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Introduction to 16S Analysis with NGS - BMR Genomics
This document provides an overview and primer on 16S amplicon sequencing and analysis for metagenomics. It discusses how 16S is a ubiquitous gene that can be used to compare microbial communities across samples, outlines common analysis steps like preprocessing, OTU picking, taxonomy assignment, and diversity metrics, and introduces two analysis tools - MEGAN and Qiime. Key advantages and limitations of the 16S amplicon approach are highlighted.
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Introduction to 16S Analysis with NGS - BMR Genomics
- 1. 16S Amplicons A primeron metagenomics Andrea Telatin and Eleonora Sattin BMR Genomics
- 2. De novo genome Exomeseq 16S Amplicons Out of the machine Introduction to bioinformatics You are here
- 3. Today’s menu:! • abiological perspective on 16S-seq • a short primer on bioinformatic analysis • introducing two tools: • MEGAN (GUI) • Qiime (pipeline)
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- 5. – J. C.Venter “whatever we look at, we are barely scratching the surface”
- 7. Flores G. E.et al., 2011 (PLoS One) Microbial Biogeography of Public Restroom Surfaces
- 8. Key questions • Whois out there? • What are they doing?
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- 11. 16S Amplicon PROS! • Ubiquitousgene • Contains both conserved and variable regions CONS! • Copy number variations • It’s only a (single) gene
- 12. metagenomics ≠ ampliconsequencing
- 13. Driving idea • Wellestablished method that can be used to compare different samples • At any step we introduce bias, that have to be taken into account • Sampling (replicate or lie) • Cell breakage (are you strong enough?) • Amplification (where do your primers come from?) • Sequencing (how good is your machine?) • Analysis (database annotation?)
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- 15. This is ahard example. ! That is another easy example.
- 16. This is a--hard---- example. || ||||| | | ||||||||| That is another easy example. This is a-- h-ard---- example. || ||||| | | ||||||||| That is anothe-r easy example. This is a hard example.------ || ||||| | | That is another easy example. GapCost
- 17. 1) AGT 2) AT 3)ATC 1) AGT 2) A–T 3) ATC 1) AGT 2) AT– 3) ATC 1) AGT - 2) A - T - 3) A - TC A B C A C B B C A INPUT
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- 19. Sequencing Reads Pre-Processing Denoising OTU Picking Taxonomicalclassification Alpha/Beta Rarefaction PCA
- 20. Sequencing output (454, Illumina,Sanger) fastq, fasta, qual, or sff/trace files Metadata mapping file Pre-processing e.g., remove primer(s), demultiplex, quality filter Denoise 454 Data PyroNoise, Denoiser Reference based BLAST, UCLUST, USEARCH Pick OTUs and representative sequences De novo e.g., UCLUST, CD-HIT, MOTHUR, USEARCH Assign taxonomy BLAST, RDP Classifier Align sequences e.g., PyNAST, INFERNAL, MUSCLE, MAFFT Build 'OTU table' i.e., sample by observation matrix Build phylogenetic tree e.g., FastTree, RAxML, ClearCut Database Submission (In development) OTU (or other sample by observation) table Phylogenetic Tree Evolutionary relationship between OTUs α-diversity and rarefaction e.g., Phylogenetic Diversity, Chao1, Observed Species β-diversity and rarefaction e.g., Weighted and unweighted UniFrac, Bray- Curtis, Jaccard Interactive visualizations e.g., PCoA plots, distance histograms, taxonomy charts, rarefaction plots, network visualization, jackknifed hierarchical clustering. Legend Required step or input Optional step or input Currently supported for marker-gene data only (i.e., 'upstream' step) Currently supported for general sample by observation data (i.e., 'downstream' step) www.QIIME.orgwww.QIIME.org
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- 23. Sequencing Reads Pre-Processing Denoising OTU Picking Taxonomicalclassification Alpha/Beta Rarefaction PCA
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