Protein Association Network Analysis Using STRING (Part 2)

It’s going to take me to
another evidence page. And it’s going to show how the
paper, or the abstract, or the actual text, they are
co-mentioned. So this is the paper where
those two genes are mentioned together. So that’s the reason
that association has been pulled up. I’m going to go back to
the network page. And I wanted to show you a
couple of other options. One option that I want to show
is the interactivity. Now even if we’re going
to the interactivity, let’s come down here. You see here, that they are
a bunch of nodes that are connected, a bunch of proteins
that are associated. But there are still a number
of proteins that are not connected here. So what you can do is add a few
more nodes, or proteins to this network, and
see if they come together or they get connected. So I’m going to add
here this color. I’m adding additional
white nodes. White nodes– meaning, that
proteins that are not actually in your import, but
they are extra. And if there is– it will add
extra nodes in order to maximize connections
along your import. So I’m going to add two
proteins, two nodes, and update the parameters. It’s going to add
two white nodes. Let’s see if many other
proteins of ours come into that. So here we added two white
nodes, as you see here. As you see here, adding this
white node just an additional this white node brought three
different proteins that were previously not connected
together. So that looks like an
intermediate here. That could be a middle man
there associating and establishing that association
among those three proteins. So that’s how you do that. You can keep on exploding. You can keep on adding
a couple of nodes and see how it works. So that’s one kind of
exploration that you can do. Another thing that I want to
show is interactivity. You see this little option
here that says Advanced. You click the button Advanced. It’s going to take me to another
page where it gives me options to do stuff. For example here, I can
do some clusterings– Kmeans clusterings,
MCL clusterings. You can also look for if there
is any enrichments. One of the things that I want
to show you is the layout. So you can go there. If you have really messed up–
a lot of proteins and associations, you can go there
and start a relaxation. It will try to fit in. It will try to adjust. It will try to show
you a nice layout. So if you’re done, you
can go stop it. Those are small network stuff
that you can do here in [INAUDIBLE]. Like I mentioned, STRING
is not meant to do network analysis. It’s just a database
of associations. But you can still do the
small things here. If your interests are in
exploring this network– let’s say that, if you keep
adding nodes, and you keep reducing the conference, what’s
going to happen is after a while, this network
might get messed up. For example, let’s
come up here. And let me just ten more nodes
and update these parameters. So if I add 10 more nodes and
update the parameters, let’s see what happens here. OK. Now you see a lot more nodes
coming together. But there are a lot more
connections here, a lot more associations. This connection becomes too many
to handle in the web, in the internet. Then what you could do is take
this data out of STRING. And then you could put it into
a specialized network, visualization and analysis
tool, like Cytoscape. So how do we do that? You can save this data, what all
you see here, in several different formats. So if you want to do that, what
you do is, you go here. Let’s turn off the
Advanced option. So I just click that. It comes off the Advanced
option. Clicking that Advanced
button turns it off. And if you scroll down,
the regular option– not the Advanced option–
you see the Save button. So you click that Save button. STRING gives you options to
save the data that you saw before– that network data
that you saw before in a variety of formats. You can save the image
that you saw before. You can save it in a low
resolution, high resolution. You can save it in
the SVG format. You can save the evidences
alone. You can save the confidence
code alone. And you can save the network
data in XML format, in a graph layout, and in a couple of
other formatting options. One of the most common way
to export this data, or save this data– especially if you want to import
this into Cytoscape is this tab delimited
text format. So just click on that tab
delimited text format. So this is the data that STRING
is going to give you. All it has is the node– node one, node two– and then some IDs. And then all the individual
scores from all these individual resources. And then you’re going to also
add the combined score. So this is the output that
STRING gives you as a text format data. You can take this. You can put it in a table. Or you can import it into
software like Cytoscape and the wonderful further
analysis. So this is the basic stuff that
I wanted to talk to you today about. Like I mentioned, there
are a number of other protein-protein interaction
or association databases out there. But everyone of them has its
own benefits and issues. But the nicest thing about
STRING, like I mentioned, it has the data– interaction data– for a large variety of
organisms, different domains of biology, starting from
prokaryotes, eukaryotes– if you’re working on
any organism– like a plasmodium– you could always hope to get
some information from STRING. That’s one of the
nicest things. The other nicest thing is, it
let’s you export data in a variety of formats. So you can combine this data
with other databases, other resources and you can come
up with comprehensive– All right, so that’s all I
wanted to talk to you today about STRING. I hope you will enjoy STRING and
get something out of that. Thank you so much.

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3 thoughts on “Protein Association Network Analysis Using STRING (Part 2)

  1. hi in this tool there when I start searching I was confusing because it ask me for protein but in
    fact it cannot recognize the protein it only recognize the gene

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