My talk from the IBMS Congress 2019, outlining key challenges and advice, based on our experience, for people who want to implement pathogen genomics services.

1. Implementing Pathogen Genomics
Dr Tom Connor
Bioinformatics Lead Public Health Wales,
Reader, Cardiff University and Group Leader Quadram
Institute
Clostpath 2019
Supported by

2. Conflict of Interest
No Conflicts to declare!
Funding Sources
MRC CLIMB
Welsh Government
Public Health Wales NHS
Trust
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Microbes in the Food Chain

3. Acknowledgements Other PHW Colleagues
Dr Matt Backx
Dr Catherine Moore
Trefor Morris
Michael Perry
Dr Harriet Hughes
Dr Noel Craine
Dr Simon Cottrell
Helen Adams
David Heyburn
Fatima Downing
Sue Edwards
Cardiff University
Dr Matt Bull*
Dr Anna Price
*now joined my
bioinformatics
team in PHW
PHW, Pathogen Genomics
Dr Sally Corden
Joanne Watkins
Lee Graham
Alec Birchley
Bree Wilcox
Jason Coombes
Lauren Gilbert
Luke Turner
PHW Genomics
Workstreams
(ARGENT, DIGEST,
WCM, HIV, Influenza)

4. Where we are in Wales
• In July 2017 the Welsh Government launched the
Genomics for Precision Medicine Strategy, with over
£10M spent so far
• PHW is leading the Pathogen Genomics elements,
with a Pathogen Genomics Unit launched in 2018
• Current development areas
– AMR bacterial surveillance and characterisation
– Cystic Fibrosis polymicrobial infection
diagnostics
– Enterovirus surveillance
• Production systems
– C. difficile surveillance and outbreak support
– Mycobacteria identification and characterisation
– Influenza surveillance
– HIV susceptibility testing
Accreditation in process
System design/needs assessment in
progress
Pilot system development

5. Genomics has massive potential
High throughput =
Faster turnaround
Times
Whole Genome = More
tests possible
simultaneously
Common data types =
Diagnostic data can be used
for surveillance in real time
Unambiguous
= clearer
diagnostics

6. The (clinical) sequencing process has 5 main elements
1. Sample to Sequencer
2. Sequencing
3. Reads to reports
4. Interpretation
5. Fixing it when it breaks
In contrast to a research lab, in a clinical lab we have
specialists at each point, and reproducibility
(time/elements/performance) is critical

7. However, the major costs and difficulties do not lie with
the generation of data, they lie with how we share, store
and analyse the data we generate
Bioinformatics expertise
User accessibility of software/hardware
Appropriate compute capacity
Software development
Storage availability
Network capacity
Sequencing is now relatively cheap and easy ; we can
sequence large numbers of strains for modest amounts
of money
These
account
for up to
90%
of the
costs of
doing
genomics
work
Chances are, your organisation has lab skills, but bioinformatics is
a new area. Challenge 1: The sequencing iceberg.

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Why the complexity?
Next generation sequencing involves
taking many copies of a genome and
smashing it up into millions of small
fragments that can be sequenced
using a sequencing instrument
We then have to put this back together
Think of it as a huge, complex jigsaw
puzzle, with most pieces being sky,
and a number of pieces being from
other puzzles
And, unlike in human genomics, we
often don’t know what the original
‘puzzle’ is meant to look like
Once we have rebuilt the puzzle, we
will have a range of questions we
want to ask
These questions require different
approaches to find the answers we
need
And microbial bioinformatics best
practice doesn’t exist yet

9. • Genomic epidemiology
– Outbreak Analysis
– Genomic Surveillance
• Analysis and interpretation
– Bespoke
• Fungal
• Outbreak characterisation
• Reference isolate characterisation
– Routine
• AMR
• Bacterial
• Viral
• Software development
• Pipeline design and development
• Pipeline/software validation and verification
testing
• IT Infrastructure design, development and
maintenance
• Systems Engineering
• Data sharing
Our solution: an interdisciplinary ‘bioinformatics’ team
STP specialisation: Applied
Epidemiology
STP specialisation:
Microbiology
STP specialisation:
Bioinformatics
Enterprise IT/High
Performance Computing

10. Challenge 2:Troubleshooting, there is a lot that can go wrong
• Wrong organism cultured
• Contamination of organism in
culturing lab
• More than one
strain/species/whatever in culture
• Contamination in non-cultured
organisms
• Extraction failure
• Contamination during extraction
• Library preparation (failure)
• Library pooling (failure)
• Library/barcode mixup
• Issues with quantification (at any
stage)
• Contamination of Library prep
reagents
• Contamination of library prep lab
• Mistakes/issues with
fragmentation and library cleanup
• Carryover during sequencing
• Incorrect details on sample sheet
• Sequencer errors/problems
• Software bug
• Hardware issue introducing error
• Hardware failures
• Data degradation
• Database problems
• Managing issues from dealing with
research systems
Almost every one of these issues will only become
apparent after sequencing

11. There is no magic way to stop things going wrong
• Doing the preparation to build these
services is very important (was ~2
years of work to get the first service up)
• Also, automating wherever possible has
been really helpful
• There will still be issues
• Important to involve everyone in
identifying issues
• With the right team and approach these
things can be dealt with
• Requires close co-working of all staff
(lab-based, computer-based and
clinical)
• Requires all staff to understand, to
some level, the processes going on

12. Challenge 3: working out where you want to use genomics
• May seem a little simple, but
making the choice of where
you will deploy genomics is
hard
• Everyone will want to use it
for their thing
• It may not be the most
appropriate tool
• Requires a lot of work, and
strong engagement from
clinical colleagues
• Also requires a clear idea of
what you want genomics to
do, and what it is replacing

13. How genomics is making a difference for us
• HIV: Faster, cheaper, more
information
• TB/NTMs: unambiguous, cheaper and
faster than previous methods,
allowing surveillance/public health
activities to happen at the same time
as treatment
• C. difficile: being able to identify when
something isn’t an outbreak, and
being able to track spread across the
healthcare system
• Influenza: real time surveillance in-
season, real time response

14. What it looks like when it works - Influenza
• Identified a switch from Sanger to NGS for influenza
would be beneficial
• Proposal for a pilot study to Welsh government in
October 2017
• Full system operational in 2018/19 season
• Enabled us to achieve a turnaround time of 7 days for
most samples through the season
• Meant that we could start to perform in-season
surveillance and look at PH interventions in response
to changes in the influenza population
• Also enabled us to do focused outbreak tracking
• Is an iterative process, and has been improved for this
year
• Demonstrates nicely how once everything is in place,
you can go from standing start to full clinical service
rapidly

15. What it looks like when it works – C. difficile
• Since 2017 we have been working to produce a service for C. difficile outbreak support
• Now entering parallel running
• Linking genomics and Public Health enables patient-level examination of causes and
prevention measures

16. What it looks like when it works – C. difficile
As part of preliminary work we looked
at data from North Wales
In North Wales 11/18 clusters crossed
hospitals
Pattern has continued following scale
up
Large clusters are observed for multiple
ribotypes including 002, 078 and 014

17. Advice: before you begin
• Understand that genomics is interdisciplinary
• Understand that you need a team pulling together
with skills and expertise across all of the key areas
• Understand you will make a lot of mistakes before
you get things to work
• Understand that the bioinformatics will be
disproportionately important
• Set ground rules/standards/aims at the start
• Do the maths on costs and what throughput you
need
• Recruit/consult your bioinformaticians before you
order kit
• Get the structure right, and get staff bought in early
• If you are a BMS: look at how you might upskill
yourself to better understand the new approaches
being used

18. Key gotchas to look out for
• Mission creep
• Costings/pricing
• Research equipment in clinical service
• Expecting the world
• Sensible costings/expectations
• Not engaging with staff
• Poor IT planning
• Inadequate testing of lab work elements
• Lack of tools for troubleshooting
• Not having a clear idea of what is wanted

19. Final Advice to staff
• To those planning a service
– Your staff are your foundation, and should
be a team
– Know what success looks like
– Know what you want
– Make sure you invest adequately
– Don’t underestimate bioinformatics needs
• To those who may be part of a service:
– Don’t think of a move towards digital data
as being de-skilling or somehow a threat
– Upskill yourself
– Try to understand the processes going on;
you are still a scientist
– Bioinformatics provides new opportunities,
it should be part of your career
development

20. Other PHW Colleagues
Dr Matt Backx
Dr Catherine Moore
Trefor Morris
Michael Perry
Dr Harriet Hughes
Dr Noel Craine
Dr Simon Cottrell
Helen Adams
David Heyburn
Fatima Downing
Sue Edwards
Cardiff University
Dr Matt Bull*
Dr Anna Price
*now joined my
bioinformatics
team in PHW
PHW, Pathogen Genomics
Dr Sally Corden
Joanne Watkins
Lee Graham
Alec Birchley
Bree Wilcox
Jason Coombes
Lauren Gilbert
Luke Turner
PHW Genomics
Workstreams
(ARGENT, DIGEST,
WCM, HIV, Influenza)
Acknowledgements