Real-time Phylogenomics: Joe Parker
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Talk given at a technology/informatics company, London, Feb 2018.
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Real-time Phylogenomics: Joe Parker
- 1. Real-time phylogenomics or ‘Some interesting problems in genomic big data’ Joe Parker Early-career Research Fellow, RBG Kew @lonelyjoeparker: joe.parker@kew.org
- 2. Incredible times for bioscience Images – Wikimedia commons CC BY-SA (clockwise from top left: Jeroen Rouwkema, @aGastya, author’s own, @RE73)
- 3. Background
- 4. MENU Some definitions Adventures with a genome sequencer Evolution is complex Real-time data & mass sequencing Final thoughts: the cosmology of life
- 6. Genes and genomes >ENA|AY819028|AY819028.1 Capsicum annuum cultivar Hot 1493 acyltransferase (Pun1) gene, complete cds. TCATTAGAAGGTCATACCGCTCCACGAAAATGCACCTTGAAAGATATAACACGGACAACGAATCATTATCCCCATCATCACTATTACTCCCACTTCC CTTGCACTCTTCACTGTCACCACTGACACTCCGCTTGGCAACATTTTCACTAGAATCGACGTAGTCGCTTATCTCCTTTAACTCCGAATCTGATTCG GACACCGACTGTTCAATTTTCTTTCTTTTTGAGACTTTTTCAACTGCTTCAGTTCTTCTTTTTCTACTGTTACCAGCGGTACCGGCTTTGCGTTTAGG AATGATGTTTTTTTTACCCATTTTCAACACAATCTACACCTAAAGAACAAATCTCCCATTTTTAGTTCATAGACCACAAGTCTATCAACAGAAATAACT CAATGCTCAAATGAACCCCCCTCCCCCCAAAAAAAAATTAACAAACACCCCACCATTAAACAGTTCACTACACAAACATACAATAACTGAACCAAAAT CCAACATGCAATATCAAAACACAACAATTACTAAAATCAAACTAATGCACCTAATCAAACTAATTAGCTATTAATATTTCAATTTTCACTATTTCAGCAA TCATGTTTTAAAAGAATTTCATACGTCTGAAAATTGATATATATCTAGGGCATTCTCATTTCATAGACCACGGGTCTACGGATAGACCTCGGGTCTAC GAACAGAAATAGGTCGCTGTTCAAATCAAAATGCCAAAATAACTCTTCAAACAACTATTATCCCACCATTCAACACTTCGTTGCTAAATAAACCACAA CTAAACCAAAACACCAAATTCGAAGAAAAAATTTCTACATCACTACGAGTTGATTAGCAAAAAAAAACGTTTAAATGGATCTAGAAATGATCGAAACT TGATTTTAACTAACCTTGCAAAGCAGCAACAACCCCTTAGTAGCTGGAGAAGAAGACGAAATGAAAATGGCATTTTTGGAAGAAGTAGTTTCAAAAG CAGGAGTTGGGAATTGAAGAGGAGAGAGAGGGTGGGTTTTTTTAAATATTGGAATAATTGGAGGGTGTTAGGTGTATTATATTAAATTTGTAAAGTT GTAAAAATGATGAATTGGTCCCTTGGCCGATGCGTGGGCCCCACTTTTTCATAAAAAATAAATCAAAAAGAAATTAAGTAGGTATTTGACAAATTAAT TTTGGAGGGTTCCTTCTTTGCCAATTATTCCCCACTAAGCTACTCTCATTCACTCTTATATTATAGATTATAGTATAAAGTAATACAAACTATGAATTG TTTTTATATTTTATTTTACAAGTTATGAATAGTGTTTATATAGGTCTCTATTTCCATACAATCACATTTTGTGGGCAGTTTTTTTGGGATTGTCACGAAG GCGAGGTTTGTTCATTTTGTGGAAAGAGAATTGGATTTCTACATTTTTATCATCTTCTAGGTGTGATGTTGATACTACTATTTGCCCAAATATTTGTTT TAAACATATTAATATTATGTATCAAAATGTGTACAATATAATTTAACACACGTGCAGTATGCATGTATCGCGAAACTAGTTAATTACATGCATCACATG TAATAGCAATAGTATTATTGTACGACGTACTAATATATTAGTATCTATTCTAGCTACTAATTTCCTCTTAACCGTCTCCATGCTGAAAACAACGCCACA GTGCAACGAGCCTTCTATAAAAGTTGAATTATATAAAAATAAGGTACAGTTTAGAAATAAAACTAACAAAAAGGTAACCTATAGTTTGGGGGTTGGGT AGAGGTTGTTTAGCCAGTAACTCTATTATTTCATTTCCTTTTGTCTATATAAGTGTATCCATATATGCAAGAAATGTCAACCGGCCAGCAGCATATAT TTATTTGTTAAATTAATTATGGCTTTTGCATTACCATCATCACTTGTTTCAGTTTGTAACAAATCTTTTATCAAACCTTCCTCTCTCACCCCCTCTACAC TTAGATTTCACAAGCTATCTTTCATCGATCAATCTTTAAGTAATATGTATATCCCTTGTGCATTTTTTTACCCTAAAGTACAACAAAGACTAGAAGACT CCAAAAATTCTGATGAGCTTTCCCATATAGCCCACTTGCTACAAACATCTCTATCACAAACTCTAGTCTCTTACTATCCTTATGCTGGAAAGTTGAAG GACAATGCTACTGTTGACTGTAACGATATGGGAGCTGAGTTCTTGAGTGTTCGAATAAAATGTTCCATGTCTGAAATTCTTGATCATCCTCATGCAT CTCTTGCAGAGAGCATAGTTTTGCCCAAGGATTTGCCTTGGGCGAATAATTGTGAAGGTGGTAATTTGCTTGTAGTTCAAGTAAGTAAGTTTGATTG TGGGGGAATAGCCATCAGTGTATGCTTTTCGCACAAGATTGGTGATGGTTGCTCTCTGCTTAATTTCCTTAATGATTGGTCTAGCGTTACTCGTGAT CATACGACAACAACTTTAGTTCCATCTCCTAGATTTGTAGGAGATTCAGTCTTCTCTACACAAAAATATGGTTCTCTCATTACGCCACAAATTTTGTC CGATCTCAACCAGTGCGTACAGAAAAGACTCATTTTTCCTACAGATAAGTTAGATGCACTTCGAGCTAAGGTAATACTACCATCGTCCATTATTGTTT GTCTTACGGTATTTTTGAAAAGAATAATATTTAATAGTCTTCTTGAGACATATTTCACTTAACAAGCCTAGGCTATTTAGTCTATTTGTAGAAGCTACT CTTAAACGCCTCACTTAGTTAATAGCACTCCACTTATTGGTGTCAAAAACTACTCTTGGACATGTCATTTACTTAATAACACTCCACTTAATTATCGAA
- 7. Alignment, assembly, annotation Li et al. (2011) EBI / NCBI / DDBJ
- 8. Three millennia of modelling life
- 9. Phylogenetic trees H. sapiens ATG CTC TAT GAG P. troglodoytes ATG CTC TTT GAG G. gorilla ATG CTT TAT TAC P. troglodoytes G. gorilla H. sapiens P. troglodoytes 11 9 8
- 11. 2. Adventures with DNA sequencing
- 13. Field-sequencing for real Conditions 100% humidity; 6-13ºC Essential kit 800w generator 3x laptops Centrifuge Waterbath Polystyrene boxes (lots) Kettle(…!) Yield >400Mbp data in three days; A. thaliana ~2.01x coverage
- 14. Snowdonia, HelloWorld & ‘tent-seq’ A. thaliana Arabidopsis lyrata Congeneric species; Reference genomes available Field-sequenced (MinION) & Lab-sequenced (Illumina™) Orthogonal BLAST: 4 sample*sequencer combinations Compare TRUE & FALSE rates for varying ID statistic cutoffs
- 15. Field- vs. lab-sequenced sample ID Match individual reads to each reference with BLAST Compare match lengths in TRUE and FALSE cases ‘Length bias’ ID stat: lengthTRUE - lengthFALSE Compare TRUE & FALSE rates as length bias cutoff varies MiSeq (lab) MinION (field)
- 16. Bitty data (1) partial queries Subsample MinION output Repeat ID pipeline, record mean ID stat sbias Replicates: N = 30 Simulate from 100 – 104 reads (≈instant → hours)
- 17. Bitty data (2) partial references Take reference genome at high contiguity Fragment randomly to target (low) contiguity Repeat read identification using fragmented DB Simulate N50 ≈1,000bp to N50 ≈ 10Mbp
- 18. Keeping it simple: Kew Science Festival Six species: whole genome- skim samples with MinION in preparation Build BLAST DBs from skimmed data Select ‘unknown’ (blinded) sample, extract DNA and resequence in real-time Compare to partial DBs in six-way BLAST competition Live ID ?
- 19. de novo genome assembly Data MiSeq only MiSeq + MinION Assembler Abyss hybridSPAdes Illumina reads, 300bp paired-end 8,033,488 8,033,488 Illumina data (yield) 2,418 Mbp 2,418 Mbp MinION reads, R7.3 + R9 kits, N50 ~ 4,410bp - 96,845 MinION data (yield) - 240 Mbp Approx. coverage 19.49x 19.49x + 2.01x Assembly key statistics: # contigs 24,999 10,644 Longest contig 90 Kbp 414 Kbp N50 contiguity 7,853 bp 48,730 bp Fraction of reference genome (%) 82 88 Errors, per 100 kbp: #N’s 1.7 5.4 # mismatches 518 588 # indels 120 130 Largest alignment 76,935 bp 264,039 bp CEGMA gene completeness estimate: # genes 219 of 248 245 of 248 % genes 88% 99%
- 20. Wait – genes? Entire chloroplast genome (~150kbp) Plastid coding loci Individual field- sequenced MinION reads
- 21. Real-time phylogenomics Filtered reads Gene models TAIR10 CDS code Annotation SNAP 1:1 reciprocal BLAST Multiple sequence alignments MUSCLE Trimal Gene trees → Consensus tree *BEAST RAxML, TreeAnnotator Cumulative counts: Unique genes All genes (‘Lab’ being transported!)
- 22. 3. Life is complex
- 23. Evolution is complex Nakhleh, (2009); Suh (2016) Zool. Scripta. doi:10.1111/zsc.12213 Zapata et al. (2016) PNAS 113:E4052-E4060 ©2016 National Academy of Sciences
- 24. Networks Strimmer & Moulton (2000) MBE Solís-Lemus & Ané (2016) PLoS Genet. Nakhleh (2009) in: Heath & Ramakrishnan, eds., Springer
- 25. Key: Extant node Inferred node Synteny edge (physical connection Phylogeny edge (evolutionary connection) Identity edge (organismal connection) Three-colour graphs: phylogeny, synteny & identity a b c d x y z e a a
- 26. Three-colour graphs: phylogeny, synteny & identity a1 b1 a2 b2 a3 b3 b’3 a4 b4 a5 b5 Duplication Key: Extant node Inferred node Synteny edge (physical connection) Phylogeny edge (evolutionary connection) Identity edge (organismal connection) a1 b1 b2 a2 a3 b3 c1 c3 c1 Inversion
- 27. Three-colour graphs: phylogeny, synteny & identity a1 b1 a2 b2 a3 b3 a4 b4 x4 y4 x3 y3 x1 y1 Tetraploid hybrid formed Diploidization (secondary loss) Key: Extant node Inferred node Synteny edge (physical connection) Phylogeny edge (evolutionary connection) Identity edge (organismal connection) a1 b1 a2 b2 x1 x5 x2 x3 x4 x7 x6 Horizontal gene transfer
- 28. Step back: molecular evolution “Horizontal gene transfer occurs x more frequently in these lineages, because of this biology” “Convergent evolution is rare in most genes, in most organisms, but y times greater in these gene families …because of this biology” “New chomosomes are created & destroyed at z, q, rates in this reproductive strategy …because of this biology”
- 29. 4. Real big data
- 30. How big? How many? Species: • Mammals: 103 – 105 • Animals: 106 – 107 • Plants: 105 – 106 • Bacteria: >106 ? • Fungi: >>105-??? DNA sequencing machines: ~104-5 (each ~109-10 bp/day) (Organisms): (…a lot) Example Typical feature size (Mbp) Largest known genomes ~240,000 (1011) Vascular plant ~0.1 – 10,000 (108 - 1010) Human genome 3,000 (3x109) Most fungi ~0.1 – 10 (105 - 107) Bacteria ~0.1 – 1 (106) Viruses ~0.01 (105) Mitochondria / chloroplasts 0.017 / 0.2 (104 ;105) ‘Barcoding’ locus ~1000bp (102) Illumina read 75-300bp Nanopore read 100bp –>1Mbp
- 31. The tools aren’t in great shape but the prizes are there bionode.js bioboxes.org Singularity Portable sequencing, by anyone means really Big Data Informatics connecting this data through explicit models is inference Scalable, reproducible, sustainable research:
- 32. Ubiquitous / citizen-sequencing From lab-based… … to ‘app store’ genomics
- 33. Metagenomics… © 2016 Katy Reed / FR EPI2ME; Juul et al. (2016) / Metrichor.com
- 34. Health: defining ‘normal’ Credit: Darryl Leja, NHGRI
- 35. Food-chain: inputs and outputs
- 36. Environmental sampling (Species’ abundance varying in time and space) (AKA “trampling on the ecologists’ toes”)
- 37. Genomic observatories • [GBIF pic] • orms
- 38. The Tree (network) of Life iTOL: Ivica Letunic, Mariana Ruiz Villarreal
- 39. Final thoughts
- 40. Thanks, funders, contacts and questions Oxford Nanopore Technologies Ltd. Dan Turner, Richard Ronan, Gerrard CoyneU Bangor: Alexander S.T. Papadopulos (@metallophyte) RBG Kew: Postdocs: Andrew Helmstetter (@ajhelmstetter); Tim Coker Thanks: Dion Devey, Robyn Cowan, Tim Wilkinson, Stephen Dodsworth, Pepijn Kooij, Felix Forest, Bill Baker, Jan T. Kim, Jenny Williams, Abigail Barker, Mark Lee, Jim Clarkson, Mike Chester, Ester Gaya, Lisa Pokorny, Laszlo Csiba, Paul Wilkin, Richard Buggs, Mike Fay, Mark Chase, Ilia Leitch QMUL Laura Kelly, Kalina Davies, Steve Rossiter Oxford Aris Katzourakis, Oli Pybus, Jayna Raghwani Others Forest Research: Daegan Inward, Katy Reed Dstl: Claire Lonstale, James Taylor Birmingham: Nick Loman, Josh Quick U. Utah: Bryn Dentinger Imperial: James Rosindell This research was conducted in the Sackler Phylogenomics Laboratory and was supported by the Calleva Foundation Phylogenomic Research Programme and the Sackler Trust @lonelyjoeparker: joe.parker@kew.org
Notes de l'éditeur
- Definitions Genetic data, what it is, where it’s found, how we get it A genome / assembly Annotation and alignment A phylogeny or tree
- Definitions Genetic data, what it is, where it’s found, how we get it A genome / assembly Annotation and alignment A phylogeny or tree
- Naming stuff The ladder of life Binomial / ontological naming Darwin and The Tree Networks
- Portable sequencing: also long reads and real-time
- Portable Real-time Long easy
- Data in terrible conditions but anyone can do it Social media reach The Atlantic, Economist
- Direct, explicit, orthogonal test – and can it work? Picture of experimental design Outline of the study In terms of bioinformatics questions Funding: a first pot and timeline…
- We compare match lengths, and minon allows long matches
- EXPLAIN AXES: precision improves rapidly
- EXPLAIN AXES: a partial REFERENCE would work, too
- MORE FUNDING. SO simple a kid could do it? Yes The challenge I set myself: OK, it’s a simple experiment. Can I buid a trest simple ehough a child can understand it? SOCIAL MEDIA Funding: NANOPORE
- Data from one time and place can and should be useful elsewhere lash a bit of proper genomics
- Single reads match whole genes – meat & drink
- EXPLAIN AXES postdoc-years PAPER ACCEPTED
- Genomes come in all shapes and sizes Organisms too, life cycles (A)sexual reproduction; clonal replication Even genetic alphabet not fixed And mutation isn’t random Incongruence and reticulation Horizontal gene transfer Incomplete lineage sorting Hybridization Recombination
- Networks attempt to summarise this Splits graphs, directed graphical models / planar graphs
- Definition Features Generalised representations Phylogeny edge information workable-outable Other edges present in metadata; inferable Generative model; easy to interpret… Here’s a common framework for all these studies How to infer – sounds like a nightmare Many of the edges in this network are really there already Shifting paradigms, making linking easier Explicitly model phylogeny, synteny and identity Edge support reflects evidence; deviations from neutrality reflect hypotheses/models/phenomena Any nodes connecting to an identity edge are considered completely connected Maximum # edges ~n (2n-1)/2 Digraphs ~n!! Possible ancestors from one locus on n taxa essentially inverse func of when they coalesce (can have m generations of n ancestors until an event where n(m)<n(t)
- EXAMPLES Gene duplication e.g. paralogue in animal Tetraploid formed then secondary diploidization, e.g. plant Inversion in a genome Unlinked loci (e.g. bacterial plasmids) and HGT. How to infer – sounds like a nightmare Many of the edges in this network are really there already Shifting paradigms, making linking easier Explicitly model phylogeny, synteny and identity Edge support reflects evidence; deviations from neutrality reflect hypotheses/models/phenomena
- EXAMPLES Gene duplication e.g. paralogue in animal Tetraploid formed then secondary diploidization, e.g. plant Inversion in a genome Unlinked loci (e.g. bacterial plasmids) and HGT. How to infer – sounds like a nightmare Many of the edges in this network are really there already Shifting paradigms, making linking easier Explicitly model phylogeny, synteny and identity Edge support reflects evidence; deviations from neutrality reflect hypotheses/models/phenomena
- We need enough data to turn obervations, into empirical comparisons, into models and laws We know a lot about evolutionary mechanisms And a lot about (a handful of genomes) What we know tells us “it’s complicated” Most genes don’t have simple orthologues etc etc etc, hotizonatl etc But we don’t, really, have an empirical understanding of how they fit together, e.g.: - ”horizontal gene transfer occurs x more frequently in these lineages, because of this biology” - adaptive molecularconvergence is rare in most genes, in most organisms, but y times greater in these gene families because of this biology - new chomosomes are created (by duplication, endogenisation, polyploidy) and destroyed (by diploidization) at z rates in this reproductive strategy because of biology
- Global databases Algorithms, methods and theory Generally bespoke / slow / in-house Special sauce Formally linking datasets and models is inferring the network of life Shifts the job for bioinformatics from something it’s good at – sophisiticated analysis incemental To sometheing computers in gerneral are great at: linking elements In this case informatics doesn’t enable research , it is the process of inference It’s relatively easy to write a new standalone app to do x, or analyse some big dataset Reproducibility and scaling-up science mean we must work harder on the links Informatics as inference. The lonely astronomers.
- HPCs to apps: Exponential data, linear understanding. Pause – to recap This is important because it’s where we tie it together and show my contribution: Portable sequencers, easier to use More places More experimenters More data More noise Efficient comparison? Dynamic computation? Clever hashing Portable, mass sequencing is really here Massive potential for de novo genomics; phylogenomics But while we’re accumulating information at an exponential rate, we’re integrating it linearly, in essence … where are we going?
- Superset of species ID Distribution of species, sometimes functional focus We may not have positive controls We usually don’t know ‘normal’ distribution From a fringe idea to routine
- Gut microbiome Many other tissues UTI Dental Cardiac Respiratory Not just human health; pathogen surveillance
- Dodgy burgers and provenance Pests, crop inputs Feeds Supporting ecosystem health/services
- Ecosystems; habitats; communities; niches Properly ecologists’ domain Thousands of species Abundances shift in time and space All trackable with DNA Where do ecosystem services come from? How healthy?
- What’s really out there Longitudinal data Fixed locations Parallels with earth sensing Autonomous in-situ sensor platforms
- Data collection in aggregate means we can asymptotically assemble the components we need for the Tree Of Life This is loosely defined as the Map Of The World for genomic stuff Not exactly simple but not a computational / and engineering challenge, not really intellectually taxing (probably) Pretty much the biggest goal in evolution
- The cosmology of life Why genomes/chromosomes? Why that size? Why organisms? Where is the root Sequence-space and the network as a state-space Inflation Probability function of you
- Funders Thanks Reach out