My name is Tom Madden, I'm the team lead
for the BLAST group at the NCBI.
I'm going to tell you about using BLAST.
I'm going to start with BLASTing into Genomic data sets
at the NCBI website.
First, I'd like to thank Ben for inviting me to participate
in this course.
I'd also like to thank Peter Cooper, Tao Tao
and Wayne Matten for providing many of the examples
I'm using here.
I assume that most people are familiar with the BLAST
website but it's okay if you are not.
You can learn on the job.
First I'm gonna point my browser at
blast.ncbi.nlm.nih.gov
and you'll get the page
that you see right on your screen right here.
At the top you see Genomes and there's a number
of genomes here, Human, Mouse and such
and there's also a search box to find a genomic
data set for any organism not listed here.
We're gonna come back to that in a little bit.
Second, there are links for traditional types of BLAST
down below and finally, there are specialized BLAST links
down at the bottom.
These links have to do with some BLAST variants
or alignments.
Now, back to the top of the page.
I'd like to search the genomic data set at the NCBI
for Hydra vulgaris.
All I have to do is start typing, Hydra.
And you see that I get a number of choices
and so I can select one, and so I'm gonna look for
Hydra vulgaris and I can simply select it here.
And then I type go.
And the idea here is that this takes me
to the best genomic set for Hydra vulgaris.
So I have it right here.
By the way, Hydra vulgaris can regenerate it's head,
which I think would be a really awesome superpower.
Anyway, now that I'm at the search page,
I'd like to point out a few features.
First, note that the menu has preselected the genome here.
I can select the question mark and see some information
on the assembly that I'll be searching.
So now what I'll do is I'm going to paste in an identifier
from a TSA project and TSA means
Transcriptome Shotgun Assembly.
And so let me grab that identifier here
and I'm going to simply paste it in here.
And so then I can just scroll down to the bottom
of the page, and I can hit BLAST.
So this should actually run pretty quickly
because it's a relatively small data set.
Okay, so that didn't take too long and I can see
that I have matches to database sequences.
We can see from the descriptions down here
that these are unplaced, genomic scaffolds.
Let's take a quick look at the graphic up here.
One of the database sequences is quite a bit longer,
or one of the matches is quite a bit longer,
and we see some vertical lines in the middle there,
there's a vertical line there, there's another one.
And these indicate missing sequence.
Since a query here with an mRNA
and we're searching genomic sequences,
I assume these are introns.
Okay, so what I've done here is to recap,
is I've found a genomic assembly for Hydra vulgaris
from the BLAST homepage and I've searched it.
Okay, so this genomic assembly was preliminary
but at least there was some assembly.
It's a little different in some cases.
For example, if I wanna search another member
of the Hydra genus, there might be less data.
Say I wanna look at Hydra oligactis.
So in this case, the SRA data might be your best option.
There are a number of ways to get to the data.
but I'm gonna start with a BLAST page,
which you see in front of yourself.
And so I'll got to the main BLAST page,
and I'll select nucleotide blast here.
And then what I'm going to do is change the menu
to very bottom where it says Sequence Read Archive.
I see that there's a box to look for a SRA accession
or organism.
So I'll start typing here, Hydra oligactis,
so that's all I need to type and so I see two choices
I'm gonna take the first one here.
And then I'm gonna do the plus sign here
and do this again, Hydra oli
and I'm also gonna select the second one.
Now, okay so then I'll put in the same query I had before.
I'm just gonna paste in that accession from the TSA.
And I'm going to scroll down and hit BLAST.
I get some results back but rather than look at those,
I'd like to move on to looking at some human data with you.
This is Tom Madden again, and we'll be looking
at running stand-alone BLAST against SRA runs
in this part of the course.
First I'd like to mention that there are a lot
of helpful fact sheets on the NCBI ftp site.
And they're on the ftp site under pub/factsheets.
Here we see the fact sheet about setting up local SRA BLAST.
You'll find this one handy if you wanna set up SRA BLAST
on your own.
So this next search involves looking
at some metagenomic data for the human gut.
And so I'm going to start with BioSample.
I've entered digestive decontamination
as a query to BioSample.
So what I'll do is I'll run the search and once I do that,
I see there are five samples from one person.
They are all at different points in time and place.
At the top, I see 313 days at home.
And at the bottom I see four days in ICU.
In between, are 14 days, 16 days and 28 days.
I'm going to examine 16 days in ICU and at home.
I'll use the SRA links here.
And looking at the SRA page,
pages I can see the run accessions.
So that's a run accession for the 16 days ICU.
And the one for home I can find here.
Also, again the accessorary link.
And there's the SRA accession.
As I've mentioned, we're going to work on stand-alone
BLAST right now.
To do this, I've started an instance at Amazon Web Services
or AWS and I've installed a Linux SRA toolkit binary there.
So this is my instance at AWS.
And by the way, I'd like to mention that we do have
SRA binaries for PC's and MAC's so you could try searches
on your desktop.
Again, the fact sheet I mentioned earlier
could help with this.
I've also copied over a query that is supposed to help
with identifying antibiotic resistance and here it is.
For fun, you may want to try and find a different sequence
to use as a query here.
I'm gonna run these searches with some parameters I saved
to a file a bit ago.
So let me copy over the first one here.
And so a few comments on these parameters.
The database name is actually the run accession.
This is the query file I showed you before.
I've set it the max target sequences to 5,000.
That's 5,000 on different runs and I'm gonna get results for
This outfmt 7 here gives me tabular output.
I've set the evalue here, the expect value, to 1.0-6
so that I'm only looking for very significant matches.
And the output will be saved as file home313.tab
So let me get this one going here.
And while that's running I'm also gonna start the second one
So, again, the run accession is the name of the database
and 5,000 target sequences
and very significant expect value.
Okay, the searches are done.
Right now, I'll just check the number of lines in each file.
There are 68 lines in the 313 day home file
and 576 lines in the 16 day ICU file.
This looks like an interesting result about antibiotic
resistance but I'd call it preliminary
and there are any number of caveats here.
One is that I might need to adjust for the different sizes
of the search sets.
I checked, and they are both about the same size.
Another question is whether we're simply looking
at the amount of bacteria or these in the sample.
To check that, I'm going to run another search using the
16S sequence.
I tried this and I know there are lots of matches.
And so since I know there are a lot of matches, I'm setting
the max target sequences to 50,000 for this search.
And all the other parameters are the same.
And so that was the 313 day homes runs.
And this is for the 16 day ICU runs.
And also, again, I've set the max target sequences to 50,000
Now, actually, I'm pretty sure you could not do this
on the NCBI website.
It's a shared resource, so it'll limit the total CPU
one process can take.
Even if it ran you might have a problem
downloading the results.
The good news with stand-alone BLAST
is that you can abuse your system
as much as you want or your administrator permits you to.
Okay, these results are done, so let's take a look.
So what I'm gonna do, is I'm just gonna count the number
of lines in the output file.
And we see that the 16S 16 day results
have about twice as many as the 313 day at home results.
Now the 16 day antibiotic marker had about nine times
as many results as the 313 day, so maybe there's still
something here but it's not really clear-cut.
Anyway, it does look like an interesting data set
and this shows you what kind of things you can do
with SRA BLAST.