1. C. Titus Brown Assistant Professor CSE, MMG, BEACON Michigan State University ctb@msu.edu The pro-shotgun-assembly talk.

2. Acknowledgements Lab members involvedCollaborators Adina Howe (w/Tiedje) Jason Pell Arend Hintze Rosangela Canino-Koning Qingpeng Zhang Elijah Lowe Likit Preeyanon Jiarong Guo Tim Brom Kanchan Pavangadkar Eric McDonald Jordan Fish Chris Welcher Jim Tiedje, MSU Billie Swalla, UW Janet Jansson, LBNL Susannah Tringe, JGI Funding USDA NIFA; NSF IOS; BEACON.

4. Open, online science All of the software and approaches I’m talking about today are available: Assembling large, complex metagenomes arxiv.org/abs/1212.2832 khmer software: github.com/ged-lab/khmer/ Blog: http://ivory.idyll.org/blog/http://ivory.idyll.org/blog/ Twitter: @ctitusbrown

5. Note: I am phylogenetically unconstrained… Chordate mRNAseq (Molgula + lamprey + chick) Nematode genomics Soil metagenomics …but so far not microbial euks, specifically.

6. My goals in this work Interested in genes & genomes: function & evolution, but not as much taxonomy. Little or no marker work (16s/18s) Develop lightweight prefiltering techniques for other tools. Software & methods => democritize data analysis.

7. I am unambiguously pro-assembly. Short-read analysis can be misleading; need more work like Doc Pollard’s showing where/why! Assembly reduces the data size, increases boinformatic signal, and eliminates random errors. The general mental frameworks (OLC or DBG) underpin virtually all sequence analysis anyway, note. So, why not? – Assembly is HARD, SLOW, TRICKY. – Assemblies may MISLEAD you. – Assembly is a STRINGENT FILTER on your data heuristics.

8. There is quite a bit of life left to sequence & assemble. http://pacelab.colorado.edu/

9. Challenges of (micro-)euks Genomes are large and repeat rich. Diploidy and polymorphism will confuse assemblers. – Note: very problematic in tandem with repeats. Nucleotide bias => sequencing bias. Scarce samples => amplification techniques => sequencing bias. All of these confound assembly. Can we “fix”?

10. Three illustrative problem cases H. contortus genome assembly. Lamprey reference-free transcriptome assembly. Soil metagenome assembly.

11. The H. contortus problem A sheep parasite. ~350 Mbp genome Sequenced DNA 6 individuals after whole genome amplification, estimated 10% heterozygosity (!?) Significant bacterial contamination. (w/Robin Gasser, Paul Sternberg, and Erich Schwarz)

12. H. contortus life cycle Refs.: Nikolaou and Gasser (2006), Int. J. Parasitol. 36, 859-868; Prichard and Geary (2008), Nature 452, 157-158.

13. The power of next-gen. sequencing: get 180x coverage... and then watch your assemblies never finish Libraries built and sequenced: 300-nt inserts, 2x75 nt paired-end reads 500-nt inserts, 2x75 and 2x100 nt paired-end reads 2-kb, 5-kb, and 10-kb inserts, 2x49 nt paired-end reads Nothing would assemble at all until filtered for basic quality. Filtering let ≤500 nt-sized inserts to assemble in a mere week. But 2+ kb-sized inserts would not assemble even then. Erich Schwarz

14. So, problem 1: nematode H. contort Highly polymorphic Whole genome amplification Repeat ridden => Assemblers DIE HORRIBLY.

15. The lamprey problem. Lamprey genome is draft quality; low contiguity, missing ~30%. No closely related reference. Full-length and exon-level gene predictions are 50-75% reliable, and rarely capture UTRs / isoforms. De novo assembly, if we do it well, can identify – Novel genes – Novel exons – Fast evolving genes Somatic recombination: how much are we missing, really?

16. Sea lamprey in the Great Lakes Non-native Parasite of medium to large fishes Caused populations of host fishes to crash Li Lab / Y-W C-D

17. Lamprey transcrpitome Started with 5.1 billion reads from 50 different tissues. No assembler on the planet can handle this much data.

18. So, problem 2: lamprey mRNAseq Must go with reference-free approach. TOO MUCH DATA.

19. Soil metagenome assembly Observation: 99% of microbes cannot easily be cultured in the lab. (“The great plate count anomaly”) Many reasons why you can’t or don’t want to culture: – Syntrophic relationships – Niche-specificity or unknown physiology – Dormant microbes – Abundance within communities Single-cell sequencing & shotgun metagenomics are two common ways to investigate microbial communities.

20. SAMPLING LOCATIONS

21. Investigating soil microbial ecology What ecosystem level functions are present, and how do microbes do them? How does agricultural soil differ from native soil? How does soil respond to climate perturbation? Questions that are not easy to answer without shotgun sequencing: – What kind of strain-level heterogeneity is present in the population? – What does the phage and viral population look like? – What species are where?

22. A “Grand Challenge” dataset (DOE/JGI)

23. “Whoa, that’s a lot of data…”

24. Scaling challenges in metagenomics (and assembly, more generally) It is difficult to even achieve an assembly for the volume of data we can easily get. (Also see: ARMO project, ~2 TB of data.) Most current assemblers are quite heavyweight, perhaps partly because they are written by people with large resources. This fails given scaling behavior of sequencing.

25. So, problem 3: soil metagenomics TOO MUCH DATA. BAD SCALING.

26. Approach: Digital normalization (a computational version of library normalization) Suppose you have a dilution factor of A (10) to B(1). To get 10x of B you need to get 100x of A! Overkill!! This 100x will consume disk space and, because of errors, memory. We can discard it for you…

27. Digital normalization

33. Digital normalization approach A digital analog to cDNA library normalization, diginorm: Reference free. Is single pass: looks at each read only once; Does not “collect” the majority of errors; Keeps all low-coverage reads; Smooths out coverage of regions.

34. Coverage before digital normalization: (MD amplified)

35. Coverage after digital normalization: Normalizes coverage Discards redundancy Eliminates majority of errors Scales assembly dramatically. Assembly is 98% identical.

36. Wait, that works?? Note, digital normalization is freely available, with lots of tutorials. Derived approach now part of Trinity (Broad mRNAseq assembler). It is, ahem, still unpublished, but available on arXiv: arxiv.org/abs/1203.4802

37. 1. H. contort after digital normalization Diginorm readily enabled assembly of a 404 Mbp genome with N50 of 15.6 kb; Post-processing with GapCloser and SOAPdenovo scaffolding led to final assembly of 453 Mbp with N50 of 34.2kb. CEGMA estimates 73-94% complete genome. Diginorm helped by: – Suppressing high polymorphism, esp in repeats; – Eliminating 95% of sequencing errors; – “Squashing” coverage variation from whole genome amplification and bacterial contamination

38. H. contort after digital normalization Diginorm readily enabled assembly of a 404 Mbp genome with N50 of 15.6 kb; Post-processing with GapCloser and SOAPdenovo scaffolding led to final assembly of 453 Mbp with N50 of 34.2kb. CEGMA estimates 73-94% complete genome. Diginorm helped by: – Suppressing high polymorphism, esp in repeats; – Eliminating 95% of sequencing errors; – “Squashing” coverage variation from whole genome amplification and bacterial contamination

39. Next steps with H. contortus Publish the genome paper Identification of antibiotic targets for treatment in agricultural settings (animal husbandry). Serving as “reference approach” for a wide variety of parasitic nematodes, many of which have similar genomic issues.

40. 2. Lamprey transcriptome results Started with 5.1 billion reads from 50 different tissues. Digital normalization discarded 98.7% of them as redundant, leaving 87m (!) These assembled into more than 100,000 transcripts > 1kb Against known full-length, 98.7% agreement (accuracy); 99.7% included (contiguity)

41. Evaluating de novo lamprey transcriptome Estimate genome is ~70% complete (gene complement) Majority of genome-annotated gene sets recovered by mRNAseq assembly. Note: method to recover transcript families w/o genome… (Includes transcripts > 300 bp)

42. Next steps with lamprey Far more complete transcriptome than the one predicted from the genome! Enabling studies in – – Basal vertebrate phylogeny – Biliary atresia – Evolutionary origin of brown fat (previously thought to be mammalian only!) – Pheromonal response in adults

43. 3. Soil metagenomics – still hard…

44. Additional Approach for Metagenomes: Data partitioning (a computational version of cell sorting) Split reads into “bins” belonging to different source species. Can do this based almost entirely on connectivity of sequences. “Divide and conquer” Memory-efficient implementation helps to scale assembly. Pell et al., 2012, PNAS

45. Partitioning separates reads by genome. Strain variants co-partition. When computationally spiking HMP mock data with one E. coli genome (left) or multiple E. coli strains (right), majority of partitions contain reads from only a single genome (blue) vs multi-genome partitions (green). Partitions containing spiked data indicated with a * Adina Howe * *

46. Putting it in perspective: Total equivalent of ~1200 bacterial genomes Human genome ~3 billion bp Assembly results for Iowa corn and prairie (2x ~300 Gbp soil metagenomes) Total Assembly Total Contigs (> 300 bp) % Reads Assembled Predicted protein coding 2.5 bill4.5 mill19%5.3 mill 3.5 bill5.9 mill22%6.8 mill Adina Howe

47. Resulting contigs are low coverage.

48. …but high coverage is needed. Low coverage is the dominant problem blocking assembly of your soil metagenome.

49. Strain variation? Top two allele frequencies Position within contig Of 5000 most abundant contigs, only 1 has a polymorphism rate > 5% Can measure by read mapping.

50. Overconfident predictions We can assemble virtually anything but soil ;). – Genomes, transcriptomes, MDA, mixtures, etc. – Repeat resolution will be fundamentally limited by sequencing technology (insert size; sampling depth) Strain variation confuses assembly, but does not prevent useful results. – Diginorm is systematic strategy to enable assembly. – Banfield has shown how to deconvolve strains at differential abundance. – Kostas K. results suggest that there will be a species gap sufficient to prevent contig misassembly. – Even genes “chimeric” between strains are useful.

51. Reasons why you shouldn’t believe me 1)Strain variation – when we get deeper in soil, we should see more (?). Not sure what will happen, and we do not (yet) have proven approaches. 2)We, by definition, are not yet seeing anything that doesn’t assemble. 3)We have not tackled scaffolding much. Serious investigation of scaffolding will be necessary for any good genome assembly, and scaffolding is weak point.

52. Some concluding thoughts on shotgun metagenomics Making good use of environmental metagenome data is very hard; assemblies don’t solve this, but may provide traction. In particular, connection to “function” and actual biology is very hard to make. (See other speakers for good positive examples.) Our current assembly approaches do not yet push limits of data. Illumina’s high sampling rate makes it only game in town. Rate limiting factor is increasingly bioinfo-who-can-speak- to-biologists. Assembly is a really stringent filter; diginorm is not.

53. A brief tour of forthcoming awesomeness Targeted-gene assembly from short reads. (Fish et al., Ribosomal Database Project). rRNA search in shotgun data. Awesome™ techniques for comparing and evaluating different assemblies. Error correction for mRNAseq & metag data. Better diginorm. Strain variation collapse, assembly, & recovery.

54. Some specific proposals Include significant funding for bioinformatic investigation in anything you do. – Everyone gets this wrong. I’m looking at you, NIH, NSF, GBMF, Sloan, DOE, USDA. – Cleverness scales better in bioinfo than exp. Shotgun DNA and shotgun RNA + assembly- based approaches => gene “tags”. – Less experimental treatment up front is good. – Isoforms are hard, note.

55. The Last Slide All of the computational techniques are available, along with a number of preprints. They make assembly more possible but not necessarily easy. My long term goal is to make most assembly & all evaluation easy.