1. Probabilities and Probabilistic Models

2. Probabilistic models
A model means a system that simulates an object under consideration.
A probabilistic model is a model that produces different outcomes with different probabilities – it can simulate a whole class of objects, assigning each an associated probability.
In bioinformatics, the objects usually are DNA or protein sequences and a model might describe a family of related sequences.

3. Examples
The roll of a six-sided die – six parameters p1, p2, …, p6, where pi is the probability of rolling the number i. For probabilities, pi > 0 and
Three rolls of a die: the model might be that the rolls are independent, so that the probability of a sequence such as [2, 4, 6] would be p2 p4 p6.
An extremely simple model of any DNA or protein sequence is a string over a 4 (nucleotide) or 20 (amino acid) letter alphabet. Let qa denote the probability, that residue a occurs at a given position, at random, independent of all other residues in the sequence. Then, for a given length n, the probability of the sequence x1,x2,…,xn is

4. Conditional, joint, and marginal probabilities
two dice D1 and D2. For j = 1,2, assume that the probabi-lity of using die Dj is P(Dj ), and for i = 1,2,, 6, assume that the probability of rolling an i with dice Dj is
In this simple two dice model, the conditional probability of rolling an i with dice Dj is:
The joint probability of picking die Dj and rolling an i is:
The probability of rolling i – marginal probability

5. Maximum likelihood estimation
Probabilistic models have parameters that are usually estimated from large sets of trusted examples, called a training set.
For example, the probability qa for seeing amino acid a in a protein sequence can be estimated as the observed frequency fa of a in a database of known protein sequences, such as SWISS-PROT.
This way of estimating models is called Maximum likelihood estimation, because it can be shown that using the observed frequencies maximizes the total probability of the training set, given the model.
In general, given a model with parameters and a set of data D, the maximum likelihood estimate (MLE) for  is the value which maximizes P(D | ).

6. Model comparison problem
An occasionally dishonest casino uses two kinds of dice, of which 99% are fair, but 1% are loaded, so that a 6 appears 50% of the time.
We pick up a dice and roll [6, 6, 6]. This looks like a loaded die, is it? This is an example of a model comparison problem.
I.e., our hypothesis Dloaded is that the die is loaded. The other alternative is Dfair. Which model fits the observed data better? We want to calculate:
P(Dloaded | [6, 6, 6])

7. Prior and posterior probability
P(Dloaded | [6, 6, 6]) is the posterior probability that the dice is loaded, given the observed data.
Note that the prior probability of this hypothesis is 1/100 – prior because it is our best guess about the dice before having seen any information about the it.
The likelihood of the hypothesis Dloaded:
Posterior probability – using Bayes’ theorem

8. Comparing models using Bayes’ theorem
We set X = Dloaded and Y = [6, 6, 6], thus obtaining
The probability P(Dloaded) of picking a loaded die is 0.01.
The probability P([6, 6, 6] | Dloaded) of rolling three sixes using a loaded die is 0.53 =
The total probability P([6, 6, 6]) of three sixes is
P([6, 6, 6] | Dloaded) P(Dloaded) + P([6, 6, 6] | Dfair)P(Dfair).
Now,
Thus, the die is probably fair.

9. Biological example
Lets assume that extracellular (ext) proteins have a slightly different composition than intercellular (int) ones. We want to use this to judge whether a new protein sequence x1,…, xn is ext or int.
To obtain training data, classify all proteins in SWISS-PROT into ext, int and unclassifiable ones.
Determine the frequencies and of each amino acid a in ext and int proteins, respectively.
To be able to apply Bayes’ theorem, we need to determine the priors pint and pext, i.e. the probability that a new (unexamined) sequence is extracellular or intercellular, respectively.

10. Biological example - cont.
We have: and
If we assume that any sequence is either extracellular or intercellular, then we have
P(x) = pext P(x | ext) + pintP(x | int).
By Bayes’ theorem, we obtain
the posterior probability that a sequence is extracellular.
(In reality, many transmembrane proteins have both intra- and extracellular components and more complex models such as HMMs are appropriate.)

11. Probability vs. likelihood pravdepodobnosť vs. vierohodnosť
If we consider P( X | Y ) as a function of X, then this is called a probability.
If we consider P( X | Y ) as a function of Y , then this is called a likelihood.

12. Sequence comparison by compression

13. Motivation
similarity as a marker for homology. And homology is used to infer function.
Sometimes, we are only interested in a numerical distance between two sequences. For example, to infer a phylogeny.
Figure adapted from

14. Text- vs DNA compression
compress, gzip or zip – routinely used to compress text files. They can be applied also to a text file containing DNA.
E.g., a text file F containing chromosome 19 of human in fasta format – |F|= 61 MB, but |compress(F)|= 8.5 MB.
8 bits are used for each character. However, DNA consists of only 4 different bases – 2 bits per base are enough: A = 00, C = 01, G = 10, and T = 11.
Applying a standard compression algorithm to a file containing DNA encoded using two bits per base will usually not be able to compress the file further.

15. The repetitive nature of DNA
Take advantage of the repetitive nature of DNA!!
LINEs (Long Interspersed Nuclear Elements), SINEs.
UCSC Genome Browser:

16. DNA compression
DNA sequences are very compressible, especially for higher eukaryotes: they contain many repeats of different size, with different numbers of instances and different amounts of identity.
A first idea: While processing the DNA string from left to right, detect exact repeats and/or palindromes (reverse-complemented repeats) that possess previous instances in the already processed text and encode them by the length and position of an earlier occurrence. For stretches of sequence for which no significant repeat is found, use two-bit encoding. (The program Biocompress is based on this idea.)
Data structure for fast access to sequence patterns already encountered.
Sliding window along unprocessed sequence.

17. DNA compression
A second idea: Build a suffix tree for the whole sequence and use it to detect maximal repeats of some fixed minimum size. Then code all repeats as above and use two-bit encoding for bases not contained inside repeats. (The program Cfact is based on this idea.)
Both of these algorithms are lossless, meaning that the original sequences can be precisely reconstructed from their encodings. An number of lossy algorithms exist, which we will not discuss here.
In the following we will discuss the GenCompress algorithm due to Xin Chen, Sam Kwong and Ming Li.

18. Edit operations
The main idea of GenCompress is to use inexact matching, followed by edit operations. In other words, instances of inexact repeats are encoded by a reference to an earlier instance of the repeat, followed by some edit operations that modify the earlier instance to obtain the current instance.
Three standard edit operations:
Replace: (R, i, char) – replace the character at position i by character char.
Insert: (I, i, char) – insert character char at position i.
Delete: (D, i) – delete the character at position i.

19. Edit operations different edit operation sequences:
(a) C C C C R C C C C C or (b) C C C C D C I C C C C
g a c c g t c a t t g a c c g t c a t t
g a c c t t c a t t g a c c t t c a t t
infinite number of ways to convert one string into another.
Given two strings q and p. An edit transcript (q, p) is a list of edit operations that transforms q into p.
E.g., in case (a) the edit transcript is: (gaccgtcatt, gaccttcatt) = (R, 4, t),
whereas in case (b) it is: (gaccgtcatt, gaccttcatt) = (D, 4), (I, 4, g).
(positions start at 0 and are given relative to current state of the
string, as obtained by application of any preceding edit operations.)

20. Encoding DNA
Using the two-bit encoding method, gaccttcatt can be encoded in 20 bits:
The following three encode gaccttcatt relative to gaccgtcatt:
In the exact matching method we use a pair of numbers (repeat − position, repeat − length) to represent an exact repeat. We can encode gaccttcatt as (0, 4), t, (5, 5), relative to gaccgtcatt. Let 4 bits encode an integer, 2 bits encode a base and one bit to indicate whether the next part is a pair (indicating a repeat) or a base. We obtain an encoding in 21 bits:

21. Encoding DNA
In the approximate matching method we can encode gaccttcatt as (0, 10), (R, 4, t) relative to gaccgtcatt. Let use encode R by 00, I by 01, D by 11 and use a single bit to indicate whether the next part is a pair or a triplet. We obtain an encoding in 18 bits:
For approximate matching, we could also use the edit sequence (D, 4), (I, 4, t), for example, yielding the relative encoding (0, 10), (D, 4), (I, 4, t), which uses 25 bits:

22. GenCompress: a one-pass algorithm based on approximate matching
For a given input string w, assume that a part v has already been compressed and the remaining part is u, with w = vu. The algorithm finds an “optimal prefix” p of u that approximately matches some substring of v such that p can be encoded economically. After outputting the encoding of p, remove the prefix p from u and append it to v. If no optimal prefix is found, output the next base and then move it from u to v. Repeat until u = . w v u p’ p

23. The condition C
How do we find an “optimal prefix” p? The following condition will be used to limit the search.
Given two numbers k and b. Let p be a prefix of the unprocessed part u and q a substring of the processed part v. If |q| > k, then any transcript (q, p) is said to satisfy the condition C = (k, b) for compression, if its number of edit operations is  b.
Experimental studies indicate that C = (k, b) = (12, 3) gives good results.
In other words, when attempting to determine the optimal prefix for compression, we will only consider repeats of length k that require at most b edit operations.

24. The compression gain function
We define a compression gain function G to determine whether a particular approximate repeat q, p and edit transcript are beneficial for the encoding:
G(q, p, ) = max {2|p| − |(i, |q|)| − wl · |(q, p)| − c, 0}.
where
p is a prefix of the unprocessed part u,
q is a substring of the processed part v of length |q| that starts at position i,
2|p| is the number of bits that the two-bit encoding would use,
|(i, |q|)| is the encoding size of (i, |q|),
w is the cost of encoding an edit operation,
|(q, p)| is the number of edit operations in (q, p),
and c is the overhead proportional to the size of control bits.

25. The GenCompress algorithm
Input: A DNA sequence w, parameter C = (k, b)
Output: A lossless compressed encoding of w
Initialization: u = w and v = e
while u ¹ e do
Search for an optimal prefix p of u
if an optimal prefix p with repeat q in v is found then
Encode the repeat representation (i, |q|), where i is
the position of q in v, together with the shortest edit
transcript (q, p). Output the code.
else Set p equal to the first character of u,
encode and output it.
Remove the prefix p from u and append it to v
end