Question: Does Needlman-Wunsch Algorithm Works With Float Values In Similarity Matrix?
3
gravatar for Shan
6.4 years ago by
Shan50
Shan50 wrote:

Hi

I want to do global sequence aligment. Referring to the wikipedia article of Needleman-Wunsch for global sequence alignment. I could see the following similarity matrix

            A       G        C        T
   A       10      -1       -3       -4
   G       -1       7       -5       -3
   C       -3      -5        9        0
   T       -4      -3        0        8

Instead of having these values I want to deal with the probability. So all the values will not be integers but real values.

In all of the matrices I had seen e.g BLOSUM50, all have the integer values. My question is that if the similarity matrix will be filled with real values. Would it work the same way.

Another thing is that, is there any special procedure to construct the similarity matrix or it could be any distance measure.

Thanks a lot.

sequence similarity alignment • 1.7k views
ADD COMMENTlink modified 6.4 years ago by Konrad80 • written 6.4 years ago by Shan50

Wouldn't you rather want to use a hidden markov model when working with probabilities?

ADD REPLYlink written 6.4 years ago by Niek De Klein2.4k
10
gravatar for Bill Pearson
6.4 years ago by
Bill Pearson820
Bill Pearson820 wrote:

The algorithm does not exclude floating point numbers, but many implementations of the algorithm do (e.g. ggsearch36 in the FASTA package), or the older align.c program in the FASTA2 package use integer matrices, because for a long time, integer arithmetic was much faster.

There are several papers on producing similarity matrices at arbitrary evolutionary distances. The Jones, Taylor, and Thornton paper (Comp. Appl. Biosci 1992 8:275-282, Comp. Appl. Biosci. is now Bioinformatics) is widely cited and their matrices widely used. More recently, Mueller, Spang and Vingron have a series of papers, including Mol. Biol. Evol., 2002 19:8-13.

However, it is not clear to me that different scoring matrices will have much effect on global (Needleman Wunsch) sequence alignments. Since those alignments are global, they must align from beginning to end, and the only difference different scoring matrices will have will depend on the gap penalties. For local alignments, the matrices matter a lot more (see Altschul's classic paper: J. Mol. Biol. (1991) 219:555).

ADD COMMENTlink written 6.4 years ago by Bill Pearson820
2
gravatar for Fabian Bull
6.4 years ago by
Fabian Bull1.3k
German
Fabian Bull1.3k wrote:

First of all: Yes it is possible. There is absolutly no reason why one should not use float values.

But keep in mind: The scores of the popular substitution-matrizes are computed using probabilities so I do not expect better results.

As stated in the comments: An natural of combining probabilities and alignments are Pair-HMMs. With these you can set your desired probabilities and ask for the optimal alignment (viterbi-algorithm).

ADD COMMENTlink written 6.4 years ago by Fabian Bull1.3k

@peri4n thanks a lot for the answer... I only have the match probabilities not transition ones... I will dig more into Pair-HMMs, I am a newbee to HMMs... It would be very nice of you if you could please recommend standard libraries and code plus usage examples... Thanks a lot...

ADD REPLYlink written 6.4 years ago by Shan50
0
gravatar for Konrad
6.4 years ago by
Konrad80
Cambridge, UK
Konrad80 wrote:

Using floating point values with NW is fine; however, using probabilities is not.

If you want to work with probabilities of alignments you need to multiply their values instead of adding. The Needleman-Wunsch algorithm uses addition since it deals with the logarithm of probabilities. This is sound since log(x * y) = log x + log y.

For a good introduction into the statistical motivation of sequence alignment algorithms, see e.g. Biological Sequence Analysis by Durbin & al.

The whole reason why matrices such as BLOSUM use integral values is that they allow vastly faster computation than floating point numbers. Furthermore, these matrices are actually derived from log’ed (and scaled) probabilities.

ADD COMMENTlink written 6.4 years ago by Konrad80
Please log in to add an answer.

Help
Access

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 2.3.0
Traffic: 1585 users visited in the last hour