Rachel Adams - SMBE Euks Meeting

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Rachel Adams - SMBE Euks Meeting

  1. 1. Next-generational sequencing formicrobial ecology:alpha diversity, beta diversity, andbiases in high-throughput sequencingRachel AdamsAndrew RomingerSara BrancoThomas Bruns
  2. 2. Understudied but fundamental ecologicalhabitatImplications for human healthSick building syndromeMetrics are practically absent: composition andquantitative characteristicsNeed comparison of “typical” buildingsThe microbiome of the built environment
  3. 3. Understudied but fundamental ecologicalhabitatImplications for human healthSick building syndromeMetrics are practically absent: composition andquantitative characteristicsNeed comparison of “typical” buildings and highreplication across settings to detect patternsThe microbiome of the built environment
  4. 4. ???The What and Why of the indoor microbiome
  5. 5. ???ArchitectureVentilationBuilding functionThe What and Why of the indoor microbiome
  6. 6. ???ArchitectureVentilationBuilding function Environmental settingThe What and Why of the indoor microbiome
  7. 7. ???ArchitectureVentilationBuilding function Environmental settingResidentsThe What and Why of the indoor microbiome
  8. 8. Fungi in the indoor microbiome, and beyondYeastsFilaments
  9. 9. Fungi in the indoor microbiome, and beyondYeastsFilamentsSaprobes
  10. 10. Fungi in the indoor microbiome, and beyondYeasts SaprobesSymbiontsParasites Mutualists− +
  11. 11. Assessing environmental fungi1. Estimated that 5-20% of fungi grow in culture2. Identification requires a fungal taxonomist
  12. 12. Assessing environmental fungiSSU RNA (18S) (5.8S) LSU RNA (28S)ITS1 ITS2Nuclear ribosomal internal transcribed spacer(ITS) region as a universal DNA barcodemarker for Fungi - Schoch et al. 2012
  13. 13. High-throughput sequencing has greatly expandedcapabilities in microbial ecology
  14. 14. ACGAGTGCGTHigh-throughput sequencing has greatly expandedcapabilities in microbial ecology
  15. 15. ACGAGTGCGTHigh-throughput sequencing has greatly expandedcapabilities in microbial ecology
  16. 16. ACGAGTGCGTACGCTCGACAAGACGCACTCAGCACTGTAGATCAGACACG104– 107sequence readsHigh-throughput sequencing has greatly expandedcapabilities in microbial ecology
  17. 17. α1β12ϒα2 α3β23β13alpha, beta, gamma diversity
  18. 18. α1α2 α3alpha, beta, gamma diversity
  19. 19. α1β12α2 α3β23β13alpha, beta, gamma diversity
  20. 20. α1β12ϒα2 α3β23β13alpha, beta, gamma diversity
  21. 21. Kunin et al. 2010Groundtruthing high-throughput sequencing foralpha richness
  22. 22. Kunin et al. 2010αtrue < αestGroundtruthing high-throughput sequencing foralpha richness
  23. 23. Groundtruthing high-throughput sequencing
  24. 24. True samplesHigh-throughputsequencingObserved samplesα1α2 α3α1+α2+ α3+In terms of diversity, we know that αcan be elevated in high-throughputsequenced communities...
  25. 25. True communityObserved communityβ12 β13β23β12? β13?β23?α1α2 α3α1+α2+ α3+...but how does that changeconclusions of ecological processesthat are based on β diversity?High-throughputsequencing
  26. 26. A key component to community ecology: Linkingprocesses to this compositional variationAdams et al., ISME Journal, 2013Beta diversity: the variation in species compositionamong sites
  27. 27. Do errors that inflate alpha diversity bias conclusions on betadiversity between samples?Why would it?• Particular taxa in one environment grouping do not amplify oramplify in a way that skews relative abundance of all others*• Clustering incorrectly groups divergent taxa or splits identicaltaxaHypothesis: NoWhile richness/diversity estimations will be off for any givensample, conclusions of beta-diversity will be robust to theQuestion and hypotheses
  28. 28. Do errors that inflate alpha diversity bias conclusions on betadiversity between samples?Why would it?• Particular taxa in one environment grouping do not amplify oramplify in a way that skews relative abundance of all others*• Clustering incorrectly groups divergent taxa or splits identicaltaxaHypothesis: NoWhile richness/diversity estimations will be off for any givensample, conclusions of beta-diversity will be robust to theQuestion and hypotheses
  29. 29. Do errors that inflate alpha diversity bias conclusions on betadiversity between samples?Why would it?• Particular taxa in one environment grouping do not amplify oramplify in a way that skews relative abundance of all others*• Clustering incorrectly groups divergent taxa or splits identicaltaxaWhile richness/diversity estimations will be off for any givensample, conclusions of beta-diversity will be robust to theerrorsQuestion and hypotheses
  30. 30. Simulation processInitial communitySimulated communityOTU1 OTU2 … OTUjSample1Sample2…Sample iOTU1 OTU2 … OTUkSample1Sample2…
  31. 31. Simulation processExpected relativeabundance of OTUsInitial communities
  32. 32. Simulation processBiased relativeabundanceVariation in taxon-specific amplificationInitial communitiesExpected relativeabundance of OTUs
  33. 33. Simulation processBiased relativeabundanceVariation in taxon-specific amplificationBiased relativeabundance + errorSequence errorInitial communitiesExpected relativeabundance of OTUs
  34. 34. Simulation processBiased relativeabundanceVariation in taxon-specific amplificationBiased relativeabundance + errorSequence errorClustering OTUsInitial communitiesBiased relativeabundance + error +clusteringExpected relativeabundance of OTUs
  35. 35. Simulation processBiased relativeabundanceVariation in taxon-specific amplificationBiased relativeabundance + errorSequence errorBiased relativeabundance + error +clusteringClustering OTUsSimulated communitiesInitial communitiesExpected relativeabundance of OTUs
  36. 36. Model summary – 2 types of errors1. Create group differences that aren’t there (Type I error)-0.5 0.0 0.5-0.4-0.20.00.20.4TrueNMDS1NMDS2-0.5 0.0 0.5-0.4-0.20.00.20.4PerceivedNMDS1NMDS2
  37. 37. Model summary – 2 types of errors2. Loose groups differences that are there (Type II error)-0.5 0.0 0.5-0.4-0.20.00.20.4TrueNMDS1NMDS2-0.5 0.0 0.5-0.4-0.20.00.20.4PerceivedNMDS1NMDS2
  38. 38. Model summary output1. Presence of bias: Statistical categorical differencesGroups R2p-valueLocation 0.02 0.34Season 0.20 0.0012. Degree of bias: percentage difference between trueand simulated communities(Simulated – True)True= Normalized bias
  39. 39. Model summary output1. Presence of bias: Statistical categorical differences2. Degree of bias: percentage difference between trueand simulated communities(Simulated distance – True distance)True distance= Normalized errorMorisita-Horn distance metricGroups R2p-valueLocation 0.02 0.34Season 0.20 0.001
  40. 40. Categorical differences are robust to high-throughputsequencing errors in alpha diversity, regardless of theunderlying patterns of beta-diversityThe degree of bias is not affected by the underlyingpatterns of beta-diversity but dependent oncommunity characteristicsModel findings
  41. 41. Model findingsCategorical differences are robust to high-throughputsequencing errors in alpha diversity, regardless of theunderlying patterns of beta-diversityThe degree of bias is not affected by the underlyingpatterns of beta-diversity but dependent oncommunity characteristics
  42. 42. True Simulated True Simulated0.00.20.40.60.81.0pvalues No groups Two groupsModel summary – Type I & II error
  43. 43. True Simulated True Simulated0.00.20.40.60.81.0pvalues No groups Two groupsModel summary – Type I & II error
  44. 44. True Simulated True Simulated0.00.20.40.60.81.0pvalues No groups Two groupsModel summary – Type I & II errorWhether groups are different or the same will not be biasedby inflated alpha diversity
  45. 45. Model summary – Degree of biasDegree of bias will be affected by- the error rate of the platform and OTU- clustering- the gamma diversity of the environment- the precise shape of the species abundancedistributionBut not the relationship among samples
  46. 46. Increasing probability of sequencing error and over-splitting OTUs increases bias1e-04 0.0334 0.0667 0.10.00.10.20.30.40.50.6No groupsNormalizederror1e-04 0.0334 0.0667 0.1Two groupsProbability of splitting
  47. 47. Increasing OTU richness decreases bias100 600 11000.00.20.40.60.8Number of OTUsNormalizederror
  48. 48. Shape of species abundance distribution (SAD) affectsbias0 200 400 600 800 1000 1200010002000300040005000RankAbundance
  49. 49. Shape of species abundance distribution (SAD) affectsbias1.5 2.5 3.50.00.20.40.60.8Increasing SAD varianceNormalizederror
  50. 50. As true community distance increases, degree of errordecreases0.65 0.70 0.75 0.800.20.30.40.50.6True distanceNormalizederror
  51. 51. Clustering is the main error-producing stepTrue Amplified Split0.00.10.20.30.40.5R^2values Two groups
  52. 52. Simulation overviewCategorical analysis very robust to errors in high-throughput biasesDegree of bias will be affected by error rate of thesequencing platform and OTU-clustering, the gammadiversity of the environment, the precise shape of thespecies abundance distributionHigh-throughput error leads to an over-estimation ofthe difference between groupsMean bias is ~20-40%Incorrect OTU clustering is most of that
  53. 53. Steps1. In silico: Add further complexity to simulations2. In vitro: Empirically test artificially-createdmicrobial communities
  54. 54. Do errors that inflate alpha diversity bias conclusions on betadiversity between samples?Why would it?• Particular taxa in one environment grouping do not amplify oramplify in a way that skews relative abundance of all others*• Clustering incorrectly groups divergent taxa or splits identicaltaxaHypothesis: NoWhile richness/diversity estimations will be off for any givensample, conclusions of beta-diversity will be robust to the errorsQuestion and hypotheses
  55. 55. Air samples in a mycology classroom:a unique source distorts perceived species richness
  56. 56. Air samples in a mycology classroom:a unique source distorts perceived species richness
  57. 57. Mycology classroom appears to be less rich than otherclassrooms…0 2000 4000 6000 800002004006008001000BACDEIndividualsChaoEstimatedRichness
  58. 58. … but has higher biomassA B C D E050100150200ClassroomPenicilliumsporeequivalents
  59. 59. Composition of non-mycology classrooms are similarABCDEProportionClassroom0 20 40 60 80 100
  60. 60. Mycology classroom dominated by a few taxaABCDEProportionClassroom0 20 40 60 80 100
  61. 61. xxPuffballs dominate mycology classroomPisolithus, aka dog turd fungus Battarrea, tall stiltballLycoperdon, common puffball
  62. 62. Mycology classroom dominated by a few taxaABCDEProportionClassroom0 20 40 60 80 100* * **Adams et al., in review
  63. 63. Beta diversity of mycology classroom: distinctcommunities-1.5 -1.0 -0.5 0.0 0.5-0.4-0.20.00.20.40.60.8NMDS1NMDS2Observed
  64. 64. Beta diversity of mycology classroom: distinctcommunities-1.5 -1.0 -0.5 0.0 0.5-0.4-0.20.00.20.40.60.8NMDS1NMDS2ObservedTaxonomy reassigned
  65. 65. Beta diversity of mycology classroom: distinctcommunities-1.5 -1.0 -0.5 0.0 0.5-0.4-0.20.00.20.40.60.8NMDS1NMDS2ObservedTaxonomy reassignedAbundance reassigned
  66. 66. Conclusions• While deciphering alpha diversity is problematic:- Inflated alpha due to sequence error & clustering- Deflated alpha due to unevennessbeta diversity calculations are robust to these errorsin high-throughput sequencing• Empirical test will be used to corroborate conclusionsof in silico simulations• High-throughput sequencing will continue to be apromising tool for microbial ecologists
  67. 67. Conclusions• While deciphering alpha diversity is problematic:- Inflated alpha due to sequence error & clustering- Deflated alpha due to unevennessbeta diversity calculations are robust to these errorsin high-throughput sequencing• Empirical test will be used to corroborate conclusionsof in silico simulations• High-throughput sequencing will continue to be apromising tool for microbial ecologists
  68. 68. Conclusions• While deciphering alpha diversity is problematic:- Inflated alpha due to sequence error & clustering- Deflated alpha due to unevennessbeta diversity calculations are robust to these errorsin high-throughput sequencing• Empirical test will be used to corroborate conclusionsof in silico simulations• High-throughput sequencing will continue to be apromising tool for microbial ecologists
  69. 69. References – potential biases in high-throughputsequencingDNA extraction: Frostegard et al Appl Environ Microbiol 1999; DeSantis et al FEMSMicrobiology 2005; Feinsten et al Appl Environ Microbiol 2009; Morgan et al PLoS ONE2010; Delmont et al Appl Environ Microbiol 2011PCR amplification/Relative abundance: Amend et al Mol Ecol 2010; Engelbrektson et alISME Journal 2010; Bellemain et al BMC Microbiol 2010; Schloss et al PLoS ONE2011; Pinto & Raskin PLoS ONE 2012; Klindworth et al Nucleic Acids Res 2013Sequencing error/Chimeras/OTU clustering: Huse et al Genome Biol 2007; Huse et alEnviron Microbiol 2010; Kunin et al Environ Microbiol 2010; Quince et al BMCBioinformatics 2010; Lee et al PLoS ONE 2012; Pinto & Raskin PLoS ONE 2012;Bachy et al ISME Journal 2013Sequencing platform/protocol: Morgan et al PLoS ONE 2010; Luo et al PLoS ONE 2012Even sampling depth: Schloss et al PLoS ONE 2011; Gihring et al Environ Microbiol2012Denoising: Gasper & Thomas PLoS ONE 2013;
  70. 70. Empirical test of simulation results100 600 11000.00.20.40.60.8Number of OTUsNormalizederror
  71. 71. PCR bias-0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.20.00.51.01.52.0PCR bias: beta distribution a=0.5, beta=1.0Scatter around line of true abundance versus amplified abundanceDensity0 200 400 600 800 1000 12000200400600800100012001400True abundanceAmplifiedabundance
  72. 72. OTU splitting bias0 5 10 15 200.00.10.20.30.4Split bias: binomial distribution with n=100Number of splitsDensityp=0.001p=0.0667p=0.0334p=0.00010.0 0.5 1.00.00.20.40.60.81.01.2Split location: beta distribution with a=b=0.5Location of splitDensity

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