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生物信息学方法 I

多伦多大学

4.7（739 個評分） | 69K 名學生已註冊

Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on. Topics covered include multiple sequence alignments, phylogenetics, gene expression data analysis, and protein interaction networks, in two separate parts. The first part, Bioinformatic Methods I (this one), deals with databases, Blast, multiple sequence alignments, phylogenetics, selection analysis and metagenomics. The second part, Bioinformatic Methods II, covers motif searching, protein-protein interactions, structural bioinformatics, gene expression data analysis, and cis-element predictions. This pair of courses is useful to any student considering graduate school in the biological sciences, as well as students considering molecular medicine. Both provide an overview of the many different bioinformatic tools that are out there. These courses are based on one taught at the University of Toronto to upper-level undergraduates who have some understanding of basic molecular biology. If you're not familiar with this, something like https://learn.saylor.org/course/bio101 might be helpful. No programming is required for this course. Bioinformatic Methods I is regularly updated, and was last updated for January 2019.

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Genetic Analysis, Bioinformatics Analysis, Evolution, Comparative Genomics

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4.7（739 個評分）

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RG

Nov 05, 2017

I enjoyed doing the course. It is exciting to see how much one can learn from a few gene or protein sequences. Thank you for making the course understandable to a beginner in bioinformatics!

PS

Jul 09, 2017

A fully recommended course for those who wish to understand the basics and strengthen their skills in Bioinformatics. A well-designed and precise course. Thank you Dr. Nicholas Provart. :-)

從本節課中

Selection Analysis

In this module we'll take a set of orthologous sequences from bacteria and use DataMonkey to analyze them for the presence of certain sites under positive, negative or neutral selection. Such an analysis can help understand the biology of a set of protein coding sequences by identifying residues that might be important for biological function (those residues under negative selection) or those that might be involved in response to external influences, such as drugs, pathogens or other factors (residues under positive selection).

Introduction2:04

教學方

Nicholas James Provart
Professor

腳本

[MUSIC]

Alright.

In this week's lab, we'll be doing selection analysis, which

attempts to identify if natural selection is acting on DNA sequences.

And, if in fact natural selection is acting, is

the selection in a positive direction or a negative direction?

And that means is the selection for or against a site.

And we're also trying to address how the

sequences are changing because of natural selection.

So there are three main flavors of natural selection.

Positive or Darwinian selection occurs when a beneficial

mutation arises in a population and increases in frequency.

So an example of this would be

antibiotic resistance rising in a bacterial population.

And that would quickly spread throughout the population.

So that bacteria, if

someone's being treated with an antibiotic,

all the bacteria would quickly acquire that mutation.

The other kind of natural selection is negative,

or purifying selection, which is the opposite of positive selection.

And this occurs when a detrimental mutation is selected out of a population.

A detrimental mutation is typically anything that

would inactivate a protein or an enzyme.

And these kinds of mutations quickly

become eliminated from a population.

The third flavor of natural

selection is balancing or diversifying selection.

And this is a kind of selection that favors

the maintenance of genetic variation at a locus.

You can imagine that this kind of selection might occur in a

population that is subject to or exists in different environmental

niches, where one allele might be

advantageous under one condition

and the a certain other allele might

be advantageous under another set of conditions.

How do we measure selection?

There are two commonly used measures: Tajima's D and the dN/dS test.

And there are many ways to quantify genetic variation.

Typically population geneticists focus on two metrics,

those being theta, which is based on the number of segregating or variable

sites in sample, and pi, which is based on the average number of differences

between all pairwise combinations of sequences.

This is also called pairwise nucleotide diversity.

So, both theta and pi measure variation.

Pi is more sensitive to the frequency of

genetic variants, while theta considers all variants equal.

Regardless of whether they're only found in one sequence, or half

of all of the sequences.

And pi takes these differences into account.

So this is just a cartoon of what I just said.

Basically we can have rare polymorphisms occurring just in one sequence.

We can have frequent polymorphisms occurring in

several of the sequences from a population.

And

Theta just counts the number of polymorphims

across the sequences, whereas pi measures the pairwise

difference between all possible pairwise comparisons of the sequences.

Theta is the number of

segregating sites and pi is the nucleotide diversity.

So natural selection acts directly on mutations that change

fitness, so this typically manifests at the protein level.

But genetic regions that are

surrounding a selected site are also influenced.

And the reason for this is that genetic regions surrounding

a site are dragged along, due to genetic hitchhiking.

And that, the size of the piece that hitchhikes along, is dependent on

the rate of recombination. So most of the genetic variation in

the surrounding regions is neutral.

So these are third position substitutions in the codon.

We'll talk about this in a little bit. That don't change the coding sequence.

But we can still see signs of selection due to linkage to to a selected

polymorphism. Tajima's D looks at theta and

pi and asks which one predominates. So if a gene is,

is neutrally evolving where there's no selection acting,

theta would be roughly equal to pi. In the case of positive selection

what happens is the genetic variation is swept from a population.

This is called a selective sweep. Very little genetic variation

will remain in a population after a beneficial mutation spreads through it.

But then new genetic variation will begin to accumulate and

this new genetic variation is accumulating in a homogeneous background,

and therefore most mutations will be at a low

frequency and in this case, theta is greater than pi.

And in the case of balancing selection, what is

happening here is that we're retaining alleles, genetic variation,

longer than would be expected. So, the allele is neither...

it doesn't sweep through the population or it's not eliminated from the population.

And since that's the case, those

alleles will arise to intermediate frequencies.

In this case, theta is less than pi.

So, this is just a cartoon again of what I was

just saying.

basically, if the genes are neutrally

evolving our theta is roughly equal to pi.

And our Tajima's D is approximately zero.

If there has been a selective sweep

due to positive selection, what we'll see is a lot of accumulation of

the rare variants in members of the population at a given locus.

And in this case theta is greater than pi and D

is less than zero. And if alleles have been retained for longer period

We'll actually see accumulation of variation in

multiple branches of the locus and theta in this

case is less than pi and D is greater than zero.

So, there are some caveats when using Tajima's D. You can see

that it could be a powerful statistic for detecting seleciton.

But it can be fooled by a couple of factors.

One of these is the population having

gone through a bottle neck, and this bottle neck

would very much resemble the case of positive selection.

So it's very difficult to distinguish between

a population having gone through a bottleneck versus positive selection.

And the other issue could be if

the locus is in a region of

of low recombination.

So basically there are some other tests that we need to apply,

to look at the population substructure with other tools

in order to be able to interpret Tajima's D properly.

The other kind of test we can do for natural

selection is to use the dN by dS ratio test.

So this is also known as the Ka by Ks ratio test.

And its perhaps the most widely used method for detecting the

pattern, the kind of natural selection

that is happening from nucleotide sequence data.

And its quite useful because we can actually infer selection

all the way down to the codon to ask the question

whether or not there are specific sites that are being selected for.

In order to be able to understand the dN by

dS test, we need to

understand non-synonymous vs synonymous nucleotide changes.

Now, non-synonymous substitutions result in a change in the protein sequence.

Synonymous substitutions change the DNA sequence, but not

the protein sequence

due to the degeneracy of the genetic code.

So if we look at our codons, The codons for amino acids,

We can see that for instance, in the case of leucine, there are actually six

codons that encode leucon leucine. TTA, TTG

TTT and so on. And typically it's the third position

(sometimes the second position) that can change without

changing the amino acid that's being coded for.

And typically the first position of codon

is the one that causes a non-synonymous change.

The dN by dS ratio test actually calculates the

ratio of the rate of non-synonymous substitutions, which is dN

(the number of non-synonymous substitutions per non-synonymous site, so

we look at all possible non-synonymous sites)

To the rate of synonymous substitutions, dS, which is

the number of synonymous

substitutions per possible synonymous site.

Now synonymous subsutitutions are not

exposed to strong selective pressures because

they don't result in a change to the protein sequence.

And typically selective pressure acts at the level

of the proteins, which are the manifestation of the phenotype

that arises from the DNA sequence,

and thus they accumulate at roughly a constant rate.

So, we can use this, the rate of

synonymous substitions as a baseline

to compare the substitutions that do change

the protein sequence, which are the non-synonymous substitutions.

So when we do a dN by a dS ratio

test, it's critical to have our DNA actually

broken up into codons and aligned by codons.

And we talked about this when we were doing

earlier labs. We can actually translate our sequence from DNA

into proteins and then do the alighnment at the protein level.

And then backtranslate into the DNA sequence.

And once we've got the DNA sequence in codon-based alignment, then we can

do the dN by dS ratio test with those data.

So how do we interpret the dN by dS ratio? In the case of a completely neutral

sequence it would be free to change with no constraints.

You would expect the dN to be the same as dS, so the

rate of non-synonymous substitutions to be the

same as the rate of synonymous substitutions.

In this case, the ratio of dN by dS would be equal to one.

And that's also

shown over here in this cartoon.

Ka by Ks would be equal to one.

When there are selective constraints on a sequence such that there's

negative selection you'd expect fewer

substitutions that changed the protein sequence.

So we would have a lower dN number, and therefore dN by dS would be less than one.

So that's shown over here, our Ka by Ks would be down

here, closer to zero.

And in the case of positive selection, we'd expect to see a greater proportion

of amino acid substitutions in the

population because they're being increased by positive selection.

So we would have a higher number of non-synonymous substitutions,

relative number of non-synonymous substitions,

therefore our dN by dS ratio would be greater

than one.

Now, it's important to keep in mind that most functional

genes, protein coding genes are under some level of selection constraint

so

the dN by dS ratios are typically well below one

for coding regions. Certain sites within those sequences,

however, can be under positive selection. So this figure over here to the right just

shows mouse-rat orthologous genes plotted in bins

according to their Ka by Ks ratio so Ka by

Ks ratio of one is over here, and it goes down to .05 on this side.

We see that by far the greatest

number of different values for Ka by Ks falls

into this bin here with a strongly small

kN/kS or dN/dS ratio.

So how can we use this information to study the

world around us? Well, basically evolution is an

arms race. And certainly a lot of the

drugs that we take are applying selective pressure onto

bacteria or viruses that infect us.

Or the plants that we like to eat.

And this is of concern. Certainly

changes to viral DNA sequences can happen overtime.

Changes to bacterial DNA sequences can happen overtime.

Simply through new mutations that arise.

And then these mutations, if they are beneficial, can spread to other

members of the population and could lead to outbreaks.

So for instance, this paper here actually

models the effect of anti-retroviral drugs.

On HIV and shows that certainly

some of the strains of HIV that are now resistant to

anti-retroviral drugs could pose a threat as

to cause a new epidemic which would be not

good. And so perhaps we can use this information

to actually understand the kind of selective pressure that's being applied

to DNA sequences. And to get a better understanding of how

we can perhaps modify drugs so that they

can still work against an evolved strain of HIV, for instance.

And here's an example with an HIV-1 protease using the SELECTON server of

the Tel Aviv University.

And basically the HIV-one protease is an essential enzyme for viral replication.

And several drugs target it. One of these is Ritonavir

and what the authors/creators of this tool, the SELECTON server,

did, Pupko and colleagues, they took 70

HIV-1 protease gene sequences from Ritonavir-treated HIV-1

patients extracted from a database, the Stanford HIV Drug Resistance Database.

And what they did was, they then fed those sequences, along with the PDB record (the

tertiary structure of the protease bound to Ritonavir) into the SELECTON server.

And what the SELECTON server

does is actually examine on a

residue by residue basis, whether or not the sequence of

the HIV protease is under positive selection or purifying selection.

Positively selected residues in this

case are shown in this orangey color.

And negatively selected residues or

those residues under purifying selection are in this purple color.

When we map that onto the 3D

structure of HIV-1 protease complexed with Ritonavir

we can see quite nicely actually that residues that are in the area

where the Ritonavir binds are actually being positively

selected for

So they're selected to change from something that they were

before, perhaps so that this Ritonavir can't bind as well

in the patients that are now resistant to Ritonavir.

So, by understanding which residues are being acted upon, you can actually start

to think about how evolution is acting and perhaps modify

the drug so that it could work in the cases where

it currently doesn't work.

So another site specific dN by dS analysis

comes from Professor Guttman who's a co-author on this course.

And his interests is in plant pathogen

interactions and plants are pretty interesting.

They do have actually have an immune system.

There is,

there is an innate immune system that basically

prevents most bacteria... the vast majority of bacteria actually

can't infect plants and this innate immune system is triggered

by molecular patterns, called MAMPs (Microbe-Associated Molecular Patterns).

These molecular patterns are found on structures like peptidoglycan cell

wall of bacteria, or the flagella of bacteria.

And these trigger an innate immunity.

This is commonly callose deposition,

an increase in a kind of cell wall component, and it makes it difficult for

those particular bacteria to enter the plant cell and attack it.

Sometimes the bacteria, however, have evolved mechanisms that

actually shut down this innate immunity. By

injecting components through a secretion

apparatus, effectors that actually block this innate immunity, and

plants then in turn have evolved mechanisms

to actually shut down the bacterial mechanisms.

And then the arms race continues.

These other bacteria have in turn acquired

new mechanisms for blocking this compensation mechanism here.

So it's really, it really is an evolutionary arms race.

And we can, we can start

to use sequences across many bacterial varieties, different strains of bacteria

and ask the question, are there any particular sites that are under selection,

positive selection, which might indicate that those sequences

are being forced to change by the action of

plant defense systems?

And there's a very nice paper published a couple years ago

by Honour McCann, one of Dave Guttman's grad students, and Professor Guttman,

where they took the genomes at three Peudomonas

syringae (a plant pathogen), and three Xanthomonas campestris

pathovars, scanned the 1322 orthologous core genes

for evidence of positive selection, and came up with several candidates.

So what we're looking at here

are four genes,

and the signatures of positive selection along those genes.

So the line, the red line here is a Ka by Ks ratio of one.

Anything above that line indicates a positively selected residue or region.

And anything below that line indicates a negatively-selected region,

or a region under purifying selection.

And as you can see as I mentioned before, for the rat/mouse orthologs, many of

the residues are indeed under purifying selection,

as you would expect for proteins that are

important for certain core functions: DNA processing protein,

an Elongation Factor Tu, the flagellan (you need

to have the flagellan to be able to move around).

But, however, there are some residues that do seem to be under positive selection.

And what McCann and her co-workers did is they

took those residues and actually asked the question, are

those residues being detected by the plant and are

they able to initiate this kind of innate immune response?

And if that's the case then you could imagine that those things would be

under positive selection to change, to

actually reduce the innate immune response.

And indeed, several of the peptides that

they chose from this positive selection analysis

were able to initiate a positive response in the innate immune assay that they used, which

is accumulation of callose, and which you can see here in these panels here.

So, this is a callose deposition that

we're seeing, it's measured

in the graph below.

So in today's lab what we'll be doing is examining one of the components that helps to

create this particular structure that allows bacteria to insert

a set of proteins that can actually subvert the innate immune response.

It's not clear whether or not parts of this should

be under positive selection but one suspects there might be and

we'll be examining that.

Now in order to be able to do this kind of analysis

we do need to first perform a phylogenetic analysis, and to

also of course have our sequences aligned as per their codons.

We also need some evolutionary models that

we touched on briefly in the phylogenetics lecture.

And here we will let a tool call Data Monkey help us choose the correct

model and we will also upload our phylogenetic information to this tool,

Data Monkey, in order to be able to do the positive selection analysis.

So, I hope you enjoyed today's lab, and really it's it's becoming a

bit of detective game here, trying to figure out what's going on in biology.

And the

availability of large numbers of sequences

is really allowing this detective game to become easier.

[MUSIC]

[SOUND] Hope you enjoyed the lab. [SOUND]

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