codcmp

Function

Description

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.

Statistical significance

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

Usage

This compares the codon usage tables for Escherichia coli and Haemophilus influenzae.

% codcmp Codon usage table comparison Codon usage file [Ehum.cut]: Eeco.cut Codon usage file [Ehum.cut]: Ehin.cut Output file [outfile.codcmp]:

Go to the output files for this example

Command line arguments

   Standard (Mandatory) qualifiers:
  [-first]             codon      First codon usage file
  [-second]            codon      Second codon usage file for comparison
  [-outfile]           outfile    Output file name

   Additional (Optional) qualifiers: (none)
   Advanced (Unprompted) qualifiers: (none)
   Associated qualifiers:

   "-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 deaths

Standard (Mandatory) qualifiers		Allowed values	Default
[-first] (Parameter 1)	First codon usage file	Codon usage file in EMBOSS data path	Ehum.cut
[-second] (Parameter 2)	Second codon usage file for comparison	Codon usage file in EMBOSS data path	Ehum.cut
[-outfile] (Parameter 3)	Output file name	Output file	<sequence>.codcmp
Additional (Optional) qualifiers		Allowed values	Default
(none)
Advanced (Unprompted) qualifiers		Allowed values	Default
(none)

Input file format

Output file format

Output files for usage example

File: outfile.codcmp

# CODCMP codon usage table comparison
# Eeco.cut vs Ehin.cut

Sum Squared Difference = 2.337
Mean Squared Difference = 0.037
Root Mean Squared Difference = 0.191
Sum Difference         = 9.840
Mean Difference         = 0.154
Codons not appearing   = 0

Data files

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.

To see the available EMBOSS codon usage files, run:

% embossdata -showall

To fetch one of the codon usage tables (for example 'Emus.cut') into your current directory for you to inspect or modify, run:

% embossdata -fetch -file Emus.cut

Notes

References

Warnings

Diagnostic Error Messages

Exit status

Known bugs

See also

Author(s)

Some more statistics were added by David Martin (dmartin © rfcgr.mrc.ac.uk)

History

Target users

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