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Nicola Segata and Nick Loman Principal Investigator Laboratory of Computational Metagenomics Centre for Integrative Biology University of Trento Italy.

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Presentation on theme: "Nicola Segata and Nick Loman Principal Investigator Laboratory of Computational Metagenomics Centre for Integrative Biology University of Trento Italy."— Presentation transcript:

1 Nicola Segata and Nick Loman Principal Investigator Laboratory of Computational Metagenomics Centre for Integrative Biology University of Trento Italy Web Valley S sequencing for microbiome studies 1

2 The human microbiome 10x more microbial than human cells 1M times as many microbes inside each of us than humans on earth 100x more microbial than human genes Metagenomics: Who’s there? What are they doing? Study of uncultured microorganisms from the environment, which can include humans or other living hosts Focus on taxonomic and functional characteristics of the total collection of microorganisms within a community Main experimental tool is high-throughput sequencing: ~10M short (~100nt) reads per dataset Nature 486(7402) Scientific American, May 2012

3 Liu, Bo, et al. "Accurate and fast estimation of taxonomic profiles from metagenomic shotgun sequences." BMC genomics 12.Suppl 2 (2011): S4. 16S sequencing PROS: Cost-effective Avoids non-bacterial contamination The resulting dataset is reasonable in size and complexity Mature analysis software available Can potentially catch low abundance bacteria CONS: Not genome-wide (so no metabolic potential) Limited taxonomic resolution Not effective for pathogen profiling Cannot catch viruses and eukaryotes Several (usually underestimated) biases Almost impossible cross-study comparisons

4 16S-based “metagenomics” 4 PCR to amplify the single 16S rRNA marker gene George Rice, Montana State University Classify sequence  microbe Samples Microbes Counts V6 V2

5 Ribosomes are the universal machinery that translate the genetic code into proteins. The ribosomal machinery is composed by: Two subunits several proteins mRNAs tRNAs rRNA (5S, 16S, 23S) The ribosome

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8 Center for Molecular Biology of RNA, University of California The 16S rRNA

9 The 16S rRNA gene 1/3 Baker, G. C., J. J. Smith, and Donald A. Cowan. JMMs 55.3 (2003): This annotation has been performed on a representative E. coli 16S sequence

10 The 16S rRNA gene 2/3

11 The 16S rRNA gene 3/3

12 Center for Molecular Biology of RNA, University of California The 16S rRNA V1 V2 V3 V4 V5 V6 V7 V8 V9 V1 V2 V3 V4 V5 V6 V7 V8 V9

13 1 16S: The 530 loop structure of six species

14 The 16S gene: statistical view of the variable regions Variability within the 16S rRNA gene V1 V2 V3 V4 V5 V6 V7 V8 V9 Andersson, Anders F., et al. " PloS one 3.7 (2008) Claesson, Marcus J., et al. Nucleic acids research (2010) Multiple variable regions can be targeted simultaneously (if you have long enough reads!) Which HTM would you choose? 454 historically well suited (~400nt reads  3 regions), good cost/throughput trade-off Illumina (HiSeq) is not optimal (shorter reads, unnecessary high throughput) Illumina MiSeq and IonTorrent can be a nice compromise.

15 Which HTM would you choose? Throughput Very low (~1 seqs / sample) Medium (~3k seqs / sample) High (~50k seqs / sample)

16 The data revolution is now 16

17 One of the challenges: which technology? 17

18 Mol Ecol Resour Sep;11(5): One of the challenges: which technology? 18

19 Mol Ecol Resour Sep;11(5): One of the challenges: which technology? 19

20 In silico primer validation/testing The idea: use the available (taxonomically labeled) 16S sequences to check which organisms are targeted by the primers (to test single probes) (to test pairs of probes, below)

21 Fw: CCTACGGGRSGCAGCAG Rev: ATTACCGCGGCTGCT (our primers) An example on “universal” primers

22 Archaea, 49.2% matches Bacteria, 94.7% matches Proteobacteria, 97.1 % matches WS6 candidate division, 2.9 % matches An example on “universal” primers BE AWARE: universal primers do not exists, and the choice of the primers is going to bias your study no matter what!

23 Validation of hypervariable regions using a mock community Ward, Doyle V., et al. PloS one 7.6 (2011): e39315-e39315.

24 Variability within hyper variable regions

25 Hamady, Micah, and Rob Knight. Genome research 19.7 (2009): A high level 16S analysis workflow

26 CAAGCCGAAUGCAGCUAUUC CAAGCCUGAUGCAGCCAUGC CAUGCCUGAGACAGCCUUGC CAAGCCUGAUGCAGCCAUGC CAAGCCGAAUGCAGCUAUCC CAAGGCUGAGACAGCCUUGC CAAGCCUGAUGCUGCCAUGC CAAGCCGAAUGCAGCUAUGC CAAGCCGGAGACAGCCUUGC AAAGCCUGAUGCAGCCAUGC CAAGCCGAAUGCAGCUAUUC CAAGCCUGAUGCAGCCAUGC CAUGCCUGAGACAGCCUUGC CAAGCCUGAUGCAGCCAUGC CAAGCCGAAUGCAGCUAUCC CAAGGCUGAGACAGCCUUGC CAAGCCUGAUGCUGCCAUGC CAAGCCGAAUGCAGCUAUGC CAAGCCGGAGACAGCCUUGC CAAGCCUGAUGCAGCCAUGC CAAGCCGAAUGCAGCUAUUC CAAGCCGAAUGCAGCUAUCC CAAGCCGAAUGCAGCUAUGC CAUGCCUGAGACAGCCUUGC CAAGGCUGAGACAGCCUUGC CAAGCCGGAGACAGCCUUGC CAAGCCUGAUGCAGCCAUGC CAAGCCUGAUGCUGCCAUGC AAAGCCUGAUGCAGCCAUGC Input dataset (one sample) Multiple-sequence alignment Operational taxonomic unit (OTUs) definition OTU_1 OTU_2 OTU_3 16S DB with taxonomic information OTU_1  E. coli OTU_2  S. aureus OTU_3  S. pneumoniae OTU_1  30% OTU_2  30% OTU_3  40% OTU_1 OTU_2 OTU_3 Schematic 16S analysis workflow

27 Jurasinski, G., Retzer, V., & Beierkuhnlein, C. (2009). Oecologia, 159(1), Alpha-diversity A measure of how diverse (complex) a microbial community is “within sample” diversity Species richness (i.e. number) is a widely use alpha diversity index Beta-diversity A measure of how different two microbial communities are “between sample” diversity Inverse of number of shared species is one possibility to estimate beta-diversity Intro into diversity analysis

28 Practical tutorial time


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