Rachel Adams - SMBE Euks Meeting
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Rachel Adams - SMBE Euks Meeting Rachel Adams - SMBE Euks Meeting Presentation Transcript

  • Next-generational sequencing formicrobial ecology:alpha diversity, beta diversity, andbiases in high-throughput sequencingRachel AdamsAndrew RomingerSara BrancoThomas Bruns
  • 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
  • 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
  • ???The What and Why of the indoor microbiome
  • ???ArchitectureVentilationBuilding functionThe What and Why of the indoor microbiome
  • ???ArchitectureVentilationBuilding function Environmental settingThe What and Why of the indoor microbiome
  • ???ArchitectureVentilationBuilding function Environmental settingResidentsThe What and Why of the indoor microbiome
  • Fungi in the indoor microbiome, and beyondYeastsFilaments
  • Fungi in the indoor microbiome, and beyondYeastsFilamentsSaprobes
  • Fungi in the indoor microbiome, and beyondYeasts SaprobesSymbiontsParasites Mutualists− +
  • Assessing environmental fungi1. Estimated that 5-20% of fungi grow in culture2. Identification requires a fungal taxonomist
  • 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
  • High-throughput sequencing has greatly expandedcapabilities in microbial ecology
  • ACGAGTGCGTHigh-throughput sequencing has greatly expandedcapabilities in microbial ecology
  • ACGAGTGCGTHigh-throughput sequencing has greatly expandedcapabilities in microbial ecology
  • ACGAGTGCGTACGCTCGACAAGACGCACTCAGCACTGTAGATCAGACACG104– 107sequence readsHigh-throughput sequencing has greatly expandedcapabilities in microbial ecology
  • α1β12ϒα2 α3β23β13alpha, beta, gamma diversity
  • α1α2 α3alpha, beta, gamma diversity
  • α1β12α2 α3β23β13alpha, beta, gamma diversity
  • α1β12ϒα2 α3β23β13alpha, beta, gamma diversity
  • Kunin et al. 2010Groundtruthing high-throughput sequencing foralpha richness
  • Kunin et al. 2010αtrue < αestGroundtruthing high-throughput sequencing foralpha richness
  • Groundtruthing high-throughput sequencing
  • True samplesHigh-throughputsequencingObserved samplesα1α2 α3α1+α2+ α3+In terms of diversity, we know that αcan be elevated in high-throughputsequenced communities...
  • 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
  • A key component to community ecology: Linkingprocesses to this compositional variationAdams et al., ISME Journal, 2013Beta diversity: the variation in species compositionamong sites
  • 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
  • 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
  • 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
  • Simulation processInitial communitySimulated communityOTU1 OTU2 … OTUjSample1Sample2…Sample iOTU1 OTU2 … OTUkSample1Sample2…
  • Simulation processExpected relativeabundance of OTUsInitial communities
  • Simulation processBiased relativeabundanceVariation in taxon-specific amplificationInitial communitiesExpected relativeabundance of OTUs
  • Simulation processBiased relativeabundanceVariation in taxon-specific amplificationBiased relativeabundance + errorSequence errorInitial communitiesExpected relativeabundance of OTUs
  • Simulation processBiased relativeabundanceVariation in taxon-specific amplificationBiased relativeabundance + errorSequence errorClustering OTUsInitial communitiesBiased relativeabundance + error +clusteringExpected relativeabundance of OTUs
  • Simulation processBiased relativeabundanceVariation in taxon-specific amplificationBiased relativeabundance + errorSequence errorBiased relativeabundance + error +clusteringClustering OTUsSimulated communitiesInitial communitiesExpected relativeabundance of OTUs
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • True Simulated True Simulated0.00.20.40.60.81.0pvalues No groups Two groupsModel summary – Type I & II error
  • True Simulated True Simulated0.00.20.40.60.81.0pvalues No groups Two groupsModel summary – Type I & II error
  • 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
  • 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
  • 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
  • Increasing OTU richness decreases bias100 600 11000.00.20.40.60.8Number of OTUsNormalizederror
  • Shape of species abundance distribution (SAD) affectsbias0 200 400 600 800 1000 1200010002000300040005000RankAbundance
  • Shape of species abundance distribution (SAD) affectsbias1.5 2.5 3.50.00.20.40.60.8Increasing SAD varianceNormalizederror
  • As true community distance increases, degree of errordecreases0.65 0.70 0.75 0.800.20.30.40.50.6True distanceNormalizederror
  • Clustering is the main error-producing stepTrue Amplified Split0.00.10.20.30.40.5R^2values Two groups
  • 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
  • Steps1. In silico: Add further complexity to simulations2. In vitro: Empirically test artificially-createdmicrobial communities
  • 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
  • Air samples in a mycology classroom:a unique source distorts perceived species richness
  • Air samples in a mycology classroom:a unique source distorts perceived species richness
  • Mycology classroom appears to be less rich than otherclassrooms…0 2000 4000 6000 800002004006008001000BACDEIndividualsChaoEstimatedRichness
  • … but has higher biomassA B C D E050100150200ClassroomPenicilliumsporeequivalents
  • Composition of non-mycology classrooms are similarABCDEProportionClassroom0 20 40 60 80 100
  • Mycology classroom dominated by a few taxaABCDEProportionClassroom0 20 40 60 80 100
  • xxPuffballs dominate mycology classroomPisolithus, aka dog turd fungus Battarrea, tall stiltballLycoperdon, common puffball
  • Mycology classroom dominated by a few taxaABCDEProportionClassroom0 20 40 60 80 100* * **Adams et al., in review
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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;
  • Empirical test of simulation results100 600 11000.00.20.40.60.8Number of OTUsNormalizederror
  • 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
  • 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