The tRNA adaptation index
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.

README.md

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

mariodosreis@gmail.com

School of Biological and Chemical Sciences
Queen Mary University of London

INSTALLATION

From the R prompt (assumming you have the devtools package installed) type

> devtools::install_github("mariodosreis/tai")

And that's it! The package has been installed!

QUICK TUTORIAL

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 it in!).

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

The file 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. (> indicates the R prompt). Type:

> require("tAI")

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 calculate tAI:

> 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 sequence in 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 in the 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!

REFERENCES

[1] 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.

[2] 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.