 Dear students, in this module, we're going to discuss the progressive alignment for multiple sequence alignment or simply progressive alignment for MSA. So, MSA involves progressive alignment by using pair-wise sequence alignment repeatedly. So, you initially align two sequences and you create such sequence alignment pairs and then you align the pairs in order to obtain an overall multiple sequence alignment. However, if you take notice of the iterative nature of MSA, it means it will take a lot of time. So, progressive alignment because you have to create all these alignment combinations takes a lot more time than a simple pair-wise alignment. So, the important thing is to understand how progressive alignment works. So, let's take a look at this. So, as a first step in progressive alignment for MSA, you perform pair-wise alignments on all sequences that is you take these sequences and you align sequence one with all other sequences. So, you will be aligning one with two, one with three, one with four and one with five. So, in this way, sequence two will also be aligned with all other sequences and then this process will continue until all the sequence alignment combinations are created. So, once you have these combinations of alignment, then you're ready to move to the second step. So, in the second step, what you do is you construct a guide tree or simply a dendogram using a distance matrix. So, here you need to understand what is a distance matrix and what is a guide tree. So, for instance, this is a dendogram or a guide tree for four sequences S1, S2, S3 and S4. So, to understand what this tree represents, you need to consider the length of these branches. So, here you can see that this branch and this branch is equal in length and this branch has a very long length. Also, these two branches are longer than these and the branch is smaller for these two. So, essentially it means that S4 and S2 are similar while S1 and S3 are similar to each other. So, how do you compute the distance matrix? Now that you know what is a guide tree. So, the distance matrix is simply the distance equals 1 minus the number of matches over number of comparisons. So, if you remember you have already performed the pairwise alignment here. So, you already know how many matches exist between S1 and let's say S2 as well as S1 and S3 and similarly S1 and S4. So, since you know the number of matches, as well as the number of comparisons, you can easily compute the distance between each one of these sequences. So, now you have computed the distance and now you want to move to the third step that is you need to create a branching order like this in order to represent the distances. So, as I just mentioned that similarity is all is equal to matches over comparison. So, if you look at this similarity matrix, then you can see that amongst all the values in this matrix, the highest value is 0.87 and this is between V1 and V3. So, V1 and V3 are very similar and the similarity score is 0.87. Next you have 0.62 which is the similarity between V3 and V4. So, note that this is the similarity and now you want to create a guide tree and you can see that V1 and V3 which had the most similarity are given here as present on the same branch and next V3 and V4 had a score of 0.62 which meant that they were slightly less similar to V1 and V3. So, V3 is here and V4 is next to V3 and in the end you have V2 which scored the least in comparison to the other sequences. So, in this way you have constructed something called a guide tree and you have put most similar sequences on the same branch followed by other sequences that are increasingly different from them. This approach however has some shortcomings. For instance, if you have performed an alignment as the first step which may be incorrect then your entire guide tree will be biased towards that. So, there is a lot of dependence on the first alignment that you employ to create the guide tree. Also, if the sequences that you use are dissimilar that then this can introduce an error in your alignment as well. And lastly to overcome this what you do is you create a guide tree and then you repeat the process to create better and improved version of the guide trees that is iteratively. So, in conclusion the progressive alignments can help you to perform multiple sequence alignment and that in progressive alignment you repeatedly align sequences to arrive at a guide tree using the similarity between sequences and then you create the relationship between the sequences. These approaches can help you to refine the results especially if the results need improvement you can iterate this process. You can repeat this process by using different pairs that you employ to start the progressive alignment in the MSA.