codcmp |
codcmp reads two codon usage table files and writes to file the differences in codon usage fractions between the two tables.
The usage fraction of a codon is its proportion (0 to 1) of the total number of the codons in the sequences used to construct the usage table. For each codon that is used in both tables, it takes the difference between the usage fractions in the two tables. The sum of the differences and the sum of the differences squared is reported in the output file. It also counts the number of the 64 possible codons which are unused (i.e. has a usage fraction of 0) in either one or the other or both of the codon usage tables, and writes this to the output file.
How do you interpret the statistical significance of any difference between the tables?
Answer:
This is a very interesting question. I don't think that there is any way to say if it is statistically significant just from looking at it, as it is essentially a descriptive statistic about the difference between two 64-mer vectors. If you have a whole lot of sequences and codcmp results for all the possible pairwise comparisons, then the resulting distance matrix can be used to build a phylogenetic tree based on codon usage.
However, if you generate a series of random sequences, measure their codon usage and then do codcmp between each of your test sequences and all the random sequences, you could then use a z-test to see if the result between the two test sequences was outside of the top or bottom 5%.
This would assume that the codcmp results were normally distributed, but you could test that too. The simplest way is just to plot them and look for a bell-curve. For more rigour, find the mean and standard deviation of your results from the random sequences, use the normal distribution equation to generate a theoretical distribution for that mean and standard deviation, and then perform a chi square between the random data and the theoretically generated normal distribution. If you generate two sets of random data, each based on your two test sequences, an F-test should be used to establish that they have equal variances. Then you can safely go ahead and perform the z-test.
You could use shuffle to base your random sequences on the test sequences - so that would ensure the randomised background had the same nucleotide content.
F-tests, z-tests and chi-tests can all be done in Excel, as well as being standard in most statistical analysis packages.
Answered by Derek Gatherer <d.gatherer © vir.gla.ac.uk> 21 Nov 2003
This compares the codon usage tables for Escherichia coli and Haemophilus influenzae.
% codcmp Codon usage table comparison Codon usage file: Eecoli.cut Second Codon usage file: Ehaein.cut Output file [eecoli.codcmp]: |
Go to the output files for this example
Standard (Mandatory) qualifiers: [-first] codon First codon usage file [-second] codon Second codon usage file for comparison [-outfile] outfile [*.codcmp] Output file name Additional (Optional) qualifiers: (none) Advanced (Unprompted) qualifiers: (none) Associated qualifiers: "-first" associated qualifiers -format1 string Data format "-second" associated qualifiers -format2 string Data format "-outfile" associated qualifiers -odirectory3 string Output directory General qualifiers: -auto boolean Turn off prompts -stdout boolean Write first file to standard output -filter boolean Read first file from standard input, write first file to standard output -options boolean Prompt for standard and additional values -debug boolean Write debug output to program.dbg -verbose boolean Report some/full command line options -help boolean Report command line options. More information on associated and general qualifiers can be found with -help -verbose -warning boolean Report warnings -error boolean Report errors -fatal boolean Report fatal errors -die boolean Report dying program messages |
Standard (Mandatory) qualifiers | Allowed values | Default | |
---|---|---|---|
[-first] (Parameter 1) |
First codon usage file | Codon usage file in EMBOSS data path | |
[-second] (Parameter 2) |
Second codon usage file for comparison | Codon usage file in EMBOSS data path | |
[-outfile] (Parameter 3) |
Output file name | Output file | <*>.codcmp |
Additional (Optional) qualifiers | Allowed values | Default | |
(none) | |||
Advanced (Unprompted) qualifiers | Allowed values | Default | |
(none) |
# CODCMP codon usage table comparison # Eecoli.cut vs Ehaein.cut Sum Squared Difference = 2.178 Mean Squared Difference = 0.034 Root Mean Squared Difference = 0.184 Sum Difference = 9.504 Mean Difference = 0.148 Codons not appearing = 0 |
codcmp requires two codon usage tables which are read by default from the EMBOSS data file from Ehum.cut in the data/CODONS directory of the EMBOSS distribution. If the name of a codon usage file is specified on the command line, then this file will first be searched for in the current directory and then in the data/CODONS directory of the EMBOSS distribution.
EMBOSS data files are distributed with the application and stored in the standard EMBOSS data directory, which is defined by the EMBOSS environment variable EMBOSS_DATA.
To see the available EMBOSS data files, run:
% embossdata -showall
To fetch one of the data files (for example 'Exxx.dat') into your current directory for you to inspect or modify, run:
% embossdata -fetch -file Exxx.dat
Users can provide their own data files in their own directories. Project specific files can be put in the current directory, or for tidier directory listings in a subdirectory called ".embossdata". Files for all EMBOSS runs can be put in the user's home directory, or again in a subdirectory called ".embossdata".
The directories are searched in the following order:
The following notes based on Derek Gatherer's comments are useful for interpreting the significance of any difference between the tables.
It's not normally possible to be certain a a difference is statistically significant just from looking at it, as it is essentially a descriptive statistic about the difference between two 64-mer vectors. If you have a whole lot of sequences and codcmp results for all the possible pairwise comparisons, then the resulting distance matrix can be used to build a phylogenetic tree based on codon usage.
However, if you generate a series of random sequences, measure their codon usage and then do codcmp between each of your test sequences and all the random sequences, you could then use a z-test to see if the result between the two test sequences was outside of the top or bottom 5%.
This would assume that the codcmp results were normally distributed, but you could test that too. The simplest way is just to plot them and look for a bell-curve. For more rigour, find the mean and standard deviation of your results from the random sequences, use the normal distribution equation to generate a theoretical distribution for that mean and standard deviation, and then perform a chi square between the random data and the theoretically generated normal distribution. If you generate two sets of random data, each based on your two test sequences, an F-test should be used to establish that they have equal variances. Then you can safely go ahead and perform the z-test.
You could use the shuffle program to base your random sequences on the test sequences - so that would ensure the randomised background had the same nucleotide content. F-tests, z-tests and chi-tests can all be done in Excel, as well as being standard in most statistical analysis packages.
Program name | Description |
---|---|
cai | Calculate codon adaptation index |
chips | Calculates Nc codon usage statistic |
cusp | Create a codon usage table from nucleotide sequence(s) |
syco | Draw synonymous codon usage statictic plot for a nucleotide sequence |
Some more statistics were added by
David Martin (dmartin © rfcgr.mrc.ac.uk)