[MUSIC PLAYING]
Hello, everyone, and thanks for attending the first ever Google
Genomics webinar.
My name is Sri, I'm the Business Head for Google Genomics,
and today I'll be walking you through some
of the key features, benefits, and some cool example
applications of the genomics platform
that we are very actively developing.
It is fairly common knowledge now that sequencing cost has
been plummeting, much faster than Moore's law,
and the volume of genomic information
is exploding, as you can see in this slide.
This trend is not likely to slow down anytime soon.
Even reasonable projections estimate
that the amount of genomic data will double every year,
and will hit one exabyte within the next five years.
If the current trends persist, we will hit a billion genomes
by 2025.
That's billion with a B.
But having all that raw data is not useful
unless you're able to extract clinical meaning from it.
We will need to layer on this raw data
a variety of analytics, ranging from data mining,
novel algorithms, intuitive visualization,
all the way to very deep machine learning,
to ultimately have patient and health care impact.
This kind of data quantity transition
has been seen by Google before.
We have seen it in search, in video, and also email.
Now, as scientists scale up their studies,
and begin to query thousands, and soon, millions of genomes,
you will need to have scalable technologies for manipulating,
analyzing, and interpreting enormous data sets,
without the time or cost of moving
that data from place to place.
And this is where Google believes it can help.
Google Genomics that is built on the Google Cloud platform
aims to help the life science community organize the world's
genomic information, and make it accessible and useful.
And just to give you some perspective on Google Cloud
Platform, just a part of Google's infrastructure,
which supports YouTube, can handle 300 hours
of video every minute.
And our search, which returns you
the results in a quarter of a second,
that search index is over 100 petabytes in size.
Now let's translate that into genomic terms.
That is the equivalent of raw data from six whole genome
sequences uploaded to our servers every minute,
and we can index and search the equivalent of a million
whole genome sequences currently.
Now let's talk about policy, and mounting scientific evidence.
Policy-- There's increasing recognition
on the importance of cloud computing
for genomic data analysis, so much so that in early March
the NIH updated its genomic data sharing
policy to allow use of public cloud to store and access
genomic data.
In parallel, there's mounting evidence in the life science
community that cloud-based genomic data
analysis is the future.
Recent articles in "Nature" showcased how much
cheaper, faster, and flexible cloud computing resources were,
compared to local data centers.
Google Cloud Platform resources are even
cited as a key public cloud computing
resource that is used increasingly
by genomics researchers.
Google Cloud Platform offers both infrastructure
as a service, and platform as a service.
However, we also build a unique genomics API
that powers many genomic-specific features,
and like a buffet, you can choose
whether you want to use our infrastructure
services, platform services, or genomic-specific features set
that we have built.
We group the key benefits of Google Genomics
as Store, Process, Explore, and Share.
Let's talk about these one by one.
Store- we enable content-aware storage.
That is, we know that you are storing A, T, Z's, and C's.
And we have separate read and variant
stores, that allow a variety of different functionality.
All of this data can be accessed by a web
API that is an implementation on the GA4GH specifications.
We store many different data sets, like 1000 Genomes,
Platinum Genomes, as well as reference sequences
for your analysis.
I will also talk soon about a couple of large data sets
that are stored on Google Genomics.
Let's move on to Process.
We allow running of data processing pipelines
to analyze public or private genomic data stored
on Google Genomics.
In addition, we inked a deal with the Broad Institute
recently to bring the popular GATK
pipeline as a service in a cloud-optimized format.
Moving on to Explore.
We enable tertiary analysis of genomic data using a variety
of tools for research purposes.
We enable usage of Google-scale tools
like BigQuery, that allow real time analysis
of massive volumes of data.
Moving on to Share.
We enable sharing of these data sets painlessly, yet securely,
with fine grained access control, two factor
authentication, client keys, security at rest
and at transit.
And security is paramount to us.
We sign BAAs that ensure our platform
supports our customer's HIPAA compliance requirements.
We also have a variety of security certifications,
and some of the highest security encryption
standards in the industry, to ensure that your sensitive data
stays safe.
Now let's get a little bit into the details of how
Google Genomics actually works.
The sequence data from the sequencer
can be stored in a variety of different file formats,
such as FASTQ, BAM, or VCFs on Google Cloud.
It is then copied from Google Cloud to Google Genomics
into the read or variant store, at which
stage we know its genetic information.
The API calls can then be made to make a variety of queries
or analysis on this data.
If you want to do further tertiary analysis,
we also enable specific data cohorts
to be exported to BigQuery for further analysis.
The interesting thing to note here
is that we enable users of varying levels of expertise
to access and analyze this data.
If you're an expert programmer, you can write code.
If you're a wet lab scientist, you
can use a web UI to write simple SQL TypeScripts.
If you're a bioinformatic scientist,
and your favorite tool is RStudio or iPython Notebook,
you can directly make API calls from your tool of choice.
I'd like to provide a few examples that illustrate
these various benefits.
We will start with walking through the Autism Speaks data
set, and then move on to the National Cancer Institute's
TCGA data set.
Autism Speaks is one of the largest nonprofits focusing
on autism-related research.
As you can see, there is overwhelming evidence now
that there's a genetic basis to the autism spectrum disorders.
To understand this genetic basis,
Autism Speaks has launched an ambitious project
to sequence 10,000 persons, ranging
from children with autism, affected as well as unaffected
siblings, and parents.
This entire data set is going to be made available
on Google Genomics for qualified researchers,
and when completed, will be over a petabyte in size.
This data set stores genomic data, ranging from raw reads
to the variants, as well as phenomic data,
such as diagnosis result, demographic info, family info,
and a few other pertinent metadata.
We already have about 1,700 whole genome sequences
and associated phenomic data for researchers to start analyzing.
Autism Speaks also built a web portal to host its database,
to allow interactive querying of the data set.
This allows non-bioinformaticians to easily
query and explore interesting data cohorts.
Now let's talk about a second valuable data
set, which is the National Cancer Institute's TCGA data
set.
Google Genomics is involved in two of the three winning bids
for NCI's Cancer Cloud pilot.
Both the open access TCGA data, as well as
the controlled access data, requiring dbGaP authorization,
will be hosted on Google Genomics.
And just like Autism Speaks, ISB is developing a web research
portal to allow users to explore the TCGA data set
and select pertinent cohorts of interest,
and open it directly into an iPython notebook
to continue their analysis.
Switching gears, we will now focus
on the Process and Explore aspects of our platform.
For analysis, we will briefly review Broad's GATK processing
pipeline.
We will then look at the open source code examples
that we have made available on GitHub.
And we will wrap up with BigQuery
doing powerful analysis on genomic data.
Let's talk about the first example.
Google Genomics recently announced a partnership
with the Broad Institute that combines their genomics
expertise with Google's engineering expertise
to help address complex biological questions.
A key initial foray in this, is to bring the widely used GATK
pipeline as a service on Google Genomics.
This enables users to not have to worry about infrastructure,
or platform, or license, and simply
focus on running that pipeline on their data.
Now let's talk about BigQuery, which
was a powerful Google-scale tool built originally
to interrogate very large internet log files, containing
billions of entries.
Turns out, this is very well suited
for querying genomic data for tertiary analysis needs.
In this block, BigQuery is being shown
to do a count and a plot of all the variants with single R2
alleles on the 1000 Genome data set.
As you can imagine, this is a fairly heavy-duty calculation,
and this query processes a terabyte of data,
and provides a result in 7.4 seconds.
This type of rapid querying was not
previously feasible without using
such powerful Google-scale tools.
This makes research interactive and real-time, resulting
in accelerated innovation.
Finally, Google and the research community
are building a growing repository of open source codes
and tools that are available on GitHub for researchers.
They can use it for their own pipelines
or modify it for their analysis.
Listed here is a subset of some of these tools, which
you will find valuable, and I'll show two of these examples
now for you.
Shown here is an example of principal coordinate analysis
code that we have made available on GitHub.
When we run this PCA analysis on the entire 1000 Genome data set
and color code the resulting scatter
plot by population type, we get a nice clustering
by ethnicity as expected.
Here's an example of code created by Stanford University
researchers, also available on GitHub.
This is used to perform quality control on genomic data sets.
This code specifically plots heterozygocity rate
on the X chromosome that helps easily
identify mislabeled samples, shown
in blue dots towards the top of the graph.
We hope to continuously improve this code set
and increase the ecosystem of valuable code
that users can pull from on our GitHub page.
Like the Autism Speaks and the TCGA data set,
we welcome interest from the community
to host valuable data sets on Google Genomics.
Similarly, like GATK, we welcome your analytics pipelines
to be hosted and shared on Google Genomics as well.
And that brings us to the conclusion of this webinar.
We hope that you have a good understanding of Google
Genomics platform, and how it can really
help you store, process, explore,
and share genomic information.
For more information, please visit our website listed here.
Thank you so much.