codon usage preferences: a test for translational selection. Nucleic Acids Research, 32: 5036–5044.