Okay, we're going to get started with today's NCBI minute.
Thank you for coming today.
Let me just put in a plug for our YouTube channel.
If you subscribe to our channel, you can get updates of all the
videos we put up on YouTube, including other tutorials.
In terms of the format of this webinar, our speaker today, Kim
Pruitt, is going to be showing you some slides about
updated prokaryote genome annotation.
If you have questions during that, go ahead and put those
into the question pod.
Some of those could be answered while Kim is talking.
Others we may answer when she is finished with the slides.
Kim will answer those.
And others yet, we will put in a document on the "Courses and
Webinars" page at NCBI under "Materials" where you can see a
log, more or less, of the questions and answers.
These slides today are already up on this materials link at the
bottom of the slide you're seeing right now.
That goes to the NCBI FTP site.
So without further ado, I'll introduce Kim Pruitt to talk
about updates to prokaryotic genome annotation.
Okay, thank you everybody.
I'm Kim Pruitt, and I'm the person who is in charge of
RefSeq karyotes and prokaryotes.
And if you're not familiar with RefSeq, in a nut shell, it is an
NCBI project to provide reference sequence standards
that incorporate current knowledge for genomes,
transcripts, and proteins.
It's a very large dataset, but today I'm going to talk to you
about some of the new features in our prokaryotic genomes.
So just to get started, I want to give you a sense of how we
create our genomic records.
These are made basically from the submitted data that's coming
into GenBank or our partner databases in the INSDC, which is
the International Nucleotide Sequence Database Consortium, or
Collaboration.
So you as the submitter community do a lot of work to
generate genome sequence data and to assemble that
into the genome sequence that is submitted to NCBI.
In some cases you're annotating that data yourselves,
or you're taking advantage of NCBI's genome annotation service
for prokaryotic genomes.
All of that information gets submitted to GenBank, and it's
available in our nucleotide and protein resources.
Sometimes the raw data is available in SRA.
The information about the assembly itself is available in
NCBI's assembly resource, and metadata about the sample that
was used and about the specific research project are available
also in our metadata databases.
The sequence data and the assembly information is then
copied into the RefSeq project, where we are then carrying out a
whole genome annotation process.
This is something that has changed recently.
In the prokaryotic world we used to propagate annotation from the
submitted GenBank entry, and only annotate those that did not
have any annotations, but now we are annotating all prokaryotic
genomes using our annotation pipeline.
This offers us a lot of improvement in terms of
consistency of the annotation, and in terms of being able to
update them in a more timely fashion, because some
submissions come in and they're never updated further to reflect
new information, and so in our pipeline we can do those kind of
updates.
So I'm not going to go into the details about our pipeline on
the previous slide that I'm sorry I moved away from too
quickly.
There was a link to find more documentation about the pipeline
process flow itself.
So our management of the proteins that we annotate on
prokaryotic records has changed significantly in the past couple
of years.
The original model that we were using was that for any given
assembled genome, and so the accession prefix is on the top
are two of the accession prefixes that we use for genomic
sequence records.
For any given genome, we used to provide a distinct accession
number, which I'm showing here with a YP accession prefix, for
each annotated coding region on that genomic sequence.
So the little pinkish boxes are coding regions, and the
individual accessions would be applied per genome.
So if you had two isolates of
E. coli from two patients in a hospital, we would be providing
separate accession numbers for both of those, so there was no
way to correlate easily that the sequences were actually
identical at the protein level, and we were having a huge
increase in redundancy in the RefSeq dataset.
And so we undertook a massive shift in our data model to
address the redundancy issue.
We went through an intermediate stage where we had our original
accession numbers cross linking to our new truly non-redundant
RefSeq protein dataset, and these currently have an
accession number prefix that begins with a WP.
So if you see an accession, WP, followed by underscore, you know
you're looking at a truly non-redundant RefSeq protein
record.
Our current model, which is the completion of our transition in
our protein management, is that all of our bacterial and
archival records are directly annotated with the non-redundant
protein record.
So what this means is, for a given CDS, let's say the first
one on my picture here, if this protein is identically found in
five other genomes from the same or different species, then we
annotate all six of those genomes with the exact same
accession number, so there will be six genomes that are
providing cross-references to this identical protein sequence.
So we've significantly reduced the redundancy in our protein datasets.
You can see some of the actual measures of that redundancy in
our documentation page.
The link is shown at the bottom of this page, where you just
plug in the NCBI root URL here.
So this offers us some advantage in terms of redundancy
management, but it also has had a very positive secondary
benefit in giving us a really good way to manage protein
names.
We can now manage protein names directly on this level of
non-redundant protein records without having to re-annotate
the genome.
We can update the protein record to offer an improved protein
name, and that improved protein name is automatically propagated
to the Genome set that protein is annotated on, and this is a
really strong advantage to this data model.
Along the way, we've also added some categorizations to RefSeq
prokaryotes, so we now categorize them as reference
genomes.
We have a small number of reference genomes.
These are the highest quality complete genomes that are
supported by curation, either at NCBI by NCBI staff scientists,
or through communities that we collaborate with.
So there's greater confidence in the sequence of these genomes
and in the annotation on these genomes, and I'm just giving you
an example.
Here in my examples, in all three cases here, the first
identifier is to NCBI's assembly database where you can get
metadata about the quality of the assembly.
The second identifier is the RefSeq genome accession number,
and then followed by the species name.
We also have a category called "representative genomes," and
what we're trying to do is select a representative per
species.
Most of these representatives are high quality complete
genomes, but some of these are draft, and what we're trying to
do is select the best genome available at this point in time.
These are subject to change over time as we get more genome
sequencing data.
And for lack of a better word, we're currently calling the
remainder our "variant genome." The majority of these represent
bacterial species for which there have been thousands and
thousands of sequences submitted for the same organism.
So we may have thousands of
E. coli genome submissions, thousands of salmonella genome
submissions, and these are really defining variants of the
species.
You can find information about these categories referenced or
representative by searching our assembly resource, and here
is an example query to find RefSeq reference genomes.
So this is a picture, a screen shot of one of our non-redundant
protein records, and you can see it has the WP accession number
showing up here on the record.
I want to point out that these records are annotated at the
lowest common denominator in terms of what organism
information they carry.
So if there is a -- in this case, this is an extreme case,
this is something that is annotated on genomes of
different species, where the lowest common denominator of
those species is bacteria, and so that is the organism
information that is carried on this sequence record.
You might find others that are carrying organism information at
the level of the genus, and, of course, there are still many that
carry organism information at the level of the species.
There's more information about the non-redundant protein
sequences available online, and at the top of the page is the
URL to that documentation.
At the bottom of the page is the URL to the specific protein
record that I'm showing you here.
Very recently we've added a database that is being used to
manage protein names, and so within that database we have
stored all of the protein names that are currently used for our
non-redundant protein records.
We have stored all of the Swiss protein names.
We are collecting and generating a large dataset of HMMs.
We are doing protein clustering.
We are looking at identical proteins, and we are curating
names.
And so we are embarked, in the past couple of months, on a very
massive initiative to improve the protein names on these
records and to standardize the way we're formatting protein
names and these records, and so you should look for improvements
to continue to come through the course of this year.
And I can't spend too much more time on this.
I think I'm over my webinar ten minutes, but if you'll bear with
me for a couple more minutes, this is a picture of a new
webpage display called the "Identical Protein Report."
You can get to this from any protein record.
There's a link at the top of the page in the same location as the
GenPept FASTA graphics link, shown here, to access this page.
This is a very useful page.
It tells you how many genomes a given protein record was
annotated on.
In this case I've boxed three.
So this same protein accession number has been annotated on
three different RefSeq genomes, the accessions are shown here,
and the coordinates of the annotation are shown here.
The organism information is shown in the column to the
right, and so you can see that these are three very different
bacterial organisms, and the lowest common denominator for
these is at the bacteria level.
In some cases, this may be an indicator that there was a
problem in identifying the correct organism for a
submission, but in other cases this is really reflecting very
interesting biology.
And last but not least, in the process of converting to our new
data model, there was an awful lot of suppression that
happened.
We suppressed the intermediate level of records, the YP and NP
accession numbers on all of our representative and variant
genomes.
I just want to make a note that for the reference genomes they
continue to use the data model where there is an independent
accession defined for those, for the reference genome.
For those records that were suppressed, we have provided a
custom message on those records.
So if you go to this URL, you will see a very informative
message provided that tells you that this accession that has
been suppressed has, indeed, been replaced by a non-redundant
protein record.
And you can navigate to that protein record to the identical
protein report or the documentation about our "Me
Imitation Project." And last, there is a comprehensive mapping
file provided for the those who would like to have a
comprehensive report by FTP.
This file is available in the RefSeq Release Catalog
Directory.
This was put out in the beginning of this month.
In July, when we process our next RefSeq release, this file
will be moved into the Release Catalog Archive Directory where
it will continue to be available.
This file provides mapping between the old suppressed
protein accession number to the alive non-redundant protein
accession that is replacing it.
It also maps old locus tags to new locus tags.
We had to annotate our genomes with new locus tags because we
are now providing de novo genome annotation for all of our
records, and there are some new genes that are being called.
So we cannot rely on existing locus tags that may have been
submitted.
It also reports information on GeneID mapping for people who
are interested in that information from NCBI's gene
resource.
And I would just like to thank people who have done all of the
work on these changes.
If you have questions, you can look at the online documentation
that I provided links to in these slides.
You can also look at our FAQ or you can contact us at either
info@ncbi or webinars@ncbi to follow up with questions.
Thank you.
So the question is what is the source of the annotations
qualifiers attached to the annotation of non-redundant
protein entry, and what is the procedure if I have a question
about that annotation?
So are you talking about -- I'm not sure exactly what you mean
by qualifiers.
Are you talking about the organism information, the
protein name, or something else?
Oh, the product, so you're talking about the protein name.
So, initially the source was what came in from GenBank.
NCBI have been working on correcting clustering and naming
things that are similar by homology for a couple of years.
In the beginning of March we created this new name management
database that really gives us a lot of power to improve our
protein names, and we have already improved about 1.2
million protein names, and this is an area that we are extremely
actively working on.
So if you have specific name examples, then, yes, please send
them, or specific names that are bad, then, yes, please send that
information to us.
We're very, very interested in improving the quality of the
names on our non-redundant protein records, and you can use
the info@ncbi e-mail to contact us for that.
Okay, that looks like all of the questions.
If you think of something else, go ahead and send those to the
webinars@ncbi e-mail address, and we'll get those included in
the documentation for this webinar.
Thank you very much.
We're going to sign off now, and we'll see you at the next NCBI
minute.
Goodbye.