Welcome to the NCBI minute. You should be seeing a slide that shows introductory stuff
about the webinar. Today we'll be talking about SmartBLAST, a new rapid protein application
tool. With me in the room is Tao Tao, also from blast-help. He will manage things and
answer questions. Tom Madden the lead BLAST developer will join us at the end. If you
have any questions please be sure to enter them in the question pod. We are recording
this webinar. It will be available on out YouTube channel and the Webinars playlist.
If there are any answers, questions that are substantive that everybody would be interested
in, we will have those available on the Materials section that will be on the ftp site and webinars
page. The slides for this particular talk are up
on the NCBI site. You can see that compressed URL listed on the slide, http://1.usa.gov/1EvVxN3.
So the tool I will introduce today is SmartBLAST. It is designed to rapidly provide a name to
an unknown protein as well as provide overall taxonomic context and sequence length context
for the close matches to the most similar proteins in the database, and we will talk
about that in a minute. One thing I want to mention about SmartBLAST,
it's an experimental service. We have a new program at NCBI called PubMed Labs. We are
putting some services out there early in their development. We want to get feedback from
people about how useful they are or how they would like to see them different than they
are right now. So I would think of it as a preliminary tool. Tom Madden has just arrived
so if you have questions for Tom, he is here.
Before we dive into SmartBLAST I want to set the motivation for it. One of the questions
David Lipman asked, he is our director, is what are people doing with BLAST? If you look
at what's going on with the protein BLAST service, we have about 50,000 blastp searches
per day.
If we look at what people are searching with, the queries have very many similar sequences
in the database; 40% have exact matches in the database. If you look at what people are
doing just based on where, on the BLAST results pages, most of them are looking at only the
top 5 matches. That suggests a particular use case for BLAST. And it's one where BLAST
is little too much for the job if you think about it that way. The use case is simply
to identify or find a name for a query sequence. Basically a full blastp search is not necessary.
It's kind of inefficient if that's what you're trying to do. That's the motivation for the
SmartBLAST tool that I will talk about now.
SmartBLAST is particularly good at finding those close matches and giving you some context
for them. That is the URL [blast.ncbi.nlm.nih.gov/smartblast/] directly linked on the BLAST homepage. You
might see the link as an option when you run a blastp search.
It has a couple of things going on. One is the rapid search for close matches. It does
it in two ways. It does a blastp search against a small, a
landmark database of reference species proteomes. You get two matches back from that. I will
come back to that. And the other one is a search against nr, our default protein database,
but it uses a different algorithm, a k-mer algorithm, which is an alignment free approach
that allows you to search nr very quickly. It generates hits to the most similar sequences,
and then to make the alignments we use blastp in blast2sequences to do this.
It also incorporates with the five matches you will get back, a multiple alignment that
incorporates your query sequence and from there we generate the SmartBLAST graphical
display. You will see a protein tree that's going to give you some taxonomic context.
There is a graphical display that shows the full length relationships among the query
and the closest matches in the database.
This is a slide that I stole from Tom Madden. Basically, this is the procedure I just talked
about, you run the fast k-mer search, you run the blastp search against the 27 proteomes,
then we use COBALT to generate the SmartBLAST display.
One thing to notice on this slide is the difference in the sizes of the two databases. Nr has
about 2 times 10 to the 10th residues, while the reference species proteome database is
about 100 times smaller and that's why we can use the BLASTP search there and still
do it fairly quickly.
This is a slide that shows, also stolen from Tom Madden, this nice tree view of organisms
whose proteomes are in this model organism database. We've got a pretty broad taxonomic
spread; it's certainly not comprehensive. But it does hit many of the most popular organisms
in our databases, and for all of these we use annotations for the best assembly for
that organism's genome that we have.
This is the heart of the SmartBLAST display. This is a search that I did with a translation
of a open reading frame from the lychee nut transcriptome. I can quickly pick the protein
sequence that is basically a novel protein sequence as far as the NCBI database is concerned,
we don't have that I generated it. I can quickly find a name for it. It's probably a DNA mismatch
repair protein. The name I would want to take from are the green matches, those of a landmark
genomes. So Arabidopsis would be the one I'd probably want to grab for the name. This is
the mlh1 homolog in this plant. We have a bunch of other plants, and this sets the taxonomic
context and it gives me the conserved domains.
And the graphical overview shows me the full length relationships among those proteins.
There is the name that we can get from the reference database, conserved domain results,
and the multiple alignment used to generate the graphical views that you see.
We also have the traditional BLAST report below it. You can see the three hits from
nr, and the two hits from the landmark database. And you also have additional hits that you
get from the landmark and nr databases.
Okay so I will do one example. I will go now to a web browser. There is the BLAST page
and you see the link to SmartBLAST. If you follow that materials link, you can do this
later on your self. There is a text file there that has some stuff in it. There is more stuff
here than you are going to do in this webinar. What I did was the exact same kind of thing
I did with the lychee nut transcriptome. This is a sequence from the striped bass transcriptome,
that I generated by finding an open reading frame on one of the RNAseq sequences.
In essence this is an unknown protein. It is from a vertebrate so that means it's likely
going to have lots of very close matches in the NR database. If I really just want to
have a good name for this protein, I have a good chance of finding a homolog in, say
human or zebrafish that would tell me something about what this protein is.
I might be able to find some other fish species that have this homolog in there. I will go
ahead and do this is as a live search. I will grab the whole sequence exact except for the
asterisk that represents a stop codon. I will copy that and then go to the BLAST
homepage. I will click on SmartBLAST to get to it. And I can just put my protein sequence
in there. What I want you to see, and one of the beauty
part of this is how quickly this happens. This is practically instantaneous.
And so right away I've got a result. It tells me pretty much what I need to know. When I
look at the graphical view, I've basically got the whole thing. It happens to be a protein
I would expect to find in any vertebrate genome. This is the cystic fibrosis transmembrane
conductance regulator. I can grab the name from either the zebrafish or the human, the
protein shown there. It's also cool because it shows me taxonomic context for the fish
species I am working with.
Gratifyingly, this works out just right because the closest protein in that tree on the left
side is from a fish in the same family, the European seabass. And these are fish in a
different family but all four of them are in the same group of dishes. The zebrafish
is in a different group and you can see it is a more distantly related protein.
I can generate a full alignment by clicking here. That opens a new tab.
There are the five sequences including my query, which is at the top there. And this
is the COBALT display that you may or may not be familiar with. For myself I like to
see the Expanded view and I like to change this to Identity. This gives me some... so
I can see what the differences are. This is a full-blown multiple sequence alignment.
So you have a full-length appreciation of how these proteins are related to each other.
Which is different than what BLASTP gives you. You can generate COBALT from a blastp
search, but this is a handy way to do this that gives you a nice concise view of everything.
And then down below I have the five hits that I get. Two green ones here, which are the
more are distantly related ones from the landmark database and then the three here from the
other fish species that we saw in the tree. And then these are the additional BLAST hits
we saved from both the landmark database search and the NR search.
And you have the traditional BLAST results down here if you want to see them.
The last thing I want to mention before we close, is that SmartBLAST is part of the PubMed
Labs feature of NCBI, so it is under rapid development and we want to hear from you and
if you will click on this, "Please let us know what you think", that sends a feedback
form to the development team, and they can take your suggestions into considerations
for making this more useful. I have one slide left.
Just to mention PubMed Labs in general, if you want to know more about this. These are
experimental services where we are requesting input early in the development cycle. We have
a blog that you may know about, called NCBI Insights. There's a nice article about PubMed
Labs and also an article there about SmartBLAST. So I will stop talking now and see if there
are any questions that have been written in. I will leave the webinar open for a few minutes
in case anybody wants to ask questions.
I see one question in here that I think I can answer. What if the query sequence is
much shorter, I guess he means than the database sequences. What you basically see is that
in the graphical view, you are still going to try to align the whole thing, the parts
of the other proteins that don't align quite as well as well will be shown. You will have
a gap.
Tao says to tell you that insulin works, as a short query.
Remember also SmartBLAST is not incredibly sensitive. If you start really using short
sequences, it will not give you a lot of results. I also want to point out that if you want
to mess around with some of the examples I have in there, it's not uncommon for you to
put a sequence in SmartBLAST and get nothing back. Remember SmartBLAST was designed to
find very similar sequences. There are plenty of sequences out there in the universe that
have nothing similar in the NCBI databases. For those kinds of sequence, a lot of times
things from metagenomes, you will have to do a full-blown BLASTP search.
So Tom, if anybody has any specific requests about SmartBLAST they should go ahead and
use the feedback link. If there's something you want to request about SmartBLAST, please
use the feedback link and tells about it .
So, I have a question, what is the most distant hit it can find. I don't quite know how to
answer that. Tom will take a stab at this one.
[Tom] As Peter was saying, SmartBLAST is doing two different kinds of searches. One is the
kmer search which is in alignment free algorithm . We find that does pretty well down to 70%
or so. It will find the close matches. If you go to run a blastp search you will get
a lot of stuff that's above 70%. The other part is that a standard BLASTP search is as
sensitive as any other blastp search. So, searching the whole of nr you can go down
to about 70%; searching the 27 proteomes, it's kind of a standard blastp.
[Peter] Right, and for those, you can see pretty distant things there. It is quite common,
that I observe that you can find things where the only matches you get are to the reference
sequences.
[Tom] So, yes, that would be possible, and I guess what Peter calls the landmarks, it
gives you landmarks of proteomes that might be fairly distant from your query.
So this is a question ... it would be nice if the colored bars showed some representation
of similarity of the query to the matches. Again I think that qualifies as a request.
Write it up and send it to us. We will take a look at it. That way we have a record and
we can take a look and see how feasible it is.
I think Wayne's question is legitimate. This is a plant because Wayne is actually one of
us. He wants to know, do you get in an RID. And, yes you do get an RID, but currently
we don't have any way for you to save it. [Tom] And so basically it is easier to rerun
it. There is an RID around, but I'd just say go ahead and rerun it. This is part of PubMed
Labs and things could change pretty quickly. We don't want to make any commitment as to
how to deal with the RID right now.
[Peter] These searches are so fast and the only thing you can do is enter one query,
so there's not a whole lot of advantage to sending us an RID to ask a question about
something. Send us what the query was and we can find
out what happened.
And of course the question someone just asked, is there a command line program, and the answer
is no, not right now. I don't know if there are any plans to do that.
[Tom] No, not right now. So it would be very complicated to make it
into a stand alone program. I think we will go ahead and wrap it up. Thank
you all for coming. We have several questions that might be worth compiling. We will go
ahead and put those in a document. I will go ahead and stop talking now. Thank you all
for coming.