Optional: Where in the Genome Does DNA Replication Begin? (Part 4)

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Do curso por University of California San Diego

Biology Meets Programming: Bioinformatics for Beginners

470 classificações

University of California San Diego

Biology Meets Programming: Bioinformatics for Beginners

470 classificações

Are you interested in learning how to program (in Python) within a scientific setting? This course will cover algorithms for solving various biological problems along with a handful of programming challenges helping you implement these algorithms in Python. It offers a gently-paced introduction to our Bioinformatics Specialization (https://www.coursera.org/specializations/bioinformatics), preparing learners to take the first course in the Specialization, "Finding Hidden Messages in DNA" (https://www.coursera.org/learn/dna-analysis). Each of the four weeks in the course will consist of two required components. First, an interactive textbook provides Python programming challenges that arise from real biological problems. If you haven't programmed in Python before, not to worry! We provide "Just-in-Time" exercises from the Codecademy Python track (https://www.codecademy.com/learn/python). And each page in our interactive textbook has its own discussion forum, where you can interact with other learners. Second, each week will culminate in a summary quiz. Lecture videos are also provided that accompany the material, but these videos are optional.

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Week 2

Where in the Genome Does Replication Begin? (Part 2)

Optional: Where in the Genome Does DNA Replication Begin? (Part 3) 9:05

Optional: Where in the Genome Does DNA Replication Begin? (Part 4) 4:10

Conheça os instrutores

Pavel Pevzner
Professor
Department of Computer Science and Engineering
Phillip Compeau
Visiting Researcher
Department of Computer Science & Engineering

Let's define mathematical framework for how to deal with this.

We define skew of k, the simplest number of G

minus number of C in the first K nucleotides of the genome.

And skew diagram is simply plot showing Skew(k) against k.

Now, let's construct skew diagram for the E Coli genome.

It looks beautiful.

Where do we think is the origin of replication in E Coli?

Something that the we almost had no hope to find.

Of course it's at a point the way it was decreasing and

suddenly start increasing, which is here.

We found the replication origin of E Coli.

And now, lets try to find frequent words in this origin,

and hopefully we will find DnaA box in this space.

Well after we run our frequent words problem,

unfortunately it turns out there are no frequent words that appear

even three times in this replication origin, which means that we fail.

There are many reasons why we may fail.

Maybe origin for application as derived from skew diagram doesn't show precisely

to the area where we want to see.

Or maybe, just maybe, we don't have a good grip on how

hidden messages in E Coli origin look like.

Should we give up?

Let's try to proceed further and figure out what else can be done.

And when we were looking for frequent words,

our view of frequent words was very naive.

We assumed that frequent words are simply k-mer.

But maybe hidden messages that cell uses to

initiate replications are more elusive, more subtle.

Let's look at this origin for replication in Vibrio cholerae and try to see it.

Maybe there is something else.

We already found six nine-mer in this region, but maybe,

just maybe, there is something else that deserves our attention.

And if you look carefully there, then you will see that in addition to these

six hidden messages there are two more.

There are actually two nine-mers that look almost like canonical nine-mers.

They differ just in a single mutation.

And they also represent DnaA boxes because DNA can bind not only to

the perfect nine-mer, but to different variation of this nine-mer.

To find this type of more elusive frequent words,

we need to solve frequent words with mismatches problem.

When the input is a string Text and integers k and d,

we want to find all most frequent k-mers with at most d mismatches in the Text.

And finally after we've done this and

run our frequent words with mismatches problem,

we do find somewhat more elusive frequent words in E Coli genome.

And they turn out to be indeed, real DnaA boxes, in E Coli.

Now, what I described is a very idealistic view

of how origin of replication can be found.

In reality, some bacteria have fewer DnaA boxes, and

our frequent words algorithm won't work for finding them.

Terminus of replication is often not located directly opposite

to the origin of replication.

And the skew diagram is often more complex than in the case of E Coli.

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