 Dear students, in this module, I'll be summarizing the entire scope of this course for you. So to begin with, we looked at the definitions of bioinformatics. So briefly, bioinformatics is the science of managing, organizing, obtaining, analyzing, and processing biological data. The data can be from the experiments or from the instruments. Secondly, we looked at the need for bioinformatics in the modern day. The need for bioinformatics is underlined by the fact that every day we're getting better and better instrumentation. So more and more data is being produced by these next generation instruments, such as the next generation sequencers or mass spectrometers. Therefore, there is a need to do more bioinformatics in order to manage the data that is reported. Within bioinformatics, several areas exist, such as genomics, proteomics, systems biology, personalized medicine, and so on. So we touched on genomics, proteomics, and structural modeling in this course. Some of the interesting angles to bioinformatics included it being an interdisciplinary science. So bioinformatics involves computer science, mathematics, chemistry, biology, and several other disciplines. So bioinformatics lies at the crossroads of each of these disciplines. Of course, as I just mentioned, the requirement produced by the next generation instruments in the form of data manipulation and processing has created a huge demand for bioinformatics. In this course, we specifically focused on three different areas within bioinformatics. So the first one was to compare sequences. So this involves sequence alignment, pairwise sequence alignment, multiple sequence alignment, scoring matrices, and the algorithms, such as dynamic programming. Next, we looked at the comparison of structures. So how structures are formed and how they can be compared. And lastly, we looked at structure prediction using homology, modeling. For each one of these topics, we had three specific interests. The first one was the algorithms that are there for addressing the problem. So we only looked at very simple seminal algorithms for each of these problems. Next, we looked at the databases that are there, which contain the data from each one of these areas in bioinformatics. And lastly, the online tools that are available if you want to quickly perform these tasks. So we studied the basic algorithms for each topic and we try to see how the process works. Of course, now with the modern day algorithms, there are newer versions, faster computing resources, and you can perform the same task in a much shorter time. You might be interested in developing even newer algorithms yourself as well. So with the evolution and growth of bioinformatics, newer algorithms are available, faster computers are available, and this is a rapidly growing field.