The document summarizes a talk on microbial agrogenomics. It discusses how genomics has advanced the understanding of plant-microbe interactions in several ways:
1) Genomes allow cataloging of genes and features in organisms and comparing common features between organisms that associate with phenotypes.
2) Multiple pathogen and plant genomes have enabled studying epidemiology through tracing historical origins, emergence in new areas, and host jumps.
3) Genomics provides diagnostic tools for precise pathogen identification needed for quarantine and legislation.
1. Microbial Agrogenomics
Where can it lead us?
Leighton Pritchard
Information and Computational Sciences
The James Hutton Institute
2. Acceptable Use Policy
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Recording of this talk, taking photos, discussing the content using
email, Twitter, blogs, etc. is permitted (and encouraged),
providing distraction to others during the presentation is minimised.
These slides will be available on SlideShare.
3. Table of Contents
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Introduction
Why Genomics?
2003-Now
Implications
Where Next?
Conclusions
4. The James Hutton Institute
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
5. Centres of Expertise
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
http://www.hutton.ac.uk
• Dundee Effector Consortium (DEC, with University of Dundee) [link]
• Centre for Research on Potato and Other Solanaceous Plants (CRPS) [link]
• Centre for Human and Animal Pathogens in the Environment (HAP-E) [link]
6. Plant-Pathogen Interactions
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Pathogens of barley (e.g. Rhynchosporium commune), and soft fruit
(e.g. Raspberry Leaf Blotch Virus (RLBV))
8. Issue 1: Food Security
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
• Economic cost and burden of crop disease
• P. infestans: e1bn Europe; $4bn global
• Societal impact (human health, commodity prices; farming)
• Emerging pathogens (JIT supply chain; climate change)
• Plant-associated human pathogens
• Food fraud
9. Issue 2: Environmental Sustainability
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
• Pesticide minimisation and withdrawal
• Durable resistance, soil-beneficial microbes, plant
growth/nutritional enhancement
• Traditional breeding, GM, or engineering?
• Soils: rhizosphere interactions/soil diversity
• Farming practices (water run-off, rotation, equipment-cleaning
- EU sulphuric acid ban)
10. Table of Contents
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Introduction
Why Genomics?
2003-Now
Implications
Where Next?
Conclusions
11. What Have Genomes Ever Done For Us?
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
• Catalogue features (genes, regulatory elements, etc.) in an
organism.
12. Plant-microbe interactions
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Gene products at the host-microbe interface
Dodds & Rathjen (2010) Nat. Rev. Genet. 11:539-548 doi:10.1038/nrg2812
13. Plant-Nematode Interactions
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
RNA-seq identification of 27 putative nematode effectors:
Small proteins, expressed in gland cells during feeding stage only.
Cotton et al. (2014) Genome Biol. 15:R43 doi:10.1186/gb-2014-15-3-r43
14. Plant Defence
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Prediction of NB-LRR genes (sequence capture).
Jupe et al. (2013) Plant J. 76:530-544 doi:10.1111/tpj.12307
Jupe et al. (2012) BMC Genomics 13:75 doi:10.1186/1471-2164-13-75
15. What Have Genomes Ever Done For Us?
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
• Catalogue features (genes, regulatory elements, etc.) in an
organism.
• If we have multiple genomes. . .
• What common features associate with phenotype or
environment?
16. Plant-microbe interactions
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
GWAS/QTLs/genotyping for plant breeding
http://ics.hutton.ac.uk/flapjack/
Milne et al. (2010) Bioinformatics 26:3133-3134 doi:10.1093/bioinformatics/btq580
17. Plant-microbe interactions
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Structural changes to genomes: repeat-driven expansion
duplication, mutation, recombination, epigenetic control of effectors . . .
Haas et al. (2009) Nature 461:393-398 doi:10.1038/nature08358
18. Plant-microbe interactions
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Structural changes to genomes: genome reductions
Buchnera, Serratia symbiotica - aphid symbionts, ‘random’ inactivation
Gil et al. (2002) Proc. Natl. Acad. Sci. USA 99:4454-4458 doi:10.1073/pnas.062067299
Burke & Moran (2011) Genome Biol. Evol. 99:4454-4458 doi:10.1093/gbe/evr002
19. Plant-microbe interactions
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Lateral gene transfer (virulence-associated genes)
Bell et al. (2004) Proc. Natl. Acad. Sci. 101:11105-11110 doi:10.1073/pnas.0402424101
21. What Have Genomes Ever Done For Us?
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
• Catalogue features (genes, regulatory elements, etc.) in an
organism.
• If we have multiple genomes. . .
• What common features associate with phenotype or
environment?
• Epidemiology: spread and transmission
22. Historical Origins
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Retracing 19th-century P.infestans pandemics
Yoshida et al. (2014) PLoS Pathog. 10:e1004028 doi:10.1371/journal.ppat.1004028
23. International Emergence
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Distribution of Dickeya spp. in Europe
• D.dianthicola; ◦ D.solani; Dickeya spp. on potato
Toth et al. (2011) Plant Pathol. 60:385-399 doi:10.1111/j.1365-3059.2011.02427.x
24. Host Jumps
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Movement of Dickeya from ornamental to crop plants
Parkinson et al. (2015) Eur. J. Plant Pathol. 141:63-70 doi:10.1007/s10658-014-0523-5
25. Diagnostic Tools
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Quarantine and legislation require precise identification.
Genomes enable rapid, robust RT-PCR diagnostics.
targets
V
IV
III
II
I
genomes
I
II
III
IV
V
https://github.com/widdowquinn/find differential primers
Pritchard et al. (2013) Plant Pathol. 62:587-596 doi:10.1111/j.1365-3059.2012.02678.x
Pritchard et al. (2012) PLoS One 7:e34498 doi:10.1371/journal.pone.0034498
26. Table of Contents
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Introduction
Why Genomics?
2003-Now
Implications
Where Next?
Conclusions
27. 2003: E. carotovora subsp. atroseptica
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
• £250k collaboration between SCRI, University of Cambridge,
WT Sanger Institute
• Single isolate: E. carotovora subsp. atroseptica SCRI1043
• First sequenced enterobacterial plant pathogen (32 authors!)
• Annotation: 6 people, for 6 months ≈ three person-years
• Result: single, complete 5Mbp circular chromosome (10.2X)
Bell et al. (2004) Proc. Natl. Acad. Sci. USA 101: 30:11105-11110. doi:10.1073/pnas.0402424101
28. 2003: E. carotovora subsp. atroseptica
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Compared against all 142 then-available bacterial genomes
Bell et al. (2004) Proc. Natl. Acad. Sci. USA 101: 30:11105-11110. doi:10.1073/pnas.0402424101
29. 2013: Dickeya spp.
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Sequenced and annotated 25 isolates of Dickeya over two years
• Multiple sequencing methods: 454, Illumina (SE, PE)
• Automated annotation, limited manual correction
• Results: 12-237 fragments: 4.2-5.1Mbp/genome (6-84X)
Pritchard et al. (2013) Genome Ann. 1 (4) doi:10.1128/genomeA.00087-12
Pritchard et al. (2013) Genome Ann. 1 (6) doi:10.1128/genomeA.00978-13
30. 2013: Dickeya spp.
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Whole genome-based species definitions: sp. nov. D. solani
van der Wolf et al. (2014) Int. J. Syst. Evol. Micr. 64:768-774 doi:10.1099/ijs.0.052944-0
34. 2014: Campylobacter spp.
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
≈1034 clinical, animal, food-associated Campylobacter isolates
• Illumina PE sequencing, cost ≈£60k
• Automated annotation: PRODIGAL
(w/ Ken Forbes, Norval Strachan, University of Aberdeen)
35. 2014: Campylobacter spp.
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
• 15554 ‘gene families’ in 1034 isolates.
• Calculation: 4e12 pairwise protein comparisons!
(w/ Ken Forbes, Norval Strachan, University of Aberdeen)
36. Table of Contents
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Introduction
Why Genomics?
2003-Now
Implications
Where Next?
Conclusions
37. So what’s changed?
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Everything.
• Cost: £250k → £60 per genome.
Now cheaper to sequence than analyse a genome!
Offload work from people to software.
• Location: sequencing centre, to benchtop (Nanopore!)
• Speed: sequencing run time can be less than a day
• Data: massive volume increase
38. Predicting the future is hard. . .
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Su et al. attempted to do it, though:
10,000 prokaryotes in 2015 was an underestimate.
http://sulab.org/2013/06/sequenced-genomes-per-year/
39. So what’s changed?
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Everything.
• Cost: £250k → £60 per genome.
• Location: sequencing centre, to benchtop (Nanopore!)
• Speed: sequencing run time can be less than a day
• Data: massive volume increase
More data ≈ better, but also more challenging.
• Software: more (= better. . .) software for more things
• New experiments: genomes, exomes, variant calling,
methylated sequences, STARR-seq, . . .
• New applications: diagnostics, epidemic tracking,
metagenomics, . . .
40. Sequence first. . . ask questions, later
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
• “Why?” has sometimes been replaced by “What?”
http://dilbert.com/strip/2000-01-03
“The thesis is not hypothesis driven. Add a hypothesis and refer to it in subsequent
chapters.”
41. More isn’t always better
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Deeper sequencing (more reads) = more information or better
assembly.
60-80X coverage the ‘sweet spot’ for bacterial genomes.
More reps more reads!
Conway & Bromage (2011) Bioinformatics 27:479-486 doi:10.1093/bioinformatics/btq697
42. Are database annotations reliable?
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Automated annotation is essential
The Critical Assessment of Function Annotation (CAFA) project.
Radivojac et al. (2013) Nat. Meth. 10:221-227 doi:10.1038/nmeth.2340
43. Do biased database annotations matter?
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Experimental annotations of proteins are incomplete. Is that
important?
Tested by simulation, and following databases for three years.
• Yes. It matters.
• Current large scale annotations are meaningful and almost surprisingly reliable.
• The nature and level of data incompleteness, and type of classification model
have an effect.
• “Low precision, high recall” (i.e. less discriminating) tools most significantly
affected.
Molecular function prediction is usually more reliable than
biological process prediction
Jiang et al. (2014) Bioinformatics 30:i609-i616 doi:10.1093/bioinformatics/btu472
Cozzetto et al. (2013) BMC Bioinf. 14:S3-S1 doi:10.1186/1471-2105-14-S3-S1
44. CAFA results
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
The Critical Assessment of Function Annotation (CAFA) 2013
results. (F-measure combines precision and recall)
• You can do better than
BLAST.
• Best-performing methods do
comparably well.
• Best methods used
evolutionary relationships,
structure, and expression
data.
• Machine Learning methods
work best.
Radivojac et al. (2013) Nat. Meth. 10:221-227 doi:10.1038/nmeth.2340
45. More Isn’t Always Better
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Statistical inference on large datasets requires extra care.
Hypothesis tests may incorrectly reject null hypotheses (B-H)
46. More Isn’t Always Better
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
• More tests → random effect seems ’real’
• May be considering a large set of inferences simultaneously
(and yet not notice!):“p-hacking”, “Researcher Degrees of
Freedom”
“good scientists are skilled at looking hard enough and subsequently coming up
with good stories (plausible even to themselves, as well as to their colleagues
and peer reviewers) to back up any statistically-significant comparisons they
happen to come up with.” Gelman & Loken (2013) ”The Garden of Forking Paths”
(“Data-dredging”)
True for all large data analyses: genomics, metabolomics,
proteomics, health screening, finding terrorists, etc.
Xia et al. (2012) Metabolomics 9:280-299 doi:10.1007/s11306-012-0482-9
Broadhurst & Kell (2006) Metabolomics 2:171-196 doi:10.1007/s11306-006-0037-z
47. Genome-Scale Predictions
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
• Imagine a paper describing a predictor for protein functional
class (e.g. pathogen effector)
• The paper reports sensitivity = 0.95, FPR = 0.01
• We run the predictor on 20,000 proteins in an organism
• It predicts 130 members of the class. How many of them are
likely to be true positives?
Pritchard & Broadhurst (2014) Meth. Mol. Biol. 9:280-299 doi:10.1007/978-1-62703-986-4 4
48. Genome-Scale Predictions
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
• Imagine a paper describing a predictor for protein functional
class (e.g. pathogen effector)
• The paper reports sensitivity = 0.95, FPR = 0.01
• We run the predictor on 20,000 proteins in an organism
• It predicts 130 members of the class. How many of them are
likely to be true positives?
• We need a baseline level of that class (fX ) in the genome to
determine this.
• Estimate ≈ 200 in gene complement, so fX = 0.01
• fX = 0.01 =⇒ P(class|+ve) = 0.490 ≈ 0.5: 65 TP
Pritchard & Broadhurst (2014) Meth. Mol. Biol. 9:280-299 doi:10.1007/978-1-62703-986-4 4
50. A Literature Example
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Reported sensitivity ≈ 0.71, FPR ≈ 0.15
Arnold et al. (2009) PLoS Pathog. 5:e1000376 doi:10.1371/journal.ppat.1000376
51. Big Data: New Problems
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
• Lots of high throughput experiments, and large datasets
(but even more small datasets)
• Historically ill-formed data (sequences in Word documents,
BLAST results pasted into notebooks).
• How do we connect all this data in a productive way?
This section influenced heavily by C. Titus Brown and Philip Bourne
52. Big Data: New Problems
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
• Data management. Too often:
“Goodbye to the student is goodbye to the data”
• Persistence of data resources (link rot, database entropy)
http://www.phdcomics.com/comics/archive.php?comicid=382
53. Big Data: New Problems
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
• How reproducible are computational results?
• Software/data versions prevent exact reproduction: 280h to
reproduce one paper approximately - in the same lab!
Garijo et al. (2013) PLoS One doi:10.1371/journal.pone.0080278
http://www.slideshare.net/pebourne/sib0114
54. Big Data: New Problems
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Maybe we can get away with all of this in a traditional model of
science publishing. . .
http://www.slideshare.net/c.titus.brown/2015-baltiandbioinformatics
55. Big Data: New Problems
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
. . .but lots of biological data doesn’t make sense except in the light
of other biological data.
http://www.slideshare.net/c.titus.brown/2015-baltiandbioinformatics
56. Big Data: New Solutions
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Everyone could be better off with collaboration and data sharing.
What is winning: career progression, or feeding people?
(still competing, but on analysis and insight, not on who holds what data. . .)
http://www.slideshare.net/c.titus.brown/2015-baltiandbioinformatics
57. Big Data: New Solutions
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Data quality ≈ data trust:
• Sustainable: storage, archiving, maintenance
• Findable: “where is the dataset?”, “is it available?”
• Queryable: “is X in the dataset?”
• Analysable: metadata, annotation
http://www.slideshare.net/pebourne/sib0114
58. Big Data: New Solutions
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Interoperable digital assets: datasets, software, lab books, etc.
• Uniquely identified (DOI, PMID, etc.)
• Provenance (version and access control)
• Open standards - what data to keep, how to organise it:
MINSEQE (sequencing), MIAME (microarray), MIASE (simulation), MIAPE
(proteomics), MIARE (RNAi), SBML, GFF3, SAM/BAM/CRAM, etc.
• Sustainable infrastructure for biological information
(ELIXIR, “The Commons” [US], RDF, Open Data)
http://www.slideshare.net/pebourne/sib0114
https://pebourne.wordpress.com/2014/10/07/the-commons/
59. Big Data: New Solutions
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Too much software is difficult to use for experts, or unusable for
non-experts.
Veretnik et al. (2008) PLoS Comp. Biol. doi:10.1371/journal.pcbi.1000136
60. Big Data: New Solutions
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Workflows, pipelines, and service integrative frameworks
Cock et al. (2014) Methods Mol. Biol. 1127:3-15 doi:10.1007/978-1-62703-986-4 1
Cock et al. (2013) PeerJ 1:e167 doi:10.7717/peerj.167
http://galaxy-community.org.uk/
61. Big Data: New Solutions
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Sometimes new software is needed.
Writing good software is difficult, and expensive.
http://www.theregister.co.uk/2015/01/22/us military finds f35 software is a buggy mess/
62. Big Data: New Solutions
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Not enough software engineers to go round: train what we have.
Programming literacy, computational thinking: versioned, readable,
maintainable code.
http://www.software.ac.uk/
http://software-carpentry.org/
http://datacarpentry.org/
63. Table of Contents
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Introduction
Why Genomics?
2003-Now
Implications
Where Next?
Conclusions
64. Cheap Sequencing In The Field
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Diagnostics and epidemic tracking by sequencing
Global Microbial Identifier (GMI) http://www.globalmicrobialidentifier.org:
Global system of databases for microbial/disease identification and diagnostics.
Quick et al. (2014) BMJ Open 11:e006278 doi:10.1136/bmjopen-2014-006278
65. Sequencing In The Field
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Live prediction for epidemiology?
(Peter Skelsey, JHI)
66. Sequence Isn’t Everything
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Organisms are dynamic, and multi-scale
• Context: epigenetics, tissue differentiation, mesoscale systems,
symbiosis, etc.
• Phenotypic plasticity: responses to environment - stress,
temperature, etc.
67. The Phytobiome
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Phytobiome: the plant, and its associated microbial community
• American Phytopathological Society “Phytobiomes Intitative”
• “a complete systems approach that spans foundational to applied
science focused on downstream application”
• We are not at war with all microbes. . .
https://www.apsnet.org/members/outreach/ppb/phytobiomes
68. Genomes Are Parts Lists
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
We know (some of) the bits that make up the machinery. . .
69. Flux Balance Analysis
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Flux Balance Analysis: constraint-based static representation of
metabolism (RNA/ChIP-seq adds dynamics to models)
• Set upper, lower bounds to reaction rate, define objective phenotype
(biomass, target flux profile)
• in silico knockouts; viable states; nutrient usage
• A basis for synthetic biology and engineering
70. Flux Balance Analysis
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Dickeya: 29 × FBA, host range ≈ nutrient-dependent growth
also transposon mutant libraries
(w/ Sonia Humphris, Ian Toth, JHI)
71. Plant-Microbe Interactions Are Systems
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Components, interactions, dynamics etc. = systems biology
Interaction creates a third system from host and microbe
Pritchard & Birch (2014) Mol. Plant. Pathol. 15:865-870 doi:10.1111/mpp.12210
Pritchard & Birch (2011) Plant Sci. 180:584-603 doi:10.1016/j.plantsci.2010.12.008
72. Plant-Microbe Interactions Are Systems
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Components, interactions, dynamics etc. = systems biology
Interaction creates a third system from host and microbe
microbe
(bulk)
microbe
(local)
PRR PRR*
R protein
R protein*
ø
øø
effector translocation
effector
(internalised)
PAMP
ø
ø
cell wall
microbe approaches cell microbe leaves cell/
is destroyed
microbe produces
PAMP
microbe produces
effector
PAMP binding
activates PRR
effector binding
activates R protein
callose
production
callose
loss
effector
loss
effector
loss
PAMP
loss
enhanced by callose (PTI)
and R protein* (ETI)
enhanced by
PRR* (PTI)
slowed by
callose (PTI)
callose
effector
(external)
enhanced by
effector action
No Response PTI
PTI+ETS PTI+ETS+ETI
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0 50 100 150 200 0 50 100 150 200
Time
Arbitraryunits
variable
Callose
Pathogen
Pathogen, Callose timecourses by host type
Pritchard & Birch (2014) Mol. Plant. Pathol. 15:865-870 doi:10.1111/mpp.12210
Pritchard & Birch (2011) Plant Sci. 180:584-603 doi:10.1016/j.plantsci.2010.12.008
73. Integrate Models and Data
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Integration of models and datasets still a challenge
• Models at different scales
• Kinetic, metabolomic, proteomic, transcriptomic, genomic
datasets
Hartmann & Schreiber (2014) Front. Bioeng. Biotechnol. 8:226-244 doi:10.3389/fbioe.2014.00091
74. Types of Model
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
• Combining data: models at different scales.
• Information required/produced depends on model type.
• Size/detail trade-off
Hartmann & Schreiber (2014) Front. Bioeng. Biotechnol. 8:226-244 doi:10.3389/fbioe.2014.00091/abstract
75. Synthetic Biology
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Engineering new response modes into crops.
Gurr & Rushton (2005) Trends Biotech. 23:283-290 doi:10.1016/j.tibtech.2005.04.009
76. Genome Editing
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
TALENs and CRISPR/Cas9s
http://www.lifetechnologies.com/
http://www.umassmed.edu/xuelab
77. Trait Stacking
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
For resistance and other beneficial traits (yield, nutrients, biofuels)
Vanholme et al. (2010) Trends Biotechnol. 28:543-547 doi:10.1016/j.tibtech.2010.07.008
78. Engineering Soil-Beneficial Microbes
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Refactoring of Klebsiella nitrogen fixation:
Temme et al. (2012) Proc. Natl. Acad. Sci. USA 10:763 doi:10.1073/pnas.1120788109
79. Engineering New Biology
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
dCas9 logic circuits, integrating with host regulation
Nielsen & Voigt (2014) Mol. Syst. Biol. 10:763 doi:10.15252/msb.20145735
80. Table of Contents
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Introduction
Why Genomics?
2003-Now
Implications
Where Next?
Conclusions
81. Data
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Sequencing is ever cheaper and more productive:
• Very large datasets
• More information (with good planning)
• Challenges for data storage and sharing
• Challenges for analysis (“why” vs. “what”)
• Challenges for software, accessibility (workflows,
multidisciplinary training)
• Interdisciplinary collaboration and data integration will
be essential
82. Systems/Synthetics
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
A parts list only gets us so far:
• Cells are dynamic biophysical systems
• Organisms are dynamic cellular systems
• ‘Real’ plant systems include the phytobiome
• Systems biology essential to understand plant-microbe
interactions
• Synthetic biology promises to be a powerful tool to improve
plant health, nutrition, etc.
• BUT: ethical issues around deployment of synthetic systems
92. Acknowledgements
Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
James Hutton Institute
Paul Birch
Emma Campbell
Peter Cock
Ingo Hein
Nicola Holden
Sonia Humphris
Florian Jupe
Ian Toth
NUI Galway
Florence Abram
Fiona Brennan
University of Aberdeen
Ken Forbes
Norval Strachan
University of Alberta
David Broadhurst
SASA
Vincent Mulholland
Gerry Saddler
Fera
Valerie Bertrand
John Elphinstone
Rachel Glover
Neil Parkinson
University of M¨unster
Martina Bielaszewska
Helge Karch
University of Salford
Natalie Ferry
Ryan Joynson
And many others!