1.Building better genomes, transcriptomes, andmetagenomes with improvedtechniques for de novo assembly -- an easier way to do itC. Titus BrownAssistant ProfessorCSE, MMG, BEACON Michigan State University December 2012ctb@msu.edu

2. AcknowledgementsLab members involvedCollaborators Adina Howe (w/Tiedje) Jim Tiedje, MSU Jason Pell Erich Schwarz, Caltech / Arend HintzeCornell Rosangela Canino- Paul Sternberg, CaltechKoning Robin Gasser, U. Qingpeng ZhangMelbourne Elijah Lowe Weiming Li Likit Preeyanon Funding Jiarong Guo Tim BromUSDA NIFA; NSF IOS; Kanchan PavangadkarBEACON. Eric McDonald 3. We practice open science!Be the change you wantEverything discussed here: Code: github.com/ged-lab/ ; BSD license Blog: http://ivory.idyll.org/blog (titus brown blog) Twitter: @ctitusbrown Grants on Lab Web site:http://ged.msu.edu/interests.html Preprints: on arXiv, q-bio:diginorm arxiv 4. My interestsI work primarily on organisms of agricultural,evolutionary, or ecological importance, which tendto have poor reference genomes andtranscriptomes. Focus on: Improving assembly sensitivity to better recover genomic/transcriptomic sequence, often from weird samples. Scaling sequence assembly approaches so that huge assemblies are possible and big assemblies are straightforward. 5. Weird biological samples: Single genome Hard to sequence DNA(e.g. GC/AT bias) Transcriptome Differential expression! High polymorphism data Multiple alleles Whole genome Often extreme amplified amplification bias (next slide) Metagenome (mixed Differential abundance microbial community)within community. 6. Shotgun sequencing andcoverage Coverage is simply the average number of reads that overlapeach true base in genome.Here, the coverage is ~10 just draw a line straight down from thetop through all of the reads. 7. Coverage plot of colony sequencingvs single-cell amplification (MDA) (MD amplified) 8. Non-normal coverage distributionslead to decreased assemblysensitivity Many assemblers embed a coverage model in their approach. Genome assemblers: abnormally low coverage iserroneous; abnormally high coverage is repetitivesequence. Transcriptome assemblers: isoforms should havesame coverage across the entire isoform. Metagenome assemblers: differing abundancesindicate different strains. Is there a different way? (Yes.) 9. Memory requirements (Velvet/Oases est) Bacterial genome 1-2 GB (colony) 500-1000 GB Human genome 100 GB + Vertebrate mRNA 100 GB Low complexity metagenome 1000 GB ++ High complexity metagenome 10. Practical memory measurements 11. K-mer based assemblers scalepoorlyWhy do big data sets require big machines??Memory usage ~ real variation + number of errorsNumber of errors ~ size of data set 12. Why does efficiency matter? It is now cheaper to generate sequence than it is to analyze it computationally! Machine time (Wo)man power/time More efficient programs allow better exploration of analysis parameters for maximizing sensitivity. Better or more sensitive bioinformatic approaches can be developed on top of more efficient theory. 13. 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, becauseof errors, memory. We can discard it for you 14. Digital normalization approach A digital analog to cDNA library normalization, diginorm: 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. 15. Coverage before digitalnormalization:(MD amplified) 16. Coverage after digital normalization:Normalizes coverageDiscards redundancyEliminates majority oferrorsScales assembly dramatAssembly is 98% identica 17. Digital normalization approach A digital analog to cDNA library normalization,diginorm is a read prefiltering approach that: 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. 18. Contig assembly is significantly more efficient andnow scales with underlying genome size Transcriptomes, microbial genomes incl MDA, and most metagenomes can be assembled in under 50 GB of RAM, with identical or improved results. 19. Some diginorm examples:1. Assembly of the H. contortus parasitic nematode genome, a high polymorphism problem.2. Reference-free assembly of the lamprey (P. marinus) transcriptome, a big assembly problem.3. Assembly of two Midwest soil metagenomes, Iowa corn and Iowa prairie the impossible assembly problem. 20. 1. 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 ErichSchwarz) 21. H. contortus life cycleRefs.: Nikolaou and Gasser (2006), Int. J. Parasitol. 36, 859-868;Prichard and Geary (2008), Nature 452, 157-158. 22. The power of next-gen. sequencing: get 180x coverage ... and then watch yourassemblies never finishLibraries 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. 23. Assembly after digital normalization Diginorm readily enabled assembly of a 404 Mbpgenome with N50 of 15.6 kb; Post-processing with GapCloser andSOAPdenovo scaffolding led to final assembly of453 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 24. Next steps with H. contortus Publish the genome paper Identification of antibiotic targets for treatment inagricultural settings (animal husbandry). Serving as reference approach for a widevariety of parasitic nematodes, many of whichhave similar genomic issues. 25. 2. Lamprey transcriptome assembly. Lamprey genome is draft and 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? 26. Sea lamprey in the Great Lakes Non-native Parasite ofmedium to largefishes Causedpopulations ofhost fishes tocrashLi Lab / Y-W C-D 27. 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 15,100 transcripts > 1kb Against known transcripts, 98.7% agreement (accuracy); 99.7% included (contiguity) 28. 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 thoughtto be mammalian only!) Pheromonal response in adults 29. 3. Soil metagenome assembly Observation that 99% of microbes cannot easilybe cultured in the lab. (The great plate countanomaly) Many reasons why you cant or dont want toculture: Syntrophic relationships Niche-specificity or unknown physiology Dormant microbes Abundance within communities Single-cell sequencing & shotgun metagenomicsare two common ways to investigate microbialcommunities. 30. SAMPLING LOCATIONS 31. Investigating soil microbialecology What ecosystem level functions are present, andhow 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 inthe population? What does the phage and viral population look like? What species are where? 32. A Grand Challenge dataset(DOE/JGI) Total: 1,846 Gbp soil metagenome600 MetaHIT (Qin et. al, 2011), 578 Gbp500Basepairs of Sequencing (Gbp)400300 Rumen (Hess et. al, 2011), 268 Gbp200Rumen K-mer Filtered, 111 Gbp100 NCBI nr database,37 Gbp0Iowa,Iowa, Native Kansas,Kansas, Wisconsin, Wisconsin, Wisconsin, Wisconsin,ContinuousPrairie Cultivated NativeContinuousNativeRestored Switchgrass corn corn Prairie cornPrairiePrairie GAII HiSeq 33. Whoa, thats a lot of data Estimated sequencing required (bp, w/Illumina)5E+14 4.5E+144E+14 3.5E+143E+14 2.5E+142E+14 1.5E+141E+145E+130 E. coli HumanVertebrateHuman gut Marine Soilgenome genome transcriptome 34. Additional Approach for Metagenomes: Data partitioning (a computational version of cell sorting)Split reads into binsbelonging todifferent sourcespecies.Can do this basedalmost entirely onconnectivity ofsequences.Divide and conquerMemory-efficientimplementationhelps to scaleassembly.Pell et al., 2012, PNAS 35. Partitioning separates reads by genome.When computationally spiking HMP mock data with one E. coligenome (left) or multiple E. coli strains (right), majority of partitions contain reads from only a single genome (blue) vsmulti-genome partitions (green). * * Partitions containing spiked data indicated with aAdina Howe * 36. Assembly results for Iowa corn and prairie(2x ~300 Gbp soil metagenomes)PredictedTotal Total Contigs% Reads proteinAssembly (> 300 bp) Assembled coding2.5 bill4.5 mill 19%5.3 mill3.5 bill5.9 mill 22%6.8 millPutting it in perspective:Total equivalent of ~1200 bacterial genomes Adina HoweHuman genome ~3 billion bp 37. Resulting contigs are lowcoverage. Figure 11: Coverage (median basepair) dist ribut ion of assembled cont igs from soil met agenomes. 38. Strain variation? Can measure by readTop two allele frequencies mapping. Of 5000 most abundant contigs, only 1 has a polymorphism rate > 5%Position within contig 39. Tentative observations from our soilsamples: We need 100x as much data A lot of our sample may consist of phage. Phylogeny varies more than functionalpredictions. We see little to no strain variation within oursamples Not bulk soil! Very small, localized, and low coverage samples We may be able to do selective really deepsequencing and then infer the rest from 16s. Implications for soil aggregate assembly? 40. Some concluding thoughts Digital normalization is a very powerful techniquefor fixing weird samples. (and all samples areweird.) A number of real world projects are usingdiginorm successfully (~6-10 in my lab; ~30-40?overall). A diginorm-derived procedure is now arecommended part of the Trinity mRNAseqassembler. Diginorm is1. Very computationally efficient;2. Always cheaper than running an assembler in the first place 41. Where next? Assembly in the cloud! Study and formalize paired/end mate pair handling indiginorm. Web interface to run and evaluate assemblies. New methods to evaluate and improveassemblies, including a meta assembly approach formetagenomes. Fast and efficient error correction of sequencing data Can also address assembly of high polymorphismsequence, allelic mapping bias, and others; Can also enable fast/efficient storage and search of nucleicacid databases. 42. Four+ papers on our work, soon. 2012 PNAS, Pell et al., pmid 22847406 (partitioning). In review, Brown et al., arXiv:1203.4802 (digital normalization). Submitted, Howe et al, arXiv: 1212.0159 (artifact removal from Illumina metagenomes). In preparation, Howe et al. assembling the heck out of soil. In preparation, Zhang et al. efficient k-mer 43. Education: next-gen sequencecourseJune 2013, Kellogg Biological Station; < $500 Hands on exposure to data, analysis tools. Metagenomics workshop HERE, tomorrow, 9am-3pm contact Lex Nederbra 44. Thanks!Everything discussed here: Code: github.com/ged-lab/ ; BSD license Blog: http://ivory.idyll.org/blog (titus brown blog) Twitter: @ctitusbrown Grants on Lab Web site:http://ged.msu.edu/interests.html Preprints: on arXiv, q-bio:diginorm arxiv 45. Why are we applying short-read sequencing toRNAseq and metagenomics? Short-read sampling is deep and quantitative. Statistical argument: your ability to observe raresequences your sensitivity of measurement isdirectly related to the number of independentsequences you take. Longer reads (PacBio, 454, Ion Torrent) are lessinformative for quantitation. Majority of metagenome studies going forward will make use of Illumina (~2-3 year statement 46. Digital normalization retains information, whilediscarding data and errors 47. Lossy compressionhttp://en.wikipedia.org/wiki/JPEG 48. Lossy compressionhttp://en.wikipedia.org/wiki/JPEG 49. Lossy compressionhttp://en.wikipedia.org/wiki/JPEG 50. Lossy compressionhttp://en.wikipedia.org/wiki/JPEG 51. Lossy compressionhttp://en.wikipedia.org/wiki/JPEG