codcmp

 

Wiki

The master copies of EMBOSS documentation are available at http://emboss.open-bio.org/wiki/Appdocs on the EMBOSS Wiki.

Please help by correcting and extending the Wiki pages.

Function

Codon usage table comparison

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

Question:

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

Here is a sample session with codcmp

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

   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)

Input file format

It reads in the Codon Usage Tables - these are available as EMBOSS data files. See below for details.

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         = 0.148
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:

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

None.

Warnings

None.

Diagnostic Error Messages

None.

Exit status

This program always exits with a status of 0.

Known bugs

None.

See also

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

Author(s)

Alan Bleasby (ajb © ebi.ac.uk)
European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK

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

History

Completed 9 Sept 1999
20 Oct 2000 - David Martin added a couple more statistics to the output.

Target users

This program is intended to be used by everyone and everything, from naive users to embedded scripts.

Comments

None