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[MUSIC] So, now we will discuss how to implement sequence alignment in a more efficient way. Usually, we compare quite long sequences up to few tens of thousands of amino acids And runtime of alignment algorithms, as you know, is proportional to the number of edges, which is quadratic. And memory consumption, consumption of alignment algorithm is also proportional to the number of nodes, which is also quadratic. And memory is a, often bottleneck. Because on run time you can execute billions of configurations in one second, but to store billions of values in memory you need one or few gigabytes of memory. And, if you want to compare longer and longer, longer sequences, you still can wait for many seconds. When we, while your algorithms try to compute this alignment, but you donamp;#39;t have such huge amount of memory, which is needed, for algorithms. So how can we do this? Letamp;#39;s define middle column of our Manhattan grid as a column in the middle, and so itamp;#39;s quite sim
