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Comparing Genes, Proteins, and Genomes (Bioinformatics III)

Bioinformatics,

Once we have sequenced genomes in the previous course, we would like to compare them to determine how species have evolved and what makes them different. In the first half of the course, we will compare two short biological sequences, such as genes (i.e., short sequences of DNA) or proteins. We will encounter a powerful algorithmic tool called dynamic programming that will help us determine the number of mutations that have separated the two genes/proteins. In the second half of the course, we will "zoom out" to compare entire genomes, where we see large scale mutations called genome rearrangements, seismic events that have heaved around large blocks of DNA over millions of years of evolution. Looking at the human and mouse genomes, we will ask ourselves: just as earthquakes are much more likely to occur along fault lines, are there locations in our genome that are "fragile" and more susceptible to be broken as part of genome rearrangements? We will see how combinatorial algorithms will help us answer this question. Finally, you will learn how to apply popular bioinformatics software tools to solve problems in sequence alignment, including BLAST.

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Rating 4.7 based on 23 ratings
Length 7 weeks
Starts Mar 8 (6 weeks ago)
Cost $79
From University of California San Diego via Coursera
Instructors Pavel Pevzner, Phillip E. C. Compeau, Phillip Compeau, Nikolay Vyahhi
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Science
Tags Computer Science Life Sciences Algorithms Bioinformatics Health Informatics

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What people are saying

graph theory

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

been 6 weeks since

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.

meaningful comparison must consider

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.

never seen any mooc

I have never seen any MOOC like this one.

research behind genetics especially

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.

authors put into making

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.

covers rearrangements within genes

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.

introduces chromosme rearrangement analysis

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

reading other bioinformatics materials

including al programming challenges

often found myself laying

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!

part still too confused

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).

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Rating 4.7 based on 23 ratings
Length 7 weeks
Starts Mar 8 (6 weeks ago)
Cost $79
From University of California San Diego via Coursera
Instructors Pavel Pevzner, Phillip E. C. Compeau, Phillip Compeau, Nikolay Vyahhi
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Science
Tags Computer Science Life Sciences Algorithms Bioinformatics Health Informatics

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