codcmp.txt

emboss的linux版本的源代码

                                  codcmp 



Function

   Codon usage table comparison

Description

   This program reads in two codon usage table files.

   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

   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