Visualizing the Smith Waterman Local Alignment Algorithm

30 Mar 2016

This is an interactive d3 visualization of the Smith-Waterman local alignment algorithm. This algorithm is used to align two sequences while allowing for minor imperfections (like gaps or mismatches) in the alignment. It can be used for a variety of things from matching molecular sequences (e.g. in DNA) to plagiarism detection. If you never heard of it you can scroll down and read the explanation of the algorithm.

To use the visualization tool, enter the two sequences you want to align in the fields below. A Match Score is added to the alignment when two sequence elements match. The Mismatch Score is the penalty if the alignment contains two elements that don’t match, and the Gap Score is the penalty for each gap that is introduced to one of the sequences.

You can use the defaults or fill in your own sequences and parameters in the fields below. Click on Generate Matrix to see the alignment matrix below. Sequence elements must be separated by white space. Each cell displays the score of the optimal alignment that ends with the corresponding combination of sequence elements. The arrows allow you to back trace this alignment: if you click on one of the cells the corresponding alignment is displayed on the right side of the matrix, and its evolution is highlighted in the matrix. Try it out!

Sequence 1
Sequence 2
Match Score
Mismatch Score
Gap Score

If you are interested in the code you can find it on my GitHub.

The Algorithm

My coauthors and I use it to detect similar legislation in US state legislation, the working paper will be out soon. Our project is related to this tool, the Legislative Influence Detector, developed by Matt Burgess, Eugenia Giraudy, Julian Katz-Samuels and Joe Walsh last at Data Science for Social Good 2015 to detect influence of interest groups through model legislation.

The algorithm was developed by Smith and Waterman in 1981 to find matching molecular subsequences in proteins. Given two sequences the algorithm calculates the optimal match between those sequences, called an alignment. It is called a local alignment algorithm because it can return just subsections of the sequences as compared to global alignment where the complete sequences are aligned. Many sequences that we want to align (e.g. DNA sequences, text strings, etc.) do not match perfectly. Formatting, white space, typographical errors, etc. hinder the detection of otherwise homologous matches. The Smith-Waterman algorithm returns the optimal alignment while allowing for mismatches and gaps. The extent to which such imperfections are tolerated is governed by external (tuning-) parameters that are set by the researcher. There are three such parameters, the match score, the mismatch score and the gap score. The goal of the algorithm is to return the alignment with the highest score given the input sequences and parameters.


Because there are a lot of possibilities for local alignments, we rely on a dynamic programming approach to calculate the optimal local alignment given the parameters. Dynamic programming means that results of previous calculations are stored and used in later calculations. This works in our case because every alignment’s score is a function of the characters that are aligned before it. To make this a bit clearer consider the first four elements of the two boring (just think S1 is your term paper and S2 is the Wikipedia article on the topic…) example sequences from the visualization above:

s1: a b c d

s2: a b c x

We see right away that the best local alignment here is:

s1: a b c

s2: a b c

We can more rigorously show that this is the best alignment given the scoring parameters by analyzing the alignment from back to front.

If this is the best alignment of the four letters it must also be the best alignment for the previous sub-sequences ending in “c” and “c”. This must be true because if we had a sub-optimal alignment in the first three characters we could always improve the alignment of the four characters by choosing a better one for the first three. We know that the alignment

s1: a b - c d

s2: a b c - x

is not optimal because it is not optimal in the first three characters (the “-“ indicate a gap and we introduced two unnecessary gaps). Therefore, if we know that the optimal alignment of the first three elements is:

s1: a b c

s2: a b c

we can exclude from consideration all alignments that do not start with this alignment. So when considering where to go from here we just have to decide if we want to accept the mismatch “d”-“x”, introduce a gap in sequence 1 or sequence 2, or end the alignment here (keep these four steps in mind). This is an easy decision since we have set parameters that increase or decrease our total alignment score depending on which step we chose (the match, mismatch and gap parameters).

If the next two elements match, the decision is easy - we add them and increase the total alignment score. However, if they don’t match the decision is more difficult. We only want to introduce a gap or mismatch, if it pays off later (that is if we thereby can ‘reach’ another part of the sequence that matches again). We only know that once we went through the whole sequence. The central idea is therefore to find all optimal alignments ending in all possible spots, keep these scores and after everything is calculated finding the alignment that with the highest overall score.


In order to systematically do this, we create a matrix (the matrix you see in the visualization above) where each combination of elements of the two sequences is assigned a cell. Denote the two sequences as and . Now each cell with row and column indices and corresponds to an alignment that ends in and (note that and can also be gaps that are introduced in either or , this means that and can be changed by the algorithm.

The first step to finding the optimal alignment is filling the scoring matrix. Let , and be the match, mismatch and gap scores. Define the scoring function:

\begin{equation} \label{eqn:scoring} S(a_i, b_j) = \delta^{\mathbb{I}(a_i = b_j)} + \epsilon^{\mathbb{I}(a_i \neq b_j)} \end{equation}

Where is the indicator function which returns if the condition is true and otherwise. In words, this function returns the match score if the elements match and the mismatch score otherwise.

Then the entry for each cell , and of the matrix is filled by the following recursive function:

\begin{equation} \label{eqn:fill} M_{i,j} = \max(M_{i-1,j-1} + S(a_i, b_j), M_{i-1,j} + \gamma, M_{i,j-1} + \gamma, 0) \end{equation}

The matrix is initialized empty with just a leading row of zeros and a leading column of zeros. In order to be able to insert gaps in the beginning of each sequence (or mathematically so always exists). Now we start in the upper left corner of the matrix and begin filling the cells according to the second equation. The first entry in the expression corresponds to a diagonal step, from to . If this step would be taken, elements and would be matched in the resulting alignment. If the two elements are equal the new score is the old one plus the match score if not the mismatch penalty is subtracted. The second and third entry correspond to an insertion of a gap in or respectively; or in the matrix moving one step to the right or one step down. Go back up to the matrix and check it out. You can see it by clicking on a cell, and then click on the adjacent cells above and on the left. If all scores are smaller or equal than zero, zero is filled into the cell. While filling the matrix with scores we also keep track of the history of the alignment. That is, we store which step was taken to fill a cell (from the left, the top, diagonally, or no step when zero is filled in). In the visualization this is indicated by the arrows pointing upwards, to the left or to the upper left corner of the cell.

Once the matrix is filled we scan it for the highest score. The optimal alignment is then found by following the arrows until we hit a cell with a zero or the first element in one of the sequences. You can explore different alignments in the visualization by clicking on cells. The alignment will be displayed on the right and the relevant cells will be highlighted.

This algorithm is computationally expensive, it needs time and space, which can be a lot if we want to consider longer sequences. However, it is guaranteed to find the optimal alignment given the scoring parameters. This follows from the fact that each alignment is optimal in its predecessors (see the intuition section).