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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 (c) 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
Codon usage table comparison
Version: EMBOSS:6.6.0.0
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
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 = 0.149
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
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 Calculate Nc codon usage statistic
codcopy Copy and reformat a codon usage table
cusp Create a codon usage table from nucleotide sequence(s)
syco Draw synonymous codon usage statistic plot for a nucleotide
sequence
Author(s)
Alan Bleasby
European Bioinformatics Institute, Wellcome Trust Genome Campus,
Hinxton, Cambridge CB10 1SD, UK
Please report all bugs to the EMBOSS bug team
(emboss-bug (c) 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 (c) emboss.open-bio.org) not to the original author.
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
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