UC Davis EVE 161 Lecture 7 - rRNA workflows - by Jonathan Eisen @phylogenomics
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UC Davis EVE 161 Lecture 7 - rRNA workflows - by Jonathan Eisen @phylogenomics

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UC Davis EVE161 Lecture 7 by Jonathan Eisen @phylogenomics

UC Davis EVE161 Lecture 7 by Jonathan Eisen @phylogenomics

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    UC Davis EVE 161 Lecture 7 - rRNA workflows - by Jonathan Eisen @phylogenomics UC Davis EVE 161 Lecture 7 - rRNA workflows - by Jonathan Eisen @phylogenomics Presentation Transcript

    • Lecture 7: EVE 161:
 Microbial Phylogenomics ! Lecture #7: Era II: rRNA sequencing and analysis ! UC Davis, Winter 2014 Instructor: Jonathan Eisen Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 !1
    • Where we are going and where we have been • Previous lecture: ! 6: Era II: PCR and major groups • Current Lecture: ! 7: Era II: rRNA sequencing and analysis • Next Lecture: ! 8: Era II: rRNA ecology Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 !2
    • All Analysis Should Be Guided by Goals Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
    • All Analysis Should Be Guided by Goals • Taxonomic assignment for sequences (i.e., what type of organism is the sequence from) Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
    • All Analysis Should Be Guided by Goals • Taxonomic assignment for sequences (i.e., what type of organism is the sequence from) – Best via phylogenetic analysis of sequences – Sometimes done with blast Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
    • All Analysis Should Be Guided by Goals • Taxonomic assignment for sequences (i.e., what type of organism is the sequence from) – Best via phylogenetic analysis of sequences – Sometimes done with blast • Ecological characterization of community Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
    • All Analysis Should Be Guided by Goals • Taxonomic assignment for sequences (i.e., what type of organism is the sequence from) – Best via phylogenetic analysis of sequences – Sometimes done with blast • Ecological characterization of community –Grouping into species / classifying –Have we sampled enough? –Number of species –Relative abundance Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
    • All Analysis Should Be Guided by Goals • Taxonomic assignment for sequences (i.e., what type of organism is the sequence from) – Best via phylogenetic analysis of sequences – Sometimes done with blast • Ecological characterization of community –Grouping into species / classifying –Have we sampled enough? –Number of species –Relative abundance • Comparisons between communities Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
    • All Analysis Should Be Guided by Goals • Taxonomic assignment for sequences (i.e., what type of organism is the sequence from) – Best via phylogenetic analysis of sequences – Sometimes done with blast • Ecological characterization of community –Grouping into species / classifying –Have we sampled enough? –Number of species –Relative abundance • Comparisons between communities –Taxonomy –Ecological metrics Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
    • All Analysis Should Be Guided by Goals • Taxonomic assignment for sequences (i.e., what type of organism is the sequence from) – Best via phylogenetic analysis of sequences – Sometimes done with blast • Ecological characterization of community –Grouping into species / classifying –Have we sampled enough? –Number of species –Relative abundance • Comparisons between communities –Taxonomy –Ecological metrics Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
    • All Analysis Should Be Guided by Goals • Taxonomic assignment for sequences (i.e., what type of organism is the sequence from) – Best via phylogenetic analysis of sequences – Sometimes done with blast • Ecological characterization of community –Grouping into species / classifying –Have we sampled enough? –Number of species –Relative abundance • Comparisons between communities –Taxonomy –Ecological metrics • Phylogenetic diversity Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
    • All Analysis Should Be Guided by Goals • Other Goals from rRNA analysis? Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
    • rRNA Workflow • General workflow ! Sample collection and DNA extraction ! rRNA PCR ! Sequence ! Alignment ! Cluster sequences into groups (known as operational taxonomic units or OTUs) ! Measure relative abundance of OTU by # of sequences in that group ! Try and assign a taxonomy to each OTU • Caveats ! Copy number varies extensively ! Not all organisms amplified Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 !13
    • What to Actually Measure in the Microbiome • Lists ! Taxa ! Genes ! • Summary statistics ! Alpha diversity = within sample ! Beta diversity = between samples ! (and hope these reflect something about functional properties) ! • Estimation vs. measurement Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 !14
    • rRNA PCR Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
    • rRNA PCR Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
    • Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
    • Degenerate PCR Conserved sequence shared by all species * * * * * * Ambiguities in the sequence 5’-TWCGTSGARCTGCACGGVACCGGYAC-3’ IUPAC degeneracies: W = A or T V = C or G or A S = G or C Y = C or T R = A or G 2*2*2*3*2 = 48 different primers sequences Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
    • Alignment Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
    • Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
    • rRNA OTUs Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
    • Clustering (and picking OTUs) singletons Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
    • Clustering (and picking OTUs) Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
    • Clustering (and picking OTUs) Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
    • rRNA phylogenetic trees Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
    • Diversity 1: Alpha Diversity • Alpha diversity is (basically) a measure of the diversity within a single sample • Types of alpha diversity ! Total # of species = richness ! Phylogenetic diversity of species = PD ! Total # of genes = genetic richness ! Phylogenetic diversity of genes = genetic PD Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 !26
    • n M ay 11, 2010 stabilized (Fig. 7), suggesting that further sampling will result in a greater difference in richness between the ponds with low and high productivity. Rarefaction Curves FIG. 6. Rarefaction curves of observed OTU richness in human mouth (E) and gut (F) bacterial samples. The error bars are 95% CIs and were calculated from the variance of the number of OTUs drawn in 100 randomizations at each sample size. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 !27
    • Observed vs. Estimated Alpha Diversity 4402 MINIREVIEW A CASE STUDIE In terms of both underlying assump be evaluated, nonparametric estimato for assessing microbial diversity. To potential, we applied these technique sets. In particular, we compared the estimators with the rarefaction approa the precision of their estimates cha These four data sets were among th represented a range of habitat types a ents. We came across a number of a would also have been appropriate for although others of comparable size analyzed with these techniques (5, 45 The analyses were performed with E FIG. 3. Observed and estimated OTU richness of bacteria in a human mouth (33) versus sample size. The number of OTUs observed R. Colwell, University of Connecticut for a given sample size, or the accumulation curve, is averaged over 50 .edu/estimates]). For the purposes of simulations HUGHES, JESSICA J. HELLMANN, TAYLOR H. RICKETTS, JENNIFER B. (E). Estimated OTU richness is plotted for Chaol (F) and program, we treated each cloned sequ ACE (Œ) estimators. AND BRENDAN J. M. BOHANNAN ple. We ran 100 randomizations for a Department of Biological Sciences, Stanford University, izations did not change the results. Stanford, California 94305-5020 Human mouth and gut. Two of the lem of not being able to measure bias. (This assumes that the communities are from human habitat Volume 67, no. 10, p. 4399–4406, 2001.estimator does not differ so3: lines 4 and 5 should read “. . subgingival plaque from a human bias of an Page 4402, legend to Fig. radically among compled .simulations (Œ). Estimated OTU richness is plotted for Chao1 (⅜)that it disrupts the relative order of the estimates. In munities and ACE (ⅷ) estimators.” to amplify the bacterial 16S rDNA, cre the absence of alternative evidence, this initial assumption the amplified DNA, and then sequen seems appropriate.) al. defined an OTU as a 16S rDNA s Chao (8) derives a closed-form solution for the variance of sequences differed by Յ1%. By this d SChao1: distinct OTUs from their sample of 26 Although the accumulation curve does 4 2 m n1 it is not can Slides for UC Davis EVE161 m Course3 Taught by Jonathan Eisen Winter 2014 linear (Fig. 3). Thus, we !28 tr Var͑S ͒ϭn ϩm ϩ , where m ϭ ERRATUM Counting the Uncountable: Statistical Approaches to Estimating Microbial Diversity ͩ ͪ
    • me spatial scale, would be higher est, however, that man mouth or in to our ability to are kilometers of munities, microbiecologists use to organisms. —are too diverse eful to know the nities, most diveracross biotic and ctivity, area, latirs to these quesies among sites, mens. Using this versity and many d (50, 57, 63, 64), ative exponential function (61). The benefit of estimating diversity with such extrapolation methods is that once a species has been counted, it does not need to be counted again. Hence, a surveyor can focus effort on identifying new, generally rarer, species. The downside is that for diverse communities in which Rank Abundance Curves ALIF D AV IS on M a y 1 1 , 2 0 1 0 unities have been ves. The species he x axis, and the n the y axis. The a similar pattern mmunities such as re abundant, but tail on the rank- FIG. 2. Rank-abundance curves for (a) tropical moths (n ϭ 4,538) (56) and (b) temperate soil bacteria (n ϭ 137) (39). The two most abundant species of moths (396 and 173 individuals) are excluded from panel a to shorten the y axis. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
    • Diversity 2: Beta Diversity • Beta diversity is (basically) a measure of the similarity in diversity between samples • Types of beta diversity ! Species presence/absence ! Shared phylogenetic diversity ! Gene presence / absence ! Shared phylogenetic diversity of genes ! • Frequently used as values for PCA of PCoA analysis Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 !30
    • ARTICLES Variability in Health vs. Disease 40 PC2 • • • • 30 • •• • • Ulcerative colitis • • • • • • • • • • • • • • PC1 • • • • Healthy • • 20 10 • • Cluster (%) • • Crohn’s disease • • 0 P value: 0.031 • • • 1 • Figure 4Figure 4 | Bacterialspecies abundance differentiates IBD patients and | Bacterial species abundance differentiates IBD patients and healthy individuals. healthy individuals. Principal component analysis with health status as instrumentalQin et al. 2010. Nature.on the abundance of 155 species with $1% variables, based genome coverage by the Illumina reads in at least 1 individual of the cohort, It iscarried out with 14go backwards from these patterns to was possible to healthy individuals and 25 IBD patients (21 ulcerative colitis or 4 Crohn’s disease) the clustering patters from Spain taxa and genes drive were plotted(Supplementary Table 1). Two first components (PC1 and PC2) and represented 7.3% of whole Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 inertia. Individuals (represented by points) were clustered and centre of Figure 5 | C were ranked length and c clusters with groups of 1 that contain see which were withi !31 This sugge