Methods for Microbiome Analysis

Transcription

1 Classroom Lecture 4 December 2015 Methods for Microbiome Analysis James R. Cole Director, RDP (Ribosomal Database Project) Center for Microbial Ecology Michigan State University East Lansing, Michigan U.S.A.

2 Michigan State University First land-grant university in USA, est teaching of practical agriculture, science, and engineering 50,000 students (11,000 graduate students) College of Agriculture & Natural Resources Dept. of Plant Soil and Microbial Sciences Center for Microbial Ecology Study microbes in the environment and their interactions

3 Some Recent Projects Databases and tools for microbial systematics and identification - NSF, DOE Standards development for metadata - NSF Effect of global warming on permafrost and soil carbon release - DOE Microbial interactions with priority pollutants -NIEHS Biofuel crops on marginal land - DOE, USDA Microbiome and establishment of ulcerative colitis NIH Antibiotic resistance gene transmission from farm animals to humans - USDA

4 Microbes are Dominant in Biosphere Biomass: Prokaryotes 4-6 x cells, containing: Pg of C (0.6-1 X plants) Pg of N (10 X plants) 9-14 Pg of P (10 X plants) Environments: Open ocean 0.12 x cells Soil 0.26 x cells Oceanic subsurface 3.5 x cells Terrestrial subsurface x cells Biodiversity: 400,000 4,000,000 species Whitman et al. (1998) PNAS 95:

5 Can you name these bacteria? From: Ch Terrestrial Bacteria from Agricultural Soils: By Masoomeh Shams-Ghahfarokhi, Sanaz Kalantari and Mehdi Razzaghi-Abyaneh DOI: /

6 This is how we think about diversity 5

7 Phylogenetic Tree of Life Three domains of life based on the work of Carl Woese and colleagues

8 Elucidation of the three domains Carl Woese ( ) of life Ribosomal RNA sequence as phylogenetic marker Discovered 3 rd kingdom Archaea and Bacteria separate domains 7

9 Ribosomes Universal Marker Ribosomes are the protein synthesis factories Subunits 30S 50S rrna 16S 23S 5S Core function present in all cellular organisms Very little evidence of horizontal gene transfer Historically easy to work with Purify by centrifugation and extract rrna. Now we use PCR to amplify from genomic DNA rrna genes have conserved regions interspersed with highly variable regions. Conserved regions used for both PCR primers and sequencing primers. 8

10 Secondary structure of small-subunit ribosomal RNA 5' end 3' end Image adapted from R. Gutell 9

11 Secondary structure of small-subunit ribosomal RNA 5' end 3' end Image adapted from R. Gutell 10

12 The 530 Loop of E. coli Loop Non-canonical G-U basepair Bulge Stem with canonical Watson-Crick base pairing 11

13 The 530 loop structure of six species 1 12

14 Phylogenetic tree using sequence information to represent evolutionary history 63 EschC MeOVanni ThlJanna PrnFreu AqxPyrop SflSalf

15 Diversity of uncultured organisms explored by rrna sequencing David A. Stahl, David J. Lane, Gary J. Olsen and Norman R. Pace Science, New Series, Vol. 224, No (Apr. 27, 1984), pp Published by: American Association for the Advancement of Science

16 Hydrothermal Vent Black Smoker 15

17 Metagenomics studies the genomes of organisms as communities iology/digestive_sys tem Microscopic algae in surface waters depend on nitrate, only available in deep waters. Credit: Kim Fulton- Bennett, MBARI Reference: K.S. Johnson, S.C. Riser, D.M. Karl. Nitrate supply from deep to nearsurface waters of the North Pacific subtropical gyre. Nature. Vol 465, Issue June, Figure credit: Ed Zaborski, University of Illinois. Adapted from House and Parmelee (1985). Soil Fertility in Organic Farming Systems: Much More than Plant Nutrition, Last Updated: April 04, 2011, View as web page, Author: Michelle Wander, U of Illinois 16

18 Microbes are Important in Human Health 17

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20 Hess et al SCIENCE 331:

21 Explosion in rrna Sequencing By 2008, the majority of all bacterial sequences submitted to GenBank were 16S rrna sequences Less than 2% of these had a Latin name attached (valid or not) (R. Christen, 2008)

22 Most of our knowledge of bacterial diversity comes only from rrna sequences 21

23 Three examples from our work Sequence Nearest-Neighbor Match: (Last Common Ancestor) RDP Classifier: Naïve Bayesian FrameBot Dynamic Programming

24 Gene-Targeted Metagenomic Surveys Illumina myseq Use of key (tag) sequences for multi-sample sequencing Primers target informative region Sequence 10 to 100s of samples per run 23

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29 Gene-Targeted Sequencing ~1x10^5 genome copies / ng DNA ~1x10^3 PCR amplification (~1.6^cycles) ~1x10^8 gene copies To produce 1x10^3 sequence reads 28

30 Phylogenetic Analysis vs. Classification Classification is conceptually easier to interpret. Often preferred when the groups are well understood. Phylogenetic methods are preferred for new groups or when the placement is not clear. 29

31 Machine Learning in Microbiology Machine learning focuses on prediction, based on known properties learned from the training data. Wikipedia 30

32 Taxonomic Classifiers Use concepts from Machine Learning Many algorithms can be applied Sequence Match Nearest-Neighbor (Last Common Ancestor) RDP Classifier Naïve Bayesian 31

33 Can Represent a Sequence as a Set of Overlapping k-mers GAAGCACCGGCUAACUCCGUGCCAGCAGCCGCGGUAAUACGGAGGGUGCAAG GAAGCAC UCCGUGC GCGGUAA AAGCACC CCGUGCC CGGUAAU AGCACCG CGUGCCA GGUAAUA GCACCGG GUGCCAG GUAAUAC CACCGGC UGCCAGC UAAUACG ACCGGCU GCCAGCA AAUACGG CCGGCUA CCAGCAG AUACGGA CGGCUAA CAGCAGC UACGGAG GGCUAAC AGCAGCC ACGGAGG GCUAACU GCAGCCG CGGAGGG CUAACUC CAGCCGC GGAGGGU UAACUCC AGCCGCG GAGGGUG AACUCCG GCCGCGG AGGGUGC ACUCCGU CCGCGGU GGGUGCA CUCCGUG CGCGGUA GGUGCAA 32

34 SeqMatch NN Nearest-neighbor classifier k-nn k nearest-neighbors classifier LCA Last common ancestor (Lowest common ancestor) 33

35 GAAGCAC AAGCACC AGCACCG GCACCGG CACCGGC ACCGGCU CCGGCUA CGGAGGG GGAGGGU AGGGUGC GGGUGCA GGUGCAA GUGCAAG CGGCUAA GGCUAAC GCUAACU CUAACUC UAACUCC AACUCCG ACUCCGU CUCCGUG UCCGUGC CCGUGCC CGUGCCA GUGCCAG UGCCAGC GCCAGCA CCAGCAG CAGCAGC AGCAGCC GCAGCCG CAGCCGC AGCCGCG GCCGCGG CCGCGGU CGCGGUA GCGGUAA CGGUAAU GGUAAUA GUAAUAC UAAUACG AAUACGG AUACGGA UACGGAG ACGGAGG GAAGGGA AAGGGAC AGGGACG GGGACGG GGACGGC GACGGCU ACGGCUA CGGAGGU GGAGGUC GAGGUCC AGGUCCC GGUCCCA GUCCCAA UCCCAAG SeqMatch Comparison E. coli A. pyrophilus 34

36 SeqMatch Math AB 35

37 LCA Last Common Ancestor Bacillaceae Bacillus Bacillus thuringiensis; WS 2625; Z84587 Bacillus cereus; AF Bacillus sp. No.49; AB Bacillus clausii; AJ Bacillus flexus (T); IFO15715; AB Bacillales Geobacillus Geobacillus stearothermophilus; DSM 22T; AJ Geobacillus caldoxylosilyticus; B70; AJ Listeriaceae Brochothrix Brochothrix campestris; NBRC 15547; AB Brochothrix thermosphacta; MF 88; AY Brochothrix campestris; DSMZ 4712; AY Listeria Listeria ivanovii (T); CLIP12229; X98529 Listeria grayi; NCTC10812; X

38 LCA Last Common Ancestor Bacillaceae Bacillus Bacillus thuringiensis; WS 2625; Z84587 Bacillus cereus; AF Bacillus sp. No.49; AB Bacillus clausii; AJ Bacillus flexus (T); IFO15715; AB Bacillales Geobacillus Geobacillus stearothermophilus; DSM 22T; AJ Geobacillus caldoxylosilyticus; B70; AJ Listeriaceae Brochothrix Brochothrix campestris; NBRC 15547; AB Brochothrix thermosphacta; MF 88; AY Brochothrix campestris; DSMZ 4712; AY Listeria Listeria ivanovii (T); CLIP12229; X98529 Listeria grayi; NCTC10812; X

39 LCA Last Common Ancestor Bacillaceae Bacillus Bacillus thuringiensis; WS 2625; Z84587 Bacillus cereus; AF Bacillus sp. No.49; AB Bacillus clausii; AJ Bacillus flexus (T); IFO15715; AB Bacillales Geobacillus Geobacillus stearothermophilus; DSM 22T; AJ Geobacillus caldoxylosilyticus; B70; AJ Listeriaceae Brochothrix Brochothrix campestris; NBRC 15547; AB Brochothrix thermosphacta; MF 88; AY Brochothrix campestris; DSMZ 4712; AY Listeria Listeria ivanovii (T); CLIP12229; X98529 Listeria grayi; NCTC10812; X

40 LCA Last Common Ancestor Bacillaceae Bacillus Bacillus thuringiensis; WS 2625; Z84587 Bacillus cereus; AF Bacillus sp. No.49; AB Bacillus clausii; AJ Bacillus flexus (T); IFO15715; AB Bacillales Geobacillus Geobacillus stearothermophilus; DSM 22T; AJ Geobacillus caldoxylosilyticus; B70; AJ Listeriaceae Brochothrix Brochothrix campestris; NBRC 15547; AB Brochothrix thermosphacta; MF 88; AY Brochothrix campestris; DSMZ 4712; AY Listeria Listeria ivanovii (T); CLIP12229; X98529 Listeria grayi; NCTC10812; X

41 RDP Classifier Naïve Bayesian classifier Uses training data to estimate k-mer likelihood Assignment based on genus for which k-mers are most common 40

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43 Bayesian Theory Incorporates a priori probability to calculate that, after seeing the evidence E, the a priori hypothesis H is true Likelihood evidence P(H E) = P(E H)P(H) P(E) A priori probability inferred before evidence was observed A priori probability of observing E 42

44 Find the genus that maximizes Genus G P(G V) P(G V) = P(V G)P(G) P(V) V = Set of 8-mer words in the query sequence S 43

45 Find the genus that maximizes P(G V) Assume all genera equally likely [P(G i )=P(G j )] P(G V) = P(V G)P(G) P(V) P(V) is not dependent on G. To maximize P(G V) Select the Genus that maximizes P(V G) 44

46 Estimating P(V G) CGGCUAA GUAAUAC ACGGAGG GCCGCGG B. subtilis + + B. clausii + + B. smithii + + B. flexus B. ruris + + Probability: 99% 80% 40% 01% P(V Bacillus) = ( ) = or 1 in 316 P(V Sinobaca) = ( ) = or 1 in k mer Set V When you have eliminated the impossible, whatever remains, however improbable, must be the truth. Sir Arthur Conan Doyle 45

47 Genes Beyond rrna Faster phylogenetic markers Full-length 16S rrna resolves genera, not species. Genes encoding important functions Ecologically important genes for carbon and nitrogen cycling, biogeochemical processes. Lateral gene transfer Gene phylogeny may not match the organism phylogeny.

48 Ribosomal RNA Shows the Framework

49 Functional Genes Show the Details Akasaka K Tower Residence from

50 Biogeochemical Cycles Nitrogen Cycle: Factors affecting nitrogen fixation, denitrification (greenhouse gas) Carbon Cycle: Factors affecting soil carbon sequestration, overall soil fertility, biofuel production Source: EPA 49

51 The nifh Gene is a Key Gene in Nitrogen Fixation Example: Trichodesmium thiebautii Forward Amplicon ~ 360 bp Start:112 End: 471 Reverse ~ 882 bp Zehr et al., Forward TGYGAYCCNAARGCNGA Reverse ADNGCCATCATYTCNCC Poly et al., Forward TGCGAYCCSAARGCBGACTC Reverse ATSGCCATCATYTCRCCGGA 50

52 National Ecological Observatory Network

53 NEON nifh Sequencing State Ecological Region Samples Sequences Alaska Taiga ,294 Florida Southeast 17 79,619 Hawaii Pacific Tropical ,824 Utah Great Basin 8 19,105 52

54 Frameshift Correction Standard: A G A G T G A r g V a l 454 Read: Frame 1: Frame 2: Frame 3: C G G g G T A A r g G l y - - G l y V a l - - G l y

55 RDP FrameBot > Sbjct Query NuclLen AlignLen %Identity Score Frameshifts Reversed STATS GF040U102B1BAI false Sbjct 76 GGMLKPIEGGTYEVNEAMVEDLKIGVQGPHASNLGGILSNEIAKEIGKRAFIVDPVVVDE 135 Query 61 GGMLKPIEGGTYEVNEAMVEDLKIGFEGPHAXNLGGILSNEIAKKLGKRAFIVDPVVVDX 120 Frame ggacacagggatggaggagggtaa4tggccg2atggattagagaatgaagtaggcgggg2 ggttactaggcaataacttaatat tagcac atggttcaatcaatgagctttacttta tagtgaaatattactatgtgcaat caaatt cataaattaaagaatgaataatgtttt insertion deletion mismatches 54

56 RDP FrameBot Wang et al., 2013 Extends a dynamic programming algorithm proposed by Guan et al., Comput Appl Biosci. 12:31-40 Requires a set of subject protein sequences Produces an optimal alignment between the query DNA and the subject protein sequences in the presence of frameshifts Returns the frameshift-corrected protein and DNA query sequences Reports the protein pairwise alignment with the best score 55

57 FrameBot Performance using reference sequences at various percentages of identity to query sequences Wang Q et al. mbio 2013; doi: /mbio

58 Using FrameBot to Find Nearest Known Neighbor Sequence 206 nifh references chosen from 1223 nifh genes from cultured bacteria, clustered at 10% identity. Naïve approach requires pairwise comparison with each reference, but assignment requires far more references than frameshift correction. Using a metric indexing strategy to reduce number of pairwise comparisons. 57

59 FrameBot Metric Index Requires a distance measure with metric properties (including triangle inequality). Simple edit distance is metric (global). Complex edit distance is only metric if the scoring matrix is a metric. Blosum 62 is not metric, but a metric approximation is available. Addition of frameshifts violates triangle inequality. In practice, can compensate by slightly relaxing indexing strategy. 58

60 R 2 Q R 3 δ R 1 59

61 Processing NEON nifh Samples 8 hrs to process 1.1 million using index. 92.7% within 90% identity of reference. 99.9% within 80% identity of reference. 12.7% reads had at least 1 frameshift. About half of the reference sequences were closest matches to at least one read. 60

62 Relative Abundance of NEON Reads grouped by nearest matches at the phylum and class levels, averaged for each site (observatory) as indicated by state Wang Q et al. mbio 2013; doi: /mbio

63 Principal Component Analysis of NEON Samples A B Wang Q et al. mbio 2013; doi: /mbio

64 RDP Specialized Tools for Environmental Gene Analysis Ribosomal Database Project: data and tools for high throughput rrna analysis James R. Cole, Qiong Wang, Jordan A. Fish, Benli Chai, Donna M. McGarrell, Yanni Sun, C. Titus Brown, Andrea Porras-Alfaro, Cheryl R. Kuske and James M. Tiedje (2014) Nucl. Acids Res. 41:D633-D

65 RDPʼs Popular Online Tools Interactive tools Browsers browse and select from taxonomic hierarchy; powerful search and selection features SeqMatch finds nearest neighbor, more accurate than BLAST RDP Classifier places sequences into bacterial taxonomy; fast and accurate ProbeMatch fast search algorithm, limit searches to specific regions RDPipeline and FunGene tools for gene targeted metagenomics MIMARKS GoogleSheets helps organizing standards Compliant metadata myrdp space upload and analyze your own 16S sequences in your private space 64

66 RDP Fungene Offers databases of many common ecofunctional genes and proteins Integrated tools allow researchers to browse these collections and choose subsets for further analysis, build phylogenetic trees, test primers and probes for coverage, and download aligned sequences Specialized tools to process coding gene amplicon data Fish, J. A., B. Chai, Q. Wang, Y. Sun, C. T. Brown, J. M. Tiedje, and J. R. Cole FunGene: the Functional Gene Pipeline and Repository. Front. Microbiol. 4:291 65

67 RDP Pipelines Initial Process barcode sorting and quality filtering raw reads, includes paired-end reads assembly Defined Community Analysis evaluate error patterns and rates on reads amplified from a mix of known organisms Ecological Measurement Jaccard & Sørensen Indices, Shannon & Chao1 Indices, Rarefaction Aligner Infernal model-based aligners for 16S and fungal 28S Complete-Linkage Clustering Cluster results in RDP, BIOM and other formats Updated d RDP Classifier places sequences into bacterial and fungal taxonomies; easy to train for additional genes 66

68 RDPTools Collection of commonly used RDP Tools for high throughput sequence processing and analysis. Package classifier RDP extensible sequence classifier for fungal large subunit rrna, bacterial & archaeal 16S rrna. Java ReadSeq Sequence file reader and format converter. Java Xander_assembler A gene targeted assembler tool for metagenomic sequences. Shell AlignmentTools Tools for pairwise sequence comparison, distance calculation, and hidden markov model sequence scoring (using HMMER3 models). Java Framebot Dynamic programming based frameshift detection and correction tool with nearest neighbor classification. Java Clustering RDP memory constrained hierarchical clustering tools. Java fungene_pipeline Scripts and resources for analyzing sequence data for select eco functional genes. Python KmerFilter Tool for kmer analysis. Java TaxonomyTree Taxonomy tree building and traversal utility tool. Java SeqFilters Tool for sorting and selecting nucleotide sequences according to given filters and tags. Java SequenceMatch K mer based sequence matching tool to calculate nearest neighbors of sequences. Java ProbeMatch Tool for finding (and removing) DNA/RNA primers in sequence reads. Java AbundanceStats Tool for generating various ecological abundance statistics. Java FungeneUtils Package of tools for protein sequence analysis. Java SOAP examples Code samples from various languages for interacting with RDP soap services. Perl gfclassify A gene family classifier that allows for fast and accurate classification of amplicons (or open reading frame) nucleotide sequences. C and Biopython

69 End of Lecture 68