Bioinformatical problem - specific word enrichment in a given sequence My bioinformatical problem looks like this:
two sets of gene sequences (size of set A > 1000, size of set B > 1000; sequence length varies from 1000 to 100000).  

Set_A_Sequence_1: ACGTACGTACGT...
     Set_A_Sequence_2: ACGGAAGT AAA T...
     ....
     Set_B_Sequence_1: AAA G AAA TG AAA ...
     Set_B_Sequence_2: AAA TC AAA C AAA ...
     ...

I want to see if Set_B sequences are enriched for specific word (for example: AAA).
How can I do this?
I came up with three solutions:  


*

*Count how many Set_A sequences have/don't have word;
Count how many Set_B sequences have/don't have word.
Apply Fisher's test.  

*Count how many word occurrences there are per sequence in Set_A;
Count how many word occurrences there are per sequence in Set_B.
(For a given set example that would be: Set_A:0,1; Set_B:3,3).
What statistical test I can use for such enrichment analysis?  

*Calculate percentage of sequence in Set_A covered with word;
Calculate percentage of sequence in Set_B covered with word.
(For example, data would look like this: Set_A:0%,25%; Set_B:75%,75%).
What statistical test I can use for such enrichment analysis?  


Questions:
Is it right to use Fisher test in solution 1 (Contain/Don't contain word)?
What statistical tests I could use for solution 2 (Number of words)?
What statistical tests I could use for solution 3 (Coverage with word)?
Edit
Simplified data looks like this:  
Sequence name   Length   Contain word(0/1)   Number of words   Coverage with word(%)  

Set_A_seq_1     1000               0                0                 0
Set_A_seq_2     2000               1                1                 15
Set_A_seq_3     3450               0                0                 0
Set_A_seq_4     10000              0                0                 0
Set_A_seq_5     25000              1                2                 5
...

Set_B_seq_1     20000              1                3                 25  
Set_B_seq_2     100000             1                3                 30
Set_B_seq_2     9000               1                5                 70
Set_B_seq_2     10000              1                10                85
Set_B_seq_2     12000              1                7                 60
...          

EDIT 
I wasn't able to find a lot of published methodology for genomic site enrichment, but this figure suggests perfect way of solving problem that I have.  Figure A. - Enrichment in three different genomic sites compared using permutation and odd enrichment to permutated data.

 A: Solution 1 has an issue with sequence length. For instance, if you are interested in the word A and all your sequences have length 10,000 it is extremely likely that they all contain the word of interest in which case the Fisher test will not report anything significant, even if the occurrence of the word varies a lot within the sequences.
Solution 2 suffers some bias if the sequences from the set A do not have the same average length as the sequences of set B.
Solution 3 looks best to me. But you should concatenate all the sequences of set A, compute the average coverage by the word of interest and do the same with the sequences of set B, so that you are down to the problem of comparing two proportions.
To my knowledge, giving the correct answer to your problem is currently impossible because the occurrences of the words are not independent. If these are real genes there can be strong local dependencies between the words, and modeling those dependencies is technically challenging.
However, if you are ready to accept the simplifying assumption that words are independent and identically distributed, you can use the Gaussian approximation and wrap it up with Student's t-test. You can find a full account of this approach on this page.
Briefly, you compute the two proportions $p_1$ and $p_2$, use the pooled standard error estimate $\sqrt{p_1(1-p_1)/n_1 + p_2(1-p_2)/n_2}$, where $n_1$ and $n_2$ are the total nucleotide lengths of sequences in set A and B and look up the value of the following statistic in the quantiles of the standard Gaussian distribution.
$$ x = \frac{p_1-p_2}{\sqrt{p_1(1-p_1)/n_1 + p_2(1-p_2)/n_2}} $$
If you use R, the p-value of the test will be 2*pnorm(abs(x), lower.tail=FALSE).
A: This question reminded me of the FASTQC output....that is the result of scanning many short sequences (reads)....and looking for overrepresented motifs
From : http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3%20Analysis%20Modules/11%20Overrepresented%20Kmers.html

This module counts the enrichment of every 5-mer within the sequence
  library. It calculates an expected level at which this k-mer should
  have been seen based on the base content of the library as a whole and
  then uses the actual count to calculate an observed/expected ratio for
  that k-mer.

