Comparing Genes, Proteins, and Genomes (Bioinformatics III)

Graph theory can be esoteric and abstract, but when anchored to interesting problems in biology, it becomes a lot of fun.

It took me about 1 week to finish the course, including al programming challenges, and it's been 6 weeks since I am waiting to get my work graded.

Having learned to sequence genes in the last course segment, this installment focuses on how to compare them -- an often-difficult task because a meaningful comparison must consider the possibility of mutations, sharply increasing the complexity of the task.

I have never seen any MOOC like this one.

for me this was a very intresting courde where I learined a lot about the research behind genetics especially how difficult it is to interprete the experiemntal data.

I always had a hunch that this was an excellent course, but only after reading other bioinformatics materials did it become clear to me how much effort the authors put into making this course the best that it can be.My only criticism is that the peer reviewed final challenge doesn't work very well because there are not that many participants to do the peer reviews.

The course also covers rearrangements within genes and genomes, leading to ways to compare, say, human and mouse genomes, and more kinds of computational graphs.

A very well taught course that gives you the ins and outs of sequence comparision and introduces chromosme rearrangement analysis in a succint manner.

Although this is a course in bioinformatics I often found myself laying on the bed with a piece of paper drawing graphs in order to fully grasp what I need to do so my code would be more efficient.Excellent work!

some part still too confused to me It's simply astounding to see how clearly the authors managed to explain the concepts presented in the course (and in the entire series, for that matter).