 Dear students, now I will summarize what we have done with fast alignment algorithm and how it can be applied to solve your sequence comparison problems. So fast alignment algorithm can help you to briskly compare and score sequences from database. There are multiple types of fast alignment algorithms that are available and you can employ the one which you need and get a comparison result. So to start with, once you want to employ the fast alignment algorithm, the first step is to obtain the sequence or the IDs for the proteins or the genes. So how do you get them? So in case you know the sequences, so you can simply go to NCBI and or UniProt or SwissProt and obtain the protein sequence. In case of the genes with known sequences, you can go to the gene bank and obtain the sequence. Along with the sequence, you can also input the IDs. So both of them, that is the sequence and the IDs are available from these resources. However, if the sequence is unknown, that is you don't know what is the complete sequence of the molecule that you want to compare. Then you can perform NGS or mass spec and obtain the sequence and then simply input the sequence into the fast A website. Okay, to start with, you can go to the NCBI portal. You can look at this drop down list box and you can select proteins or genes and you can copy the ID for the sequence for step number two. So for step number two, you select which type of sequence you have and you input it here below. But first you have to select the database as well. So in this case, so it's a nucleotide search. So you just select the ENA, the EMBL bank database and you select which type of fast A, that is the fast alignment algorithm that you want to use. So here I've listed several of them. You can simply go to this link at Virginia.edu and you can select which type of fast A you want to employ. So the details are given here. Once you decide which type of algorithm you want to use, you can input your sequence that you copied from NCBI here. So in case of this example, it's a DNA sequence and next you can set up your search parameters. So here the match and the mismatch scores, if you remember the gap penalties, the opening of a gap, the extension of a gap, they're all given the k tuples or the word size. So this is the size of the word that fast A uses in searching the databases and some other parameters. Next you perform the search. So the output as a sample is given here and you can see so many proteins have been reported as a result of some other search. And of course you can look at the details of each one of them. So the red here means a better match and the green things here at the bottom, they are an average match. Okay. So the table of the results is also available at the fast A EBI portal and you can look at the scores. So this is very important. The higher the score, the better. So essentially we like high scores. So this is rank number one because this protein had a smaller score as compared to this. So this is rank number two and there will be many other proteins that will be ranked in the search like that. So to conclude on fast alignment algorithm, so it is an optimization or an approximation of the local and global alignment strategies towards speeding them up in the execution. So why do we want to approximate the very nice Smith Waterman or the Needleman Munch? Because in real life we are comparing several sequences and we want to do it in a short span of time. And these approximate strategies such as BLAST and now fast A help us to achieve that in a good amount of time. Also depending on the type of comparison that you want to perform, given your query sequence, then you can select the type of fast A that you want to use.