codcmp

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Function

Description

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.

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: Eecoli.cut Second Codon usage file: Ehaein.cut Output file [eecoli.codcmp]:

Go to the output files for this example

Command line arguments

Codon usage table comparison
Version: EMBOSS:6.4.0.0

   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 and exit. 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
   -version            boolean    Report version number and exit

Qualifier	Type	Description	Allowed values	Default
Standard (Mandatory) qualifiers
[-first] (Parameter 1)	codon	First codon usage file	Codon usage file in EMBOSS data path
[-second] (Parameter 2)	codon	Second codon usage file for comparison	Codon usage file in EMBOSS data path
[-outfile] (Parameter 3)	outfile	Output file name	Output file	<*>.codcmp
Additional (Optional) qualifiers
(none)
Advanced (Unprompted) qualifiers
(none)
Associated qualifiers
"-first" associated codon qualifiers
-format1 -format_first	string	Data format	Any string
"-second" associated codon qualifiers
-format2 -format_second	string	Data format	Any string
"-outfile" associated outfile qualifiers
-odirectory3 -odirectory_outfile	string	Output directory	Any string
General qualifiers
-auto	boolean	Turn off prompts	Boolean value Yes/No	N
-stdout	boolean	Write first file to standard output	Boolean value Yes/No	N
-filter	boolean	Read first file from standard input, write first file to standard output	Boolean value Yes/No	N
-options	boolean	Prompt for standard and additional values	Boolean value Yes/No	N
-debug	boolean	Write debug output to program.dbg	Boolean value Yes/No	N
-verbose	boolean	Report some/full command line options	Boolean value Yes/No	Y
-help	boolean	Report command line options and exit. More information on associated and general qualifiers can be found with -help -verbose	Boolean value Yes/No	N
-warning	boolean	Report warnings	Boolean value Yes/No	Y
-error	boolean	Report errors	Boolean value Yes/No	Y
-fatal	boolean	Report fatal errors	Boolean value Yes/No	Y
-die	boolean	Report dying program messages	Boolean value Yes/No	Y
-version	boolean	Report version number and exit	Boolean value Yes/No	N

Input file format

Output file format

Output files for usage example

File: eecoli.codcmp

# 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

Data files

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:

• . (your current directory)
• .embossdata (under your current directory)
• ~/ (your home directory)
• ~/.embossdata

Notes

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.

References

Warnings

Diagnostic Error Messages

Exit status

Known bugs

See also

Author(s)

Please report all bugs to the EMBOSS bug team (emboss-bug © emboss.open-bio.org) not to the original author.

Some more statistics were added by David Martin

Please report all bugs to the EMBOSS bug team (emboss-bug © emboss.open-bio.org) not to the original author.

History

Target users

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