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Microbial Agrogenomics 4/2/2015, UK-MX Workshop

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.

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Microbial Agrogenomics Where can it lead us? Leighton Pritchard Information and Computational Sciences The James Hutton Institute
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.
Table of Contents Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
The James Hutton Institute Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
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]
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))
Plant-Pathogen Interactions Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions Potato pathogens, pests, and vectors. • soft-rot bacteria (Dickeya, Pectobacterium, Erwinia) • blight (Phytophthora infestans) • Potato Cyst Nematode (PCN) (Globodera) • aphids (Myzus persicae)
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
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)
Table of Contents Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
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.
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
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
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
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?
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
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
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
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
Plant-microbe interactions Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions Closely-related bacteria, different host/environmental preference. Pectobacterium atrosepticum Holden et al. (2009) FEMS Micro. Rev. 33:689-703 doi:10.1111/j.1574-6976.2008.00153.x Toth et al. (2006) Annu. Rev. Phytopath. 44:305-336 doi:10.1146/annurev.phyto.44.070505.143444
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
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
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
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
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
Table of Contents Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
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
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
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
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
2013: Dickeya spp. Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions Differences in metabolic capacity (but ≈ 20% orphan EC activities)
2014: E. coli Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions Sequenced and annotated ≈ 190 isolates of E. coli All bacteria environmental, sampled from lysimeters • Illumina PE sequencing, cost ≈£11k • Automated annotation: PROKKA (w/ Fiona Brennan, Florence Abram, NUI Galway)
2014: E. coli Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions Whole genome-based subspecies classification Brunei20070942_contigs Muenster20063091_contigs Senftenberg20070885_contigs Lys142_contigs Lys175_contigs Lys130_contigs Lys170_contigs Lys126_contigs Lys167_contigs Lys176_contigs Lys169_contigs Lys50_contigs X5038_contigs Lys131_contigs Lys171_contigs Lys111_contigs Lys107_contigs Lys114_contigs Lys16_contigs Lys22_contigs Lys65_contigs Lys56_contigs Lys113_contigs Lys109_contigs Lys77_contigs Lys102_contigs Lys100_contigs Lys92_contigs Lys94_contigs Lys80_contigs Lys64_contigs Lys82_contigs AW3_contigs X5008_contigs AW4_contigs AW1_contigs Lys118_contigs Lys138_contigs Lys121_contigs Lys122_contigs Lys177_contigs Lys155_contigs Lys165_contigs Lys163_contigs Lys160_contigs Lys161_contigs Lys172_contigs Lys144_contigs Lys135_contigs Lys146_contigs Lys123_contigs Lys124_contigs Lys150_contigs Lys140_contigs Lys157_contigs Lys173_contigs Lys156_contigs Lys158_contigs Lys159_contigs Lys162_contigs Lys5_contigs X5084_contigs X5042_contigs Lys110_contigs Lys136_contigs Lys54_contigs Lys1_contigs Lys6_contigs Lys112_contigs X5012_contigs Lys30_contigs Lys25_contigs Lys43_contigs Lys37_contigs Lys40_contigs Lys151_contigs Lys31_contigs Lys27_contigs Lys42_contigs Lys51_contigs Lys33_contigs Lys46_contigs Lys38_contigs Lys89_contigs Lys23_contigs Lys115_contigs Lys108_contigs Lys104_contigs DSM10973_contigs Lys125_contigs Lys105_contigs Lys17_contigs Lys128_contigs Lys66_contigs Lys73_contigs Lys15_contigs Lys91_contigs DSM8698_contigs DSM8695_contigs Lys74_contigs Lys61_contigs Lys9_contigs Lys153_contigs Lys84_contigs Lys93_contigs Lys72_contigs Lys62_contigs Lys21_contigs Lys59_contigs Lys63_contigs Lys83_contigs Lys19_contigs Lys4_contigs AW13_contigs Lys45_contigs Lys28_contigs Lys53_contigs Lys52_contigs Lys34_contigs Lys36_contigs Lys24_contigs Lys35_contigs Lys68_contigs Lys106_contigs Lys88_contigs Lys97_contigs Lys76_contigs Lys134_contigs Lys58_contigs Lys71_contigs Lys81_contigs Lys129_contigs Lys120_contigs Lys145_contigs Lys137_contigs Lys127_contigs Lys152_contigs Lys101_contigs Lys98_contigs Lys70_contigs Lys133_contigs Lys47_contigs Lys75_contigs Lys48_contigs Lys148_contigs Lys139_contigs Lys141_contigs Lys164_contigs Lys149_contigs Lys147_contigs Lys60_contigs Lys79_contigs Lys168_contigs Lys18_contigs Lys87_contigs Lys96_contigs Lys7_contigs Lys154_contigs Lys117_contigs Lys119_contigs Lys178_contigs Lys116_contigs Lys86_contigs Lys90_contigs Lys41_contigs Lys13_contigs Lys85_contigs X5002_contigs Lys12_contigs Lys39_contigs Lys14_contigs Lys55_contigs Lys29_contigs Lys99_contigs X5035_contigs Lys8_contigs Lys3_contigs X5034_contigs X5088_contigs Lys20_contigs Lys78_contigs Lys11_contigs Brunei20070942_contigs Muenster20063091_contigs Senftenberg20070885_contigs Lys142_contigs Lys175_contigs Lys130_contigs Lys170_contigs Lys126_contigs Lys167_contigs Lys176_contigs Lys169_contigs Lys50_contigs 5038_contigs Lys131_contigs Lys171_contigs Lys111_contigs Lys107_contigs Lys114_contigs Lys16_contigs Lys22_contigs Lys65_contigs Lys56_contigs Lys113_contigs Lys109_contigs Lys77_contigs Lys102_contigs Lys100_contigs Lys92_contigs Lys94_contigs Lys80_contigs Lys64_contigs Lys82_contigs AW3_contigs 5008_contigs AW4_contigs AW1_contigs Lys118_contigs Lys138_contigs Lys121_contigs Lys122_contigs Lys177_contigs Lys155_contigs Lys165_contigs Lys163_contigs Lys160_contigs Lys161_contigs Lys172_contigs Lys144_contigs Lys135_contigs Lys146_contigs Lys123_contigs Lys124_contigs Lys150_contigs Lys140_contigs Lys157_contigs Lys173_contigs Lys156_contigs Lys158_contigs Lys159_contigs Lys162_contigs Lys5_contigs 5084_contigs 5042_contigs Lys110_contigs Lys136_contigs Lys54_contigs Lys1_contigs Lys6_contigs Lys112_contigs 5012_contigs Lys30_contigs Lys25_contigs Lys43_contigs Lys37_contigs Lys40_contigs Lys151_contigs Lys31_contigs Lys27_contigs Lys42_contigs Lys51_contigs Lys33_contigs Lys46_contigs Lys38_contigs Lys89_contigs Lys23_contigs Lys115_contigs Lys108_contigs Lys104_contigs DSM10973_contigs Lys125_contigs Lys105_contigs Lys17_contigs Lys128_contigs Lys66_contigs Lys73_contigs Lys15_contigs Lys91_contigs DSM8698_contigs DSM8695_contigs Lys74_contigs Lys61_contigs Lys9_contigs Lys153_contigs Lys84_contigs Lys93_contigs Lys72_contigs Lys62_contigs Lys21_contigs Lys59_contigs Lys63_contigs Lys83_contigs Lys19_contigs Lys4_contigs AW13_contigs Lys45_contigs Lys28_contigs Lys53_contigs Lys52_contigs Lys34_contigs Lys36_contigs Lys24_contigs Lys35_contigs Lys68_contigs Lys106_contigs Lys88_contigs Lys97_contigs Lys76_contigs Lys134_contigs Lys58_contigs Lys71_contigs Lys81_contigs Lys129_contigs Lys120_contigs Lys145_contigs Lys137_contigs Lys127_contigs Lys152_contigs Lys101_contigs Lys98_contigs Lys70_contigs Lys133_contigs Lys47_contigs Lys75_contigs Lys48_contigs Lys148_contigs Lys139_contigs Lys141_contigs Lys164_contigs Lys149_contigs Lys147_contigs Lys60_contigs Lys79_contigs Lys168_contigs Lys18_contigs Lys87_contigs Lys96_contigs Lys7_contigs Lys154_contigs Lys117_contigs Lys119_contigs Lys178_contigs Lys116_contigs Lys86_contigs Lys90_contigs Lys41_contigs Lys13_contigs Lys85_contigs 5002_contigs Lys12_contigs Lys39_contigs Lys14_contigs Lys55_contigs Lys29_contigs Lys99_contigs 5035_contigs Lys8_contigs Lys3_contigs 5034_contigs 5088_contigs Lys20_contigs Lys78_contigs Lys11_contigs ANIm 0.9 0.92 0.94 0.96 0.98 Value 0100020003000400050006000 Color Key and Histogram Count A B1 B2 C D E F U X (w/ Fiona Brennan, Florence Abram, NUI Galway)
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)
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)
Table of Contents Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
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
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/
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, . . .
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.”
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
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
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
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
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)
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
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
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
Baserate Fallacy Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions http://bit.ly/1EFbzCI http://armchairbiology.blogspot.co.uk/2014/07/the-baserate-fallacy-revisited.html
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
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
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
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
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
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
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
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
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/
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
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/
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/
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/
Table of Contents Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
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
Sequencing In The Field Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions Live prediction for epidemiology? (Peter Skelsey, JHI)
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.
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
Genomes Are Parts Lists Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions We know (some of) the bits that make up the machinery. . .
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
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)
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
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
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
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
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
Genome Editing Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions TALENs and CRISPR/Cas9s http://www.lifetechnologies.com/ http://www.umassmed.edu/xuelab
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
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
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
Table of Contents Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
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
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
Conclusions Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Conclusions Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Conclusions Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Conclusions Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Conclusions Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Conclusions Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Conclusions Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Conclusions Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
Conclusions Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
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!

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Microbial Agrogenomics 4/2/2015, UK-MX Workshop

  • 1.
    Microbial Agrogenomics Where canit lead us? Leighton Pritchard Information and Computational Sciences The James Hutton Institute
  • 2.
    Acceptable Use Policy IntroductionWhy 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 IntroductionWhy Genomics? 2003-Now Implications Where Next? Conclusions Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
  • 4.
    The James HuttonInstitute Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
  • 5.
    Centres of Expertise IntroductionWhy 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 WhyGenomics? 2003-Now Implications Where Next? Conclusions Pathogens of barley (e.g. Rhynchosporium commune), and soft fruit (e.g. Raspberry Leaf Blotch Virus (RLBV))
  • 7.
    Plant-Pathogen Interactions Introduction WhyGenomics? 2003-Now Implications Where Next? Conclusions Potato pathogens, pests, and vectors. • soft-rot bacteria (Dickeya, Pectobacterium, Erwinia) • blight (Phytophthora infestans) • Potato Cyst Nematode (PCN) (Globodera) • aphids (Myzus persicae)
  • 8.
    Issue 1: FoodSecurity 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: EnvironmentalSustainability 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 IntroductionWhy Genomics? 2003-Now Implications Where Next? Conclusions Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
  • 11.
    What Have GenomesEver 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 WhyGenomics? 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 WhyGenomics? 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 WhyGenomics? 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 GenomesEver 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 WhyGenomics? 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 WhyGenomics? 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 WhyGenomics? 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 WhyGenomics? 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
  • 20.
    Plant-microbe interactions Introduction WhyGenomics? 2003-Now Implications Where Next? Conclusions Closely-related bacteria, different host/environmental preference. Pectobacterium atrosepticum Holden et al. (2009) FEMS Micro. Rev. 33:689-703 doi:10.1111/j.1574-6976.2008.00153.x Toth et al. (2006) Annu. Rev. Phytopath. 44:305-336 doi:10.1146/annurev.phyto.44.070505.143444
  • 21.
    What Have GenomesEver 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 WhyGenomics? 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 WhyGenomics? 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 WhyGenomics? 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 WhyGenomics? 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 IntroductionWhy Genomics? 2003-Now Implications Where Next? Conclusions Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
  • 27.
    2003: E. carotovorasubsp. 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. carotovorasubsp. 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. IntroductionWhy 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. IntroductionWhy 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
  • 31.
    2013: Dickeya spp. IntroductionWhy Genomics? 2003-Now Implications Where Next? Conclusions Differences in metabolic capacity (but ≈ 20% orphan EC activities)
  • 32.
    2014: E. coli IntroductionWhy Genomics? 2003-Now Implications Where Next? Conclusions Sequenced and annotated ≈ 190 isolates of E. coli All bacteria environmental, sampled from lysimeters • Illumina PE sequencing, cost ≈£11k • Automated annotation: PROKKA (w/ Fiona Brennan, Florence Abram, NUI Galway)
  • 33.
    2014: E. coli IntroductionWhy Genomics? 2003-Now Implications Where Next? Conclusions Whole genome-based subspecies classification Brunei20070942_contigs Muenster20063091_contigs Senftenberg20070885_contigs Lys142_contigs Lys175_contigs Lys130_contigs Lys170_contigs Lys126_contigs Lys167_contigs Lys176_contigs Lys169_contigs Lys50_contigs X5038_contigs Lys131_contigs Lys171_contigs Lys111_contigs Lys107_contigs Lys114_contigs Lys16_contigs Lys22_contigs Lys65_contigs Lys56_contigs Lys113_contigs Lys109_contigs Lys77_contigs Lys102_contigs Lys100_contigs Lys92_contigs Lys94_contigs Lys80_contigs Lys64_contigs Lys82_contigs AW3_contigs X5008_contigs AW4_contigs AW1_contigs Lys118_contigs Lys138_contigs Lys121_contigs Lys122_contigs Lys177_contigs Lys155_contigs Lys165_contigs Lys163_contigs Lys160_contigs Lys161_contigs Lys172_contigs Lys144_contigs Lys135_contigs Lys146_contigs Lys123_contigs Lys124_contigs Lys150_contigs Lys140_contigs Lys157_contigs Lys173_contigs Lys156_contigs Lys158_contigs Lys159_contigs Lys162_contigs Lys5_contigs X5084_contigs X5042_contigs Lys110_contigs Lys136_contigs Lys54_contigs Lys1_contigs Lys6_contigs Lys112_contigs X5012_contigs Lys30_contigs Lys25_contigs Lys43_contigs Lys37_contigs Lys40_contigs Lys151_contigs Lys31_contigs Lys27_contigs Lys42_contigs Lys51_contigs Lys33_contigs Lys46_contigs Lys38_contigs Lys89_contigs Lys23_contigs Lys115_contigs Lys108_contigs Lys104_contigs DSM10973_contigs Lys125_contigs Lys105_contigs Lys17_contigs Lys128_contigs Lys66_contigs Lys73_contigs Lys15_contigs Lys91_contigs DSM8698_contigs DSM8695_contigs Lys74_contigs Lys61_contigs Lys9_contigs Lys153_contigs Lys84_contigs Lys93_contigs Lys72_contigs Lys62_contigs Lys21_contigs Lys59_contigs Lys63_contigs Lys83_contigs Lys19_contigs Lys4_contigs AW13_contigs Lys45_contigs Lys28_contigs Lys53_contigs Lys52_contigs Lys34_contigs Lys36_contigs Lys24_contigs Lys35_contigs Lys68_contigs Lys106_contigs Lys88_contigs Lys97_contigs Lys76_contigs Lys134_contigs Lys58_contigs Lys71_contigs Lys81_contigs Lys129_contigs Lys120_contigs Lys145_contigs Lys137_contigs Lys127_contigs Lys152_contigs Lys101_contigs Lys98_contigs Lys70_contigs Lys133_contigs Lys47_contigs Lys75_contigs Lys48_contigs Lys148_contigs Lys139_contigs Lys141_contigs Lys164_contigs Lys149_contigs Lys147_contigs Lys60_contigs Lys79_contigs Lys168_contigs Lys18_contigs Lys87_contigs Lys96_contigs Lys7_contigs Lys154_contigs Lys117_contigs Lys119_contigs Lys178_contigs Lys116_contigs Lys86_contigs Lys90_contigs Lys41_contigs Lys13_contigs Lys85_contigs X5002_contigs Lys12_contigs Lys39_contigs Lys14_contigs Lys55_contigs Lys29_contigs Lys99_contigs X5035_contigs Lys8_contigs Lys3_contigs X5034_contigs X5088_contigs Lys20_contigs Lys78_contigs Lys11_contigs Brunei20070942_contigs Muenster20063091_contigs Senftenberg20070885_contigs Lys142_contigs Lys175_contigs Lys130_contigs Lys170_contigs Lys126_contigs Lys167_contigs Lys176_contigs Lys169_contigs Lys50_contigs 5038_contigs Lys131_contigs Lys171_contigs Lys111_contigs Lys107_contigs Lys114_contigs Lys16_contigs Lys22_contigs Lys65_contigs Lys56_contigs Lys113_contigs Lys109_contigs Lys77_contigs Lys102_contigs Lys100_contigs Lys92_contigs Lys94_contigs Lys80_contigs Lys64_contigs Lys82_contigs AW3_contigs 5008_contigs AW4_contigs AW1_contigs Lys118_contigs Lys138_contigs Lys121_contigs Lys122_contigs Lys177_contigs Lys155_contigs Lys165_contigs Lys163_contigs Lys160_contigs Lys161_contigs Lys172_contigs Lys144_contigs Lys135_contigs Lys146_contigs Lys123_contigs Lys124_contigs Lys150_contigs Lys140_contigs Lys157_contigs Lys173_contigs Lys156_contigs Lys158_contigs Lys159_contigs Lys162_contigs Lys5_contigs 5084_contigs 5042_contigs Lys110_contigs Lys136_contigs Lys54_contigs Lys1_contigs Lys6_contigs Lys112_contigs 5012_contigs Lys30_contigs Lys25_contigs Lys43_contigs Lys37_contigs Lys40_contigs Lys151_contigs Lys31_contigs Lys27_contigs Lys42_contigs Lys51_contigs Lys33_contigs Lys46_contigs Lys38_contigs Lys89_contigs Lys23_contigs Lys115_contigs Lys108_contigs Lys104_contigs DSM10973_contigs Lys125_contigs Lys105_contigs Lys17_contigs Lys128_contigs Lys66_contigs Lys73_contigs Lys15_contigs Lys91_contigs DSM8698_contigs DSM8695_contigs Lys74_contigs Lys61_contigs Lys9_contigs Lys153_contigs Lys84_contigs Lys93_contigs Lys72_contigs Lys62_contigs Lys21_contigs Lys59_contigs Lys63_contigs Lys83_contigs Lys19_contigs Lys4_contigs AW13_contigs Lys45_contigs Lys28_contigs Lys53_contigs Lys52_contigs Lys34_contigs Lys36_contigs Lys24_contigs Lys35_contigs Lys68_contigs Lys106_contigs Lys88_contigs Lys97_contigs Lys76_contigs Lys134_contigs Lys58_contigs Lys71_contigs Lys81_contigs Lys129_contigs Lys120_contigs Lys145_contigs Lys137_contigs Lys127_contigs Lys152_contigs Lys101_contigs Lys98_contigs Lys70_contigs Lys133_contigs Lys47_contigs Lys75_contigs Lys48_contigs Lys148_contigs Lys139_contigs Lys141_contigs Lys164_contigs Lys149_contigs Lys147_contigs Lys60_contigs Lys79_contigs Lys168_contigs Lys18_contigs Lys87_contigs Lys96_contigs Lys7_contigs Lys154_contigs Lys117_contigs Lys119_contigs Lys178_contigs Lys116_contigs Lys86_contigs Lys90_contigs Lys41_contigs Lys13_contigs Lys85_contigs 5002_contigs Lys12_contigs Lys39_contigs Lys14_contigs Lys55_contigs Lys29_contigs Lys99_contigs 5035_contigs Lys8_contigs Lys3_contigs 5034_contigs 5088_contigs Lys20_contigs Lys78_contigs Lys11_contigs ANIm 0.9 0.92 0.94 0.96 0.98 Value 0100020003000400050006000 Color Key and Histogram Count A B1 B2 C D E F U X (w/ Fiona Brennan, Florence Abram, NUI Galway)
  • 34.
    2014: Campylobacter spp. IntroductionWhy 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. IntroductionWhy 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 IntroductionWhy Genomics? 2003-Now Implications Where Next? Conclusions Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
  • 37.
    So what’s changed? IntroductionWhy 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 futureis 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? IntroductionWhy 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 alwaysbetter 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 annotationsreliable? 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 databaseannotations 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 WhyGenomics? 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 AlwaysBetter 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 AlwaysBetter 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 WhyGenomics? 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 WhyGenomics? 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
  • 49.
    Baserate Fallacy Introduction WhyGenomics? 2003-Now Implications Where Next? Conclusions http://bit.ly/1EFbzCI http://armchairbiology.blogspot.co.uk/2014/07/the-baserate-fallacy-revisited.html
  • 50.
    A Literature Example IntroductionWhy 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: NewProblems 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: NewProblems 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: NewProblems 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: NewProblems 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: NewProblems 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: NewSolutions 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: NewSolutions 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: NewSolutions 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: NewSolutions 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: NewSolutions 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: NewSolutions 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: NewSolutions 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 IntroductionWhy Genomics? 2003-Now Implications Where Next? Conclusions Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions
  • 64.
    Cheap Sequencing InThe 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 TheField Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions Live prediction for epidemiology? (Peter Skelsey, JHI)
  • 66.
    Sequence Isn’t Everything IntroductionWhy 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 WhyGenomics? 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 PartsLists Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions We know (some of) the bits that make up the machinery. . .
  • 69.
    Flux Balance Analysis IntroductionWhy 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 IntroductionWhy 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 AreSystems 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 AreSystems 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 andData 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 IntroductionWhy 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 WhyGenomics? 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 WhyGenomics? 2003-Now Implications Where Next? Conclusions TALENs and CRISPR/Cas9s http://www.lifetechnologies.com/ http://www.umassmed.edu/xuelab
  • 77.
    Trait Stacking Introduction WhyGenomics? 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 IntroductionWhy 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 IntroductionWhy 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 IntroductionWhy 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
  • 83.
    Conclusions Introduction Why Genomics?2003-Now Implications Where Next? Conclusions
  • 84.
    Conclusions Introduction Why Genomics?2003-Now Implications Where Next? Conclusions
  • 85.
    Conclusions Introduction Why Genomics?2003-Now Implications Where Next? Conclusions
  • 86.
    Conclusions Introduction Why Genomics?2003-Now Implications Where Next? Conclusions
  • 87.
    Conclusions Introduction Why Genomics?2003-Now Implications Where Next? Conclusions
  • 88.
    Conclusions Introduction Why Genomics?2003-Now Implications Where Next? Conclusions
  • 89.
    Conclusions Introduction Why Genomics?2003-Now Implications Where Next? Conclusions
  • 90.
    Conclusions Introduction Why Genomics?2003-Now Implications Where Next? Conclusions
  • 91.
    Conclusions Introduction Why Genomics?2003-Now Implications Where Next? Conclusions
  • 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!