This course introduces you to the basic biology of modern genomics and the experimental tools that we use to measure it. We'll introduce the Central Dogma of Molecular Biology and cover how next-generation sequencing can be used to measure DNA, RNA, and epigenetic patterns. You'll also get an introduction to the key concepts in computing and data science that you'll need to understand how data from next-generation sequencing experiments are generated and analyzed. This is the first course in the Genomic Data Science Specialization.
What Is Genomics?
Loading...
来自 Johns Hopkins University 的课程
Introduction to Genomic Technologies
1221 个评分
从本节课中
Overview
In this Module, you can expect to study topics of "Just enough molecular biology", "The genome", "Writing a DNA sequence", "Central dogma", "Transcription", "Translation", and "DNA structure and modifications".
与讲师见面
Steven Salzberg, PhDProfessor
Biomedical Engineering, Computer Science, and Biostatistics
Jeff Leek, PhDAssociate Professor, Biostatistics
Bloomberg School of Public Health
So the definition of, the dictionary definition of genomics,
is on the slide that you're seeing.
But we're not really going to use that.
Genomics is descrip, it's the study of genomes.
A genome is all of the molecular material inside your cells that defines
how your body works and how our bodies work.
There are multiple different things that we can study about genomes.
When we talk about the structure, what we often mean
is the actual sequence of nucleotides, As, Cs, Gs, and Ts in the genome.
So a genome is a long, long molecule comprised of DNA nucleotides.
And we call them, we use the letters A, C, G, and T to, as an abbreviation for
these four nuclear ties.
There's only four of them.
In the human genome,
there are approximately 3 billion of these letters strung together.
The structure of the human genome is that it's divided into 23 chromosome pairs.
22 of them are, identical to one another is two, two nearly identical copies.
One from mom and one from dad, and then you have x and y.
And if you're a man, you have x and y.
If you're a woman, you have x and x.
So that's another way to think about the structure.
And then within each chromosome,
there's a little bit of more structure we occasionally talk about.
In the middle of every chromosome, there's something called the centromere.
And at the ends of the chromosomes,
there are these special sequences that cap the chromosomes called telomere.
So that's the structure of genome.
But the function of a genome is, involves many, many other things.
That's what we're, really when we sequence a genome, what we're really getting at,
is trying to understand the function.
So the function is all the things the genome does.
So in that long,
long string of letters, we have encoded everything that that your body can do.
So we have the the instructions for how you make all the organs in your body.
For how your body develops from a single embryo,
into all these complicated tissues.
And we also have instructions for how your body does things like respiration and
metabolism.
And even such complicated things as building a brain.
Another aspect of genomics is evolution.
Evolution is a very broad topic.
When we're talking about evolution of genomes,
we talk about how genomes themselves change over time, and we usually mean over
evolutionary time, and those are very, very long time periods.
We know from having sequenced the human genomes, and now having sequenced many
humans individual genomes that all of us are nearly identical to one another.
So our genomes from generation to generation barely change at all and
most of those changes are small, random changes which don't have any effect.
But we can compare our genomes to genomes of other creatures
such as chimpanzees which are our closest relatives.
Diverge from us approximately 6 million years ago.
And we can compare our genomes to genomes of much more distant things like
fruit flies, nematodes, or even bacteria.
And when we do that, we discover that, we share a surprising amount of sequence.
Similarly, even with things as remote from us as bacteria.
And when you think about it, that sort of makes sense because the, even though,
we're, obviously, very different from bacteria, we do very,
very different things, every living thing on the planet uses DNA as its basic code.
Every living thing on the planet has to do certain things in common because of that.
For example,
every living thing has to copy its DNA in order to make copy of cells.
So bacteria use a very similar mechanism to copy DNA as, as humans,
so we find the genes in bacteria are, in those cases, similar to genes in humans.
[SOUND] And then finally, when we talk about mapping genomes,
we're actually beyond, mapping sometimes refers to just sequencing itself,
capturing the sequence, but once we capture the sequence,
the first thing we do after that is try to figure out where the genes are,
and we'll talk a lot more about that later in the course.
But, genes the word genes has, has changed a lot over the past 50 to 100 years.
Today, we think about, one way to think about it is, is say an inhe,
inheritable unit that is something that you can inherit from your parents.
Another way to think about is, is a small section of the genome that encodes
a protein which in turns does, has some function.
And that's usually what we're talking about and genomics is,
is the parts of the genome that encode proteins or
that encode little bits of sequence that can turn into functional elements.
[SOUND] So here's another definition of genomics that emphasizes not just
the study of genomes themselves but also a little bit about for application.
So why are we interested in genomes at all.
There are many, many applications of genomics, today, and the,
the list is growing, rapidly as, as we get better and better at sequencing, and
as we discover more things that we can do with genomes.
But it certainly includes medicine, many applications, in,
in pharmacy, and if you're not looking at human genomes, agriculture and, and
other areas of science.
So genomics is a relatively new field of biology.
But it's in some in, in many people's minds, it's part of biology.
But let me just emphasize a few of the differences between genomics and
more traditional biology and genetics, which have a big overlap.
So one way you can distinguish genomics from more traditional biology.
Genetics is the, traditionally when we, when we study genomes, or genes,
we studied one at a time.
Not that long ago, back in maybe the 1980s, it was a common,
a common experiment was to, to study one gene and to spend months or
years studying that one gene, or people would spend their whole careers.
You've been studying one gene and writing about that gene, and
trying to figure out what that, what function, what the function
of that gene was in said people or in say, a model version like mouse.
But today, because we can capture the whole genome,
we often look at all the genes at once or at large collections of genes all at once,
so that would be a more genomics way of studying.
The technology has been the real driver of this, so that's a very big differences and
that's really what's allowed genomics to grow the way it has the past 20 years.
In the past, we could only do targeted, what we call low throughput experiments,
studying one gene at a time instead of one organism.
Today, we can do experiments where we simultaneously measure the activity of
thousands of genes all at once using the new genomics technology.
So that's a big difference between genomics and
earlier ways of doing biology.
And then the hard part, where things differ between traditional and
genomics science.
In the past, you still had to be clever, you had to understand biology and
genetics well.
Experiments were not necessarily easy to do.
Might, they might take years.
Today, some of the experiments, you can still do the same kind of experiments,
some of the experiments are easier to do, but we generate so
much data that the data itself is overwhelming.
So, because the technology has gotten very clever, very efficient, and
very expensive, we're now looking at doing experiments where we,
where we measure thousands or tens of thousands of genes all at once,
in multiple tissues, in multiple samples.
And we know going into such experiments that there's probably a lot of
really interesting results that are going to come out.
But when we get the data, we discover that, oh my god, there's so
much data here that it's hard to figure out where the interesting results are.
So we have,
we have to deal with big data problems that we didn't have to deal with before.
We have to deal with,
statistical uncertainty in ways that we didn't have to deal before.
And we have to deal with large-scale computation in ways that were never
necessary for earlier, earlier types of biological studies.
