|
|
codcmp |
It 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.
The usage fraction of a codon is its proportion (0 to 1) of the total 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 fraction. The sum of the differences and the sum of the differences squared is reported in the output file, together with the number of unused codons.
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 standard output
-filter boolean Read standard input, write 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 = 0.149 Codons not appearing = 0 |
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:
| Program name | Description |
|---|---|
| cai | CAI codon adaptation index |
| chips | Codon usage statistics |
| cusp | Create a codon usage table |
| syco | Synonymous codon usage Gribskov statistic plot |
Some more statistics were added by
David Martin (dmartin © rfcgr.mrc.ac.uk)