We're back on time, so it's very, I'm very pleased to present Tilo Mathiths who will be
speaking about something that often asks people who sit at computers, kind of forget,
which is that biology is actually an experimental science and over to you.
Right. And I'm also, I'm co-presenting here with Hussie, so it's a very important part
because we're talking essentially about two open source projects that came together to solve
a problem in science and it also has to do with provenance and tracking essentially the
scientific progress. So yeah, I really want to start with a small overview on how you get
from primary data to computational analysis, what is involved in there.
And then we'll both talk about our collaboration, like how we thought about exchanging data
between two tools that do quite different things and essentially leading to connecting computational
workflows with overall research documentation. So I want to start with an example that's a real
person, Rolizsa. She is working with us on certain things and she's one example of a researcher who does
both a lot of wet lab work. So there's a lot of sample preparation, DNA extraction. And then
she hands it over to computational work. And a lot of times a lot of these researchers are doing
like all of these things to some degree, right. So for her to challenge just like how to
aeroblessly connect that primary data, these physical samples to the experimental context and then
the analysis workflows that have been done and afterwards on it. So if you look at the tools and
the workflows and relitz us, you see there's a lot going on, right? There's cultivating cells,
sample preparation, a lot of small data collection, like images, handwritten nodes, digital nodes,
all kind of stuff. The data lifts across different kind of file stores, local computers, etc.
Right. And then essentially a lot of the data is analyzed. So there's code snippets lying around,
node Jupyter node books, HPC scripts, etc. Right. So and how essentially do you get like all this
together? And here is basically where briefly we want to introduce the tools that we're looking at.
So I work at research space. We build our space. It's an open source research platform for
institutional research data management. So if you look at the core of this very busy image here,
there's an electronic lab node book and an inventory management system in there,
which helps researchers in the active phase of like sample preparation and primary data
preparation to document what they did and what came out of it. What differentiates us from
other node book tools and inventory management tools is that we're very cool interoperable
with other research tools and research infrastructure, such as institutional file stores,
data management planning tools, etc. And essentially our space can through these integrations
can essentially become a hub of recording the scientific progress. So and with that I'll give it
over to you. Thank you. So about Galaxy, we are basically open source data analysis platform
on the web. It originally started from the field of bioinformatics, but over time it has been
expanding to other scientific algorithms such as climate science, ecology,
came from mathematics, imaging data science, materials science, astronomy, and most recently,
even humanities. I put a very simple example there of like an extremely simple analysis that you
could do imaging data that is basically counting the amount of points that you have on this
microscope image where you can see. Okay, great. Where you can see basically a stain cells,
but most typically you don't carry out this kind of analysis, but you use Galaxy in conjunction
with an HPC computing network and you run computationally heavy and more complex analysis. For
example RNAsake or I don't know, protein folding or all these sort of analysis that you do in
bioinformatics. You can use the platform via web browser via an API and most recently we also have
an MCP server, so if you want to connect AI to it, that's also possible. So the design or
Galaxy is same that researchers and it focuses on accessibility and transparency. The design
is let's say the most two most important concepts are histories, which are a sequence of
no code and reproducible transformations that you apply to data sets and those are carried out
by the so-called Galaxy tools which are light wrappers around existing bioinformatic tools.
Then the next logical steps are the workloads, which are recipes that spawn a history from
a set of inputs. Basically, you have some dynamic control flow as well on top of that and they
can be created using the workflow editor, which is GUI where you can basically rearrange the boxes,
up boxes, connect them and in this way control the flow, you can create the workflows from a
history etc. And then the design is very friendly to the very principles because of some
characteristics. I will mention only some of them like append-only design. You also have version
tools and version workflows. It's possible to publish everything you have seen some links around
my presentation. This is because I've been publishing the analysis and the workload. You can just
click there and you will have access to it. You can also export workflows and histories and the
platform can interpret with many storage systems and compute and you can even bring your own
for your user. Of course, this history system has built-in provenance and that's about it.
The last part is about what is Galaxy. Of course, also a community, we would be nothing without
our users because a community maintains some critical parts or infrastructure for Galaxy. One is
a toolset, which is a public repository of tools, contributed by the Galaxy community, which has
over 10k tools. Then there is also the Galaxy training network, which is a large collection of
tutorials that are also contributed by the community. You just visit this URL and you have access to
a wide range of tutorials in different scientific domains and also in Galaxy development. So
administering a server or developing tools. Then the community sensors aim at specific research
areas. Many of the things have their own subdomain, so they can have their own list of tools and so on.
Finally, but most important are the public Galaxy servers that can be accessed by anyone and are
free to use. I personally maintain the use Galaxy.eu server, but there's also a large US server
and an Australian server and also a French one, but more and in the making. And just listing,
let's say the ones that have existing for a longer time, but we are incorporating, for example,
a Belgian server and so on. So with this, I hand back over to Tilo. Although you will have back
to me. Thanks. Yeah. So essentially, like you've seen, we have now two tools and we want to use the
best of both worlds. So in Galaxy, you have user-friendly access to computational workflows.
In our space, it's basically a documentation hub, right? That also then can put your data
once you're done with it elsewhere later. So we thought about, like, how can we use these two things
to streamline workflows and help researchers like Reloads? And so essentially, so essentially,
from the Galaxy side, we incorporated our space as a file store. This means that you can mount
our spaces are repository in Galaxy. And you can therefore import or export data sets from our
space or to our space. You can also export the whole histories themselves. You can also export workflows
as arrow crates. And the setup process is quite simple. Basically, you go to your user preferences
and you will have a screen to connect your own instance or whatever our space instance that you want.
And then, basically, after that, you have it available as a repository to import and export files
and browsing. So. Right. And then we thought we wanted to make it, like, also more user-friendly
from the our space side. So we created a small fordance where researchers can from a document that
they have in our space that has data connected to it. Basically, create a new history in Galaxy
and send that data over automatically. Automatically, also there's some metadata being pushed
over like unique identifiers of the file and the data in our space. And then as soon as on the
Galaxy side, the workflow is invoked, the user in our space can actually keep track of what's going on
when it's done. And then when it's done, they can put the data back into their our space document.
Right. And yeah. And so, who says that, like this, these exports can be informal for
crates, right? Or bio-computer objects. And last but not least, if we have a little bit of time,
we have a video, how this looks like. Not sure if the sound will work.
Oh, yes. That looks good. Yeah.
The video doesn't string. Let me test this out. We haven't seen the information.
There's seamless data analysis and documentation on it for us. Well, automatically,
keeping provenance from primary data to analysis results. Starting in our space, researchers
can select data attached to their documents like this aerial forest photograph and upload it directly
to Galaxy with one click. Our space automatically creates a new Galaxy history with systematic
naming based on the our space document that data was attached to. Additionally, our space adds
metadata linking back to the original experimental documentation and data in the annotation of
the transfer to files. In Galaxy, researchers can now run any available workflow on their data.
Here, we're using more noise segmentation to identify and count individual tree grounds in the forest image.
Back in our space, researchers can track the progress of their analysis in Galaxy by inspecting
the workflow status. When complete, the researcher can use the direct links to navigate to the specific
invocation and its results in Galaxy. From the invocation view, researchers that have set up our space
as a file source of Galaxy can export complete workflow packages directly back to a
allocation of their choice in the our space gallery.
Alternatively, they can select specific result files to be transferred back to our space gallery
using the send data tool.
Finally, results can be efficiently integrated directly into the original our space document,
maintaining full prominence from experimental data through computational analysis and making it available
to our space digital system of integrated tools and services.
This integration is available now with our space version 1.13 and Galaxy 254.
That was that. We also want to conclude. We have a couple of links to the Galaxy community
and also to the our space community resources. We actually also have an open office hour coming
up next week. You're all invited to join if you want. With that, any final words from you?
Oh yeah, thanks for your attention.
So unfortunately, we do not have time for questions but we do have a matrix space which you can go
to ask questions and you should post about the office hours and also the in-of-go-hack
from coming up as well. People who want to leave please do leave. Let other people in. Thank you very much.