 Dear students, now I'll be concluding this course for you and the following modules will only give you conclusions for each chapter that we have covered in this course. So the first chapter that we did in this course was sequences, the sequence analysis paradigm. You know that there can be multiple sequences such as from DNAs, from RNAs, from proteins, and you need to store them, you need to analyze them, and you need to compare them with other sequences. So we discussed initially how the genomes were sequenced and how the proteomes were sequenced. So to start with, you were trying to compare the sequences, be it from DNA, RNA or proteins, by doing a pairwise sequence alignment. So if you obtain a sequence from mass spectrometry in case of proteins or in case of genomes, if you get it from NGS, the next generation sequencers, then you would want to compare them with other similar sequences. So the pairwise sequence alignment helps you to do that. You can score the comparisons and therefore arrive at a value or a score which gives you the degree of similarity between different sequences. Next you perform the multiple sequence alignment. So the multiple sequence alignment strategy helped you to compare multiple sequences at the same time. This is especially useful if you're trying to compare orthologs and paralogs at the same time. So towards the types of alignment, we looked at the global alignment and the local alignment. So in case of global alignment, we took a biological sequence and we tried to fit another biological sequence on top of it by making an N to N comparison. The algorithm that we used for the global alignment was the needle-wrench algorithm. For the local alignment, which is different from the global alignment, in that in local alignment, we're trying to find portions within the sequence and comparing them with the other protein sequences or DNA sequences. So for the local alignments, the purpose is different that is to find domains and motifs that are conserved between sequences and the algorithm that we used for this strategy was the Smith-Wattman algorithm. Now, if you want to write these algorithms yourself, we discussed dynamic programming for it and how you could use dynamic programming to implement your own version of these algorithms. But these algorithms are also available in the form of BLAST and FASTA. So FASTA or fast alignment algorithm helps you to compare sequences as well. In case of BLAST, which means basic local alignment search tool, it can also compare locally and you can find comparisons between sequences. There are other tools as well, such as Clustall and you can, depending on your need, you can select the version of Clustall that you want to employ. Then online, you could find databases for these sequences as well, such as Gene Bank and Uniprot. So if you want to search for sequence in these databases, you can approach their website and just perform a BLAST query. The online tools or portals as they are called include Ensembl, XPC and Uniprot KB. So these online tools now are not just alignment tools. They provide you a wide variety of information regarding each protein sequence and the protein and gene sequence as well as the annotation, the gene ontology and so on and so forth. Thank you.