Technological Advancements in public health bioinformatics

Summary

In this episode of the Bioinformatics Lab podcast, Kevin Linwood and Andrew Page discuss the evolution of technology adoption in pathogen genomics. They explore the journey from software packages to cloud-based solutions, emphasizing the impact on interoperability and reproducibility. They reflect on the challenges of software installation, the rise of package managers like BioConda, and the game-changing introduction of workflow managers. The conversation also touches on the importance of open-source tools for public health and the potential of machine learning and AI to revolutionize the field.

Takeaways

• 🌐 The narrative of tech adoption in pathogen genomics has evolved from software packages to cloud-based solutions, impacting the field significantly.
• 🛠️ Early challenges included lengthy software installation processes and managing dependencies, which have improved with advancements in package managers.
• 📦 The introduction of Debian Med was a pivotal step, providing a sustainable and maintainable way to handle software packages in bioinformatics.
• 🔧 The significance of easy software installation was highlighted as a key factor in adoption, with tools like Homebrew and Bioconda streamlining the process.
• 🐍 Language shifts from Perl to Python have influenced the field, with Python emerging as a preferred language for its mathematical capabilities and community support.
• 🔄 Workflow managers like Make, Snakemake, and Nextflow have standardized the way complex analyses are conducted, enhancing interoperability and collaboration.
• 🌐 The adoption of cloud computing has been a game-changer, offering flexible, scalable resources that can be spun up quickly, without the need for extensive procurement processes.
• 🔒 Security and legislative restrictions have influenced cloud adoption, with some countries limiting data to specific regions, affecting the scalability of bioinformatics workflows.
• 🧬 The discussion highlighted the importance of open bioinformatics ecosystems, emphasizing the need for open-source tools and databases to ensure long-term support and accessibility.
• 🤖 The future of pathogen genomics is anticipated to involve significant adoption of AI and machine learning, which may change the field in ways that are currently hard to predict.

Q & A

What is the main topic discussed in the podcast?

The main topic discussed in the podcast is the narrative of technology adoption in pathogen genomics, including software packages, containers, workflow languages, and the impact of cloud-based technologies on the field.

What does the podcast highlight about the early days of software installation in bioinformatics?

The podcast highlights that in the early days, installing software could take a significant amount of time and effort, sometimes requiring a dedicated person to manage the process due to the complexity of dependencies and compatibility issues.

What role did package managers play in the evolution of tech adoption in bioinformatics?

Package managers played a crucial role by simplifying the installation process of software and their dependencies, making it easier for users to access and use bioinformatics tools.

How did the introduction of workflow managers change the field of bioinformatics?

Workflow managers allowed for the standardization and streamlining of bioinformatics processes, enabling users to string together different tools in a cohesive way, which improved interoperability and collaborative development.

What is the significance of containerization in bioinformatics?

Containerization, through technologies like Docker and Singularity, has made it easier to manage software dependencies and ensured portability of tools across different computing environments, thus enhancing reproducibility and ease of use.

What is the impact of cloud computing on the field of pathogen genomics as discussed in the podcast?

Cloud computing has allowed for faster access to computational resources, reducing the need for long procurement processes and enabling researchers to scale up their analyses quickly and efficiently.

Why is the adoption of open-source tools and platforms emphasized in the podcast?

The podcast emphasizes the adoption of open-source tools and platforms because they promote accessibility, collaborative development, and long-term sustainability, which are crucial for public health and research applications.

How does the podcast view the future of AI and machine learning in pathogen genomics?

The podcast views the future of AI and machine learning in pathogen genomics as transformative, with the potential to change the field in ways that are not yet fully understood, but will likely lead to increased efficiency and new analytical capabilities.

What challenges are associated with cloud adoption mentioned in the podcast?

The podcast mentions challenges such as data sovereignty issues, where some countries have legislation that restricts the use of cloud services to within their borders, and the sensitivity of clinical data that cannot be moved across certain boundaries.

What is the significance of graphical user interfaces (GUIs) in making bioinformatics more accessible?

Graphical user interfaces (GUIs) are significant because they abstract the technical complexities of bioinformatics, allowing users to focus on analysis and interpretation rather than command-line operations, thus making the field more accessible to a broader range of professionals.

How does the podcast reflect on the importance of open bioinformatics ecosystems?

The podcast reflects on the importance of open bioinformatics ecosystems by discussing how they facilitate the distribution of tools, enable collaboration, and ensure that resources developed with public funding are available for public health laboratories worldwide.

Outlines

00:00

🌐 Evolution of Tech Adoption in Pathogen Genomics

Kevin Linwood and Andrew Page discuss the historical narrative of technology adoption in pathogen genomics, starting from software packages to cloud-based solutions. They reflect on the evolution from manual software installation to package managers like Debian and the impact of containerization and workflow languages on interoperability and reproducibility in the field. The conversation emphasizes the importance of making software easy to install to encourage usage and the challenges faced in the past with dependency management and software installation.

05:02

🛠️ Transition to Workflow Managers and Containerization

The discussion shifts to the advent of workflow managers, starting with make files and evolving to more sophisticated tools like Snakemake and Nextflow. The speakers highlight the pivotal role of workflow managers in standardizing analysis processes and enabling the seamless integration of tools. They also touch upon the transition from traditional HPC clusters to containerization with Docker and Singularity, which解决了 dependencies and portability issues, and the initial resistance and subsequent acceptance of these technologies in the field.

10:02

🌩️ The Impact of Cloud Computing on Bioinformatics

Kevin and Andrew explore the transformative effect of cloud computing on bioinformatics, discussing how it has simplified the procurement and management of computational resources. They recount personal experiences with cloud adoption, the ease of scaling up resources, and the challenges faced due to legislative restrictions in some countries. The conversation also covers the importance of open-source tools and the risks associated with closed-source solutions, emphasizing the need for open access to maintain and extend critical tools for public health.

15:04

🌱 Tech Adoption in Wet Lab Sequencing and Data Sharing

The speakers delve into the adoption of different sequencing platforms, from Illumina to Oxford Nanopore, and the implications for data generation and benchmarking. They express concern over the lack of inclusion of certain technologies in benchmarks and the potential biases this may introduce. The discussion also addresses data sharing practices, the challenges of cross-border data transfers due to security and privacy concerns, and the need for standardized outputs to facilitate data integration and analysis.

20:07

🌟 The Future of AI and Machine Learning in Pathogenomics

Looking ahead, Kevin and Andrew anticipate the significant impact of AI and machine learning on pathogenomics. They discuss the initial forays into AI with tools like Pangolin and the preference for interpretable methods over black-box models. The conversation speculates on how AI could change the field, potentially automating routine tasks and enhancing data analysis, while also raising questions about trust, interpretability, and the need for policy frameworks to guide the adoption of these advanced technologies.

25:07

🔚 Wrapping Up the Discussion on Tech Adoption

In the final part of the conversation, the hosts summarize their discussion on tech adoption, highlighting the dynamic nature of the field and the continuous evolution of tools and platforms. They express optimism for the future, with the anticipation of less manual work and more efficient data analysis through AI, while acknowledging the ongoing challenges in understanding and trusting AI-driven decision-making processes. The episode concludes with a commitment to continue exploring these topics in future discussions.

Keywords

💡Bioinformatics

Bioinformatics is the application of computer software and hardware to store, retrieve, analyze, and represent biological information. In the context of the video, bioinformatics is central to the discussion of technological advancements in pathogen genomics. The hosts discuss how the adoption of various software and hardware technologies has transformed the field, making it easier to analyze genetic data and contribute to public health.

💡Tech Adoption

Tech adoption refers to the process by which new technologies are integrated into a field or industry. The video narrative traces the evolution of tech adoption in bioinformatics, from software packages to cloud-based solutions. It illustrates how these technologies have become essential tools for researchers in pathogen genomics, impacting areas like interoperability and reproducibility.

💡Pathogen Genomics

Pathogen genomics is the study of the genetic material of pathogens, which are disease-causing microorganisms. The video discusses the impact of technological advancements on this field, highlighting how easier software installation, improved workflow management, and cloud computing have facilitated research and analysis in pathogen genomics.

💡Package Managers

Package managers are tools that automate the process of installing, upgrading, and managing software packages. In the script, package managers like Debian and Homebrew are mentioned as part of the tech adoption narrative, emphasizing how they simplified the installation of software and dependencies, which was previously a complex and time-consuming task.

💡Containers

Containers are a type of software that packages an application and its dependencies together. The video mentions Docker and Singularity as examples of container technologies that have improved portability and dependency management in bioinformatics, allowing researchers to run complex software stacks in isolated environments.

💡Workflow Managers

Workflow managers are tools that help in designing and executing complex workflows in a structured manner. The hosts discuss the transition from individual tool management to using workflow managers like Nextflow and Snakemake, which allow for the creation of reproducible and scalable analysis pipelines in bioinformatics.

💡Cloud Computing

Cloud computing refers to the delivery of computing services, including server time and data storage, over the Internet. In the video, cloud computing is highlighted as a significant tech adoption that has enabled rapid scaling of computational resources, making it easier for bioinformatics labs to access the power they need for pathogen genomics research.

💡Interoperability

Interoperability is the ability of different systems and components to exchange data and operate together. The video emphasizes the importance of tech adoption in promoting interoperability in bioinformatics, allowing for the seamless integration of various tools and workflows, which is crucial for collaborative research and data sharing.

💡Reproducibility

Reproducibility in scientific research means that experiments can be independently replicated to confirm results. The script discusses how tech adoption, particularly in the form of standardized workflows and containerization, has enhanced reproducibility in bioinformatics by ensuring that analytical processes can be consistently applied across different environments.

💡Public Health

Public health is the science of protecting and improving the health of communities through education, research, and policy. The video connects tech adoption in bioinformatics to its impact on public health, arguing for the importance of open-source tools and databases in supporting public health initiatives, such as disease surveillance and outbreak response.

Highlights

Narrative of tech adoption in pathogen genomics from software packages to cloud-based services.

Impact of tech adoption on interoperability and reproducibility in the field.

Early challenges in software installation and dependency management.

Evolution from manual software installation to package managers like Debian.

The importance of easy software installation for user adoption.

The rise of Homebrew and the shift to bioconda for easier software management.

Mamba's role in expediting the installation process in bioinformatics.

Adoption of workflow managers like make, snake make, and nextflow.

The transition from single tool integration to comprehensive workflow management.

The significance of Galaxy for accessible bioinformatics workflows.

The move towards cloud-based solutions and their impact on resource accessibility.

The critical role of open-source in public health and pathogen genomics.

Challenges and benefits of adopting different sequencing platforms.

The influence of cloud computing on the scalability and flexibility of bioinformatics.

The future of tech adoption with a focus on machine learning and AI in pathogenomics.

The potential of AI to transform bioinformatics workflows and decision-making.

The necessity for policy and trust in AI-driven bioinformatics.

Transcripts

all right welcome I'm Kevin Linwood

joined by Andrew page we're from Fijian

and this is the bioinformatics Lab

podcast

today we're going to be talking about

sort of narrative of tech adoption over

the years in pathogen genomics uh from

you know software packages to Containers

workflow languages uh and now at this

point of cloud-based guise and the

different ways in which that's impacting

our field

and are you having much more time in in

the field than I have so you have maybe

a longer time Horizon I say that with

absolute respect for your wisdom and

experience in the field

um but you have a broader perspective of

what this has looked like because again

whenever I talk about the sort of tech

adoption story I kind of started package

managers but of course there's beyond

that you know before uh but all this

conversation of how do we make this how

do we mature this as a field for

interoperability reproducibility and all

the likes so when you think about that

The Narrative of PEC adoption

surely it starts before package managers

oh yeah like I mean I remember the dark

days where you'd have to hire someone

you know just to sit there installing

software because it would be it would

take a week to install one piece of

software you know please be editing it

and trying to find 200 different you

know dependencies for a compiler that

you know was last released five years

ago and so we've come up we've come a

very long way

um even then you know like languages and

everything like I started off in I guess

Ruby and then Pearl

um for about Maddox and you know

languages change all the time and no one

uses Brill these days unfortunately well

me uh but anyway you know and then

python so you know things change over

time and uh we do get better and

actually I genuinely I think that um

Pearl

isn't as good for mathematics as python

you know I've after maybe about 10 years

I've come to that conclusion of years in

it but then I see like my son is using

rust and he's like oh this is amazing

you know like and uh it's much faster

and it probably is actually but I'm not

gonna give a python you know

um for some new fad language it's only

been around a couple years uh anyway so

I digress like it you know

so if you just take the most basic

installing software and package managers

like

the big thing a few years ago was just

you know with Debbie and uh yeah and uh

bunty's Debian packages so can you get

into can you get something in a Debian

package you know because once it's in

there then you're sort of for life you

know

um and Debian Med was the big one for

our fields which is um for biomedical

resources go in there

and yeah

phenomenally difficult if you start

packaging for Debian Med they will

assign you like a mentor if someone who

can guide you through the process over a

few months wow because it's quite you

know they want these things to to work

universally and you know be working for

a long time so a few months to guide you

through the process of building your

Debian packages you know which would

then be sustainable in the long term and

uh kind of easy to maintain and

gosh so that was probably first gun

during 2015 and because I just happened

to work with a guy who's one of these

maintainers and

that the advantage of that is when you

go to command lending and just have up

to get install blah like Rory or

something like that and that's that

lowers the bar so much that it makes it

trivial and if you make your software

easy to install then of course people

will use it and that's that's a key

magic trick you know to uh to getting

people to use your software is to make

it trivial to install and then you know

you go one step further and then we had

a Homebrew do you remember that yeah I

do that's that's about when I entered

the the chat because yeah which is uh

which is a little bit you know another

system they made a bit easier to go and

install software uh but it kind of fell

off the rails

um they became unwieldy condo came along

bio conda and that's been kind of the

default now for people to use

um because it just works and you know

everyone has kind of gathered around

that it's quite easy to install stuff

dependencies are you know are reasonably

okay at the moment and you can't get

into obviously a bit of a nightmare

sometimes

um and then Mamba has really helped the

installation process you know rather

than waiting an hour if tensor won't be

software because things got so complex

with resolving dependencies you know it

now works it's pretty quick so you know

we've come a long way

um we've also come along with workflow

managers what was the first workflow

manager used

make I I think I would consider make the

first kind of workflow manager I was

writing make files where I was like

writing shell scripts of you know just

executables and then I would compile

them to not compile them I guess but I

would kind of curate the workflow into a

make file and then I would Define

endpoints at the beginning and then it

would be you know that's how the

workflow worked it was just recipe style

it would Define the endpoint to find the

process is very much the same components

I mean and then that's where I started

learning about snake make which was

really make in Python

and then where I really started running

in workflow managers was uh use of next

flow and I think it was actually Aaron

young again on the staff b side who kind

of introduced us to um workflow managers

and specifically uh maybe was

Kelsey Floric actually I don't remember

exactly but

they introduced us to the concepts of

workflow managers so we kind of went

from the conversation of how do I get a

single tool

to work properly on my on my machine

ensure that the dependencies are

consistent throughout the different

environments but then workflow managers

changed it in that we were able to

string them all together in a really

cohesive way sorry one second

um but

that became kind of a really pivotal

Point again I'm speaking on the staff b

side but this is something that I think

is echoed throughout the field of it's

so much more standardized and how we're

doing these these these analysis you

don't have to write your own python

logic to string data from you know your

aligner to your variant caller to

whatever it is Downstream for

characterization

rather there was a consistent language

there somebody outside of it of of your

laboratory can look at it and understand

okay this is an excellent workflow I

understand that the architecture of this

this entire repository and moreover than

that I could take the modules from that

and build it into my workflow in a

pretty seamless way so that that was a

huge jump in terms of interoperability

collaborative developments that we saw

in staff B uh and we never really looked

back from that that became just a status

quo

at first one I used was um gold

vertebral resequencing uh code base it

didn't even have a name like VR copace

was the name and it was built for the

Human Genome Project not human that

hasn't Gina a Human Genome Project

um in the Second Street so this was this

was before people were doing things at

scale and genomics so you know you're

talking more than 10 years ago and said

I love you know next word didn't exist a

lot of these other things did not exist

yeah and uh so they had to be built you

know and uh sang as she was uh at that

time you know would be producing maybe

you know 20 30 of the world sequencing

from one place so you know they had a

scale different people didn't have and

so that's what we adopted and uh we were

done adopting it for pathogens you know

which is quite different to human

because you have to do a lot of things

uh you've got a lot of small little

things and that's very different to

human which is you've got a few big

things that you want to do things on

um so it's like you know it's a flipped

problem and of course that breaks

everything and uh yeah so anyway that

was the first one I ever worked on a

highly complex

um changing anything was very difficult

it was all in code you know I was on

GitHub whatever go to his own code and

it was all in Pearl as well and you know

everything was monitored by say writing

little files to disk you know and

checking them around whatever

so it worked but you know the growing

pains came in and so then the next

Generation came out

um

which again was a super lovely beautiful

pearl

um but you know again it was it was more

cloud computing focused and it did

things very well but but then you know I

had seen Galaxy a few times like oh this

is actually pretty cool so when I went

to Department that was first thing I

brought in was Galaxy because you know

you got a web server that anyone can use

it can run on us on a cluster or on

cloud and it's got all the tools you

know kind of LinkedIn and built in

because people you know again wrap-up

tools simple XML files wrap the tool up

and make it work and you know can

broaden wakanda or whatever and then I'd

like a lot of people to do complex

workflows a lot of people who don't know

the command line moving them away from

you know the this kind of black screen

and a blinking cursor and they don't

know what to do you know it's uh to oh

yeah I can just click here I can search

for

a liner and then suddenly you can learn

your data and you know everything Flows

In from the sequencers and then it can

self-service uh to do their thing and I

think I really do think uh gooey's you

know their worker weight in gold you can

see the amount of stuff that people can

do so even in Excel you know you've got

power users of excel it can do

phenomenal things if you just give them

something straightforward easy to use

good usability they can work wonders

when it comes to the command line

obviously that's very powerful for doing

things at scale and speed and for

joining things together but you know you

have a very high barrier for entry there

for for an ordinary person even even

someone who works in mathematics it can

be a high priority entry because you

might often have to read through lots of

documentation to figure out how exactly

does this fit in what exact format does

this come out in he shouldn't have to

sell that over and over again you know

it's nice when you can just kind of have

them linked together you know someone

sells it once maybe defines the outputs

say um for next flow or for for Galaxy

and then it's there and it's wherever

whatever more Works

um

yeah anyway uh

workflow managers are fantastic as are

things like self-independencies wakanda

I just love that I mean Jesus Christ

like the the pain the absolute pain you

used to have to go through to install

software and uh you know if you use

clusters like the kind of old school

HPC you know like basically physics

clusters from back in the day you know

massive you know machines in a big room

with blinking lights

actually installing stuff on those is a

right pain in the ass right because one

base operating system and you you kind

of install stuff and load and stuff you

know like the the operating system we

add that particular you know and

whatever is installed probably you know

five years ago that that's the version

you're stuck with that's the version of

compilers are stuck with and you can't

change anything you know so actually

having the ability to run Docker and

Singularity and whatever is is amazing

and that I remember the arguments people

had you know about allowing Docker to

run on a cluster like that no that's

security risk you know we can't be

having that Singularity did help as a

little bit with that but

um yeah it's uh we've come a long way

and now we're in the cloud like I mean

Jesus that's amazing yeah you can just

spin something up in a few minutes you

don't have to wait two years to go to a

procurement process and buy stuff you

can just bang There You Go have

resources run stuff and you have full

control over it then you can tear it

down and those are that's often the the

kind of three

in terms of tech adoption those are like

the three turning points I always have

in my mind it first is sort of maybe I'd

put a slash in package managers and

containers where it was solving the

problem of dependencies and install and

portability of their single Tools in

itself and that's solving a lot of the

problems that you described and then so

now we can all if I have a tool if I

have an assembler I can run it in a

really reproducible way in my

environment and you can run in the same

way in your environment then the sort of

next big Tech adoption was the workflow

managers not only do I have the same

tools I can stream them in a way that

makes it plug and play and as you

mentioned too these workflow managers

not only help to standardize that but

they also standardize the the running of

them I can run it on an HPC I can run it

on a local VM or I can run it in the

cloud and all the workflow managers now

have built-in capabilities

for that scalability and so that you

briefly mentioned it is that that third

layer is the the GUI the the web

applications that kind of wrap all these

things together you know and my first

experience with that was uh using Galaxy

actually through genome tracker they

adopted galaxies of platform and I

realized oh my goodness I don't have to

teach CLI any longer rather I could

teach them to click a couple buttons I

can show them the workflows and I can

focus on the results and the analysis

and interpretation rather than you know

your Linux environment your library

directory structure this CD means you're

changing folders or something like that

you know and and I think I'm stealing

this from I think it was uh Peter

um Van Houston in in Cape Town where

he's he sent a line and I don't think he

meant to be you know codify this but I

thought it was a really powerful line

and in public health

uh they should be doing less

bioinformatics and more public health

and I thought oh that's kind of a nice

little line it's like yeah we should

really be working to abstract the sort

of technical nuances of bioinformatics

and really instead of approach provide

them with tools that allow them to do

Public Health

but I think gooey's the graphic user

interface is behind that is is really

what what allows us to kind of Traverse

that problem where you don't have to be

card carrying by informatician to do

bioinformatics rather you can use

bioinformatics as a tool to inform your

public health that you're implementing

there and you know obviously we've seen

that I've mentioned Galaxy Tara has been

an incredible resource where you've seen

that kind of come to play

um and then you see again I'm still in

these terms from different people Ali

black and her 10 recommendations for

pathogenomics she coined this term of an

open bioinformatics ecosystem that's

built off all these Technologies we've

talked about containerized algorithms

that are written in the standardized

workflows that are made accessible

through these GUI portals and it's like

oh okay once you find that mix we've

seen that uh that model allow us to

distribute bioinformatics tools to

Laboratories across the world we've seen

that happen in so many different ways

you know we recently put out a

publication and where Tara from the

broad Institute has fit that model

though open bioinformatics ecosystem in

that it's got a really well maintained

graphic user interface Cloud backend

with gcp it's got the doc store

repository which is you know coming out

of the ga4gh universe there

um and then it's uh

because it's standardized workflows you

can also have the standardized outputs

where that you can then transfer these

outputs into different systems be it

transferred to you know SRA and ncbi for

for distribution and international

accessibility you can also transfer

these results into maybe more secure

environments where you might be

combining things with sensitive metadata

for genomic Epi investigation so

watching the tech adoption happen it's

gotten to us a point where now it's just

these resources that uh we've all been

coiling with and trying to make sure

that work on a machine now are in the

hands of laboratorians so that they can

use these things generate the results

and make sense of the data in real time

and form what's happening on the you

know either infectious disease side be

it public health clinical food safety or

otherwise

and you can see what happens where you

know when things go wrong when things

are not open like by numerics

um

the company has decided okay there's not

enough money to be made to maintain this

you know for the public health world so

we're just gonna shut it down and that's

it and that's a closed Source locked

away highly critical tool for public

health and it's it's going and had that

been open it would have been very

different because then obviously you

know the community could take it up and

you know keep maintaining it and extend

it and and whatnot and keep it alive but

if a commercial company holds the rights

to that you know the source code and how

it works and all the all the

infrastructure behind it then it's a

it's a problem and so we really do need

to have open mathematics open tools open

databases open everything

absolutely and that's definitely been

part of the ethos in you know how we

work professionally with Public Health

Labs is that we feel as if it's funded

and supported by public health and it's

applicable to a single Public Health lab

it's very likely going to be something

that other public health Laboratories

could also find utility in you know for

example we work with Laboratories in

Mozambique uh helping develop you know

assays for HIV sequencing and Analysis

these same resources that we're working

with aphl global Health to develop and

innovate upon in in Mozambique we're

watching being proliferated to

Laboratories in the U.S who have the

same interests and again getting back to

that ethos of we're going to develop

this tool publicly funded it needs to be

open source and open accessible because

it doesn't need to be closed off that's

not necessarily the business model

that's conducive to ongoing support

long-term of uh Public Health laboratory

pathogen genomics here

absolutely and under Tech Adoption Fund

actually I was at a conference day the

other day and the what was it

science meets policy using Next

Generation sequencing tackle foodborne

threats at the European food standards

Authority I know it's really really

eye-opening because all the different

you know each country in Europe you know

taking a slightly different way of doing

things but you know obviously everyone

is doing public health and everyone is

doing food safety and you want to eat

the same in goals more or less and it's

very interesting to see how people are

approaching different problems within

the context of their own country some

you know are going very much on the uh

I suppose a short reads you know buying

stuff I do CGM plus T shared at CGM LST

results and others you know very much

more well you know we'll raise the data

or we won't release the data or whatnot

and so yeah very interesting to see how

people are approaching the same problem

yeah Tech adoption it's big yeah we've

only really talked about it on the sort

of dry lab side of things but also on

the wet lab side of things how are

people adopting different sequencers

different platforms from Illumina to ont

I think you have some perspective on

high on tour and adoption and things

like this too

so yeah I was a bit horrified to see um

some Benchmark sets and they had like oh

and T sorry they had iron torrent data

but not ont then I was like come on like

you know I understand the last time I

saw one was actually uh is wrapped in

plastic waiting for a disposal in an

underground car park

it's the first sequencing data I ever

generated was actually on an ion torrent

PGM if you're familiar with that yeah

yeah

they had the little kind of Xbox logos

or something on them as well yeah they

even had I think a slot for your iPhone

if you wanted to you know put some uh

tunes on while you're preparing those

libraries it was it was an iPod or

something something similar that is a

really old connector as well yes yeah

yeah yeah yeah yeah it was uh that was

my first uh time uh generating

sequencing data we had those ion torn

chips uh I I I'm diverting there but

that was my first tech adoption into it

was iron tour and it was interesting

because you know again they're not the I

don't think we're seeing anything that's

wildly controversial but you know known

about the data but every time I would

put the data out there people talk about

the

the air profile kind of associated with

what was being generated there so it is

interesting watching benchmarks uh

getting generated where where you have a

single technology with a known error

profile without maybe also adding some

context with either Illumina data or on

T data especially for benchmarks

yeah and was very eye-opening was that

some countries don't allow people to use

it loud for security reasons

um so it very much limits them and what

it can do in terms of mathematics and in

terms of scaling up as well they can't

just go we need more bang There you go

that's a huge point in talking about

tech adoption is cloud computing that

because that was a big conversation is

infrastructure development across the US

that we were always a part of in staff B

and people were doing pretty much

everything you could see in terms of

on-prem servers hpcs Cloud working with

academic hpcs and all the like but then

it just became so obvious that cloud was

was the solution for all the reasons it

is in every other industry

um so that that's been a big thing but

but it took a while like even me in

Virginia it was like two and a half

three years of discussions with RIT

before we were given our AWS accounts

but I think we're at a critical mass

where we've seen so many Laboratories

kind of break the barrier have the

conversations with RIT that those

conversations are shortening

dramatically and you're seeing wide

adoption across really the world I know

in Academia like um when I came into the

courtroom it was all

um traditional HPC you know big cluster

in uh in a server room on-prem and we he

moved over to openstack you know which

is a private Cloud which is a million

times better and more flexible so you

know that's where we start today and I'm

sure in the future you know it'll be

become public Cloud because it's very

easy to go from an openstack private

Cloud to a public Cloud

um

but what general pandemic is fantastic

was that uh everyone all the um covert

sequencing was being done and uploaded

to uh an academic Cloud called climb MRC

climb which is uh you know openstack

based on three different universities in

the UK and so instantly resources are

available you know like no one had to

sit around and say oh you know can we

rent some resources from here or borrow

some from here it's just like fine there

you go

um all available and then the public

health authorities

um like ukhsa we're using azure

um and you know everyone was just it was

just Cloud light loud you know in a

private public and there's no sitting

around you know waiting for to to buy

vasm at the storage was just like well

here's what we can right now and solve

these problems I know other countries uh

struggle because there's legislation

that says I don't know they can only um

if they were to use the cloud it can

only be within their country or within

one particular region so it kind of

makes things a bit more difficult

obviously Amazon Google and Azure uh

some Microsoft are doing a good job by

having data centers all around the world

but it is a problem and then when you

get done clinical stuff a lot of stuff

can't even leave like the hospital you

know something really sensitive data you

know you just can't cross that barrier

when out to Quantum we uh the building

had the

we shared with local hospital and

obviously research in hospital we had

two separate physical networks in one

building and one you know one kind of

server room as well you know two

separate physical networks because they

had to keep you know the clinical stuff

Toki separate from the research stuff

researchers seen as you know I've seen

more Lucy goosey and high risk than that

clinical stuff well I just fair enough

but anyway it hopefully with time people

will adopt a cloud a bit more

and before we end the last front or I

guess we talked about with the

historical Tech adoption now looking

forward I think you know you and I have

had a couple episodes on this but what

we're seeing is the big Tech adoption

that's really going to be changing the

field is you know machine learning and

Ai and how it's going to impact things

and we saw a little murmurs of it even

like with Pangolin there was the pangol

learn and it was like okay how are

people going to deal with this sort of

black box of decision making and it

wasn't taken to Kylie I mean it was

taken in its utility and practicality

and speed but it was definitely a huge

preference to the phylogenetic placement

because people could make sense of the

Usher uh approach versus machine

learning but it's going to be

interesting over the next couple years

where more of this adoption of machine

learning and AI in general there

um or more specifically I guess it is

going to be interesting to see how uh it

impacts our field I don't know if you

have a hot take in the last couple

minutes before we close out in Tech

adoption of AI and uh in pathogenomics

I honestly think it's going to change

things in ways we can't even comprehend

right now you know it'll just be there

it'll always be on it'll just be part of

what we do and I'm excited for the

future you know I'm excited to do less

work I'm I love now when I program that

uh it saves me so much time having to

type stuff out because you know like it

can you know pick up all the obvious

stuff that I I'm probably gonna do

yeah because we're already using it in

active writing active programming I'm

really interested to see how the big

tools kind of come in here because a lot

of the what we do is a lot of classic

categorization that seems well for the

taking for these kinds of Technologies

but I think it's going to be the policy

and Adoption of how do we

to what level of trust we put to this in

knowing that we can't really necessarily

just open up and understand how we came

to these decisions so

that's what keeps our jobs exciting

absolutely all right good episode we'll

uh continue on this conversation I'm

sure in future ones in the coming weeks