QIIME and the art of fungal community analysis. Greg Caporaso

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1 QIIME and the art of fungal community analysis Greg Caporaso

2 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 OTU (or other sample by observation) table Phylogenetic Tree Evolutionary relationship between OTUs Denoise 454 Data Database Submission α-diversity and rarefaction β-diversity and rarefaction PyroNoise, Denoiser (In development) e.g., Phylogenetic Diversity, Chao1, Observed Species e.g., Weighted and unweighted UniFrac, Bray- Curtis, Jaccard Pick OTUs and representative sequences Reference based BLAST, UCLUST, USEARCH De novo e.g., UCLUST, CD-HIT, MOTHUR, USEARCH Interactive visualizations e.g., PCoA plots, distance histograms, taxonomy charts, rarefaction plots, network visualization, jackknifed hierarchical clustering. Assign taxonomy BLAST, RDP Classifier Align sequences e.g., PyNAST, INFERNAL, MUSCLE, MAFFT Legend marker-gene data only general sample by observation data Build 'OTU table' i.e., sample by observation matrix Build phylogenetic tree e.g., FastTree, RAxML, ClearCut (i.e., 'upstream' step) Required step or input (i.e., 'downstream' step) Optional step or input

3 QIIME is not (just) a 16S pipeline! 16S 18S nihh ITS Phage metagenome Coming soon: General metagenomics and metatranscriptomics

4 OTU picking De Novo Reads are clustered based on similarity to one another. Reference- based Closed reference: any reads which don t hit a reference sequence are discarded Open reference: any reads which don t hit a reference sequence are clustered de novo

5 De novo OTU picking Pros All reads are clustered Cons Not parallelizable OTUs may be defined by erroneous reads

6 Closed- reference OTU picking Pros Built- in quality filter Easily parallelizable OTUs are defined by high- quality, trusted sequences Cons Reads that don t hit reference dataset are excluded, so you can never observe new OTUs

7 Percentage of reads that do not hit the reference collecyon, by environment type.

8 Open- reference OTU picking Pros All reads are clustered ParYally parallelizable Cons Only par$ally parallelizable Mix of high quality sequences defining OTUs (i.e., the database sequences) and possible low quality sequences defining OTUs (i.e., the sequencing reads)

9 Sequencing output (454, Illumina, Sanger) fastq, fasta, qual, or sff/trace files Metadata mapping file Processing amplicon data with no reference sequences Pre-processing e.g., remove primer(s), demultiplex, quality filter OTU (or other sample by observation) table Phylogenetic Tree Evolutionary relationship between OTUs Pick OTUs and representative sequences α-diversity and rarefaction e.g., Phylogenetic Diversity, Chao1, Observed Species β-diversity and rarefaction e.g., Weighted and unweighted UniFrac, Bray- Curtis, Jaccard Reference based BLAST, UCLUST, USEARCH De novo e.g., UCLUST, CD-HIT, MOTHUR, USEARCH Interactive visualizations e.g., PCoA plots, distance histograms, taxonomy charts, rarefaction plots, network visualization, jackknifed hierarchical clustering. Align sequences e.g., PyNAST, INFERNAL, MUSCLE, MAFFT Legend marker-gene data only general sample by observation data Build 'OTU table' i.e., sample by observation matrix Build phylogenetic tree e.g., FastTree, RAxML, ClearCut (i.e., 'upstream' step) Required step or input (i.e., 'downstream' step) Optional step or input

10 Sequencing output (454, Illumina, Sanger) fastq, fasta, qual, or sff/trace files Metadata mapping file Processing amplicon data with no reference tree Pre-processing e.g., remove primer(s), demultiplex, quality filter OTU (or other sample by observation) table Phylogenetic Tree Evolutionary relationship between OTUs Pick OTUs and representative sequences α-diversity and rarefaction e.g., Phylogenetic Diversity, Chao1, Observed Species β-diversity and rarefaction e.g., Weighted and unweighted UniFrac, Bray- Curtis, Jaccard Reference based BLAST, UCLUST, USEARCH De novo e.g., UCLUST, CD-HIT, MOTHUR, USEARCH Interactive visualizations e.g., PCoA plots, distance histograms, taxonomy charts, rarefaction plots, network visualization, jackknifed hierarchical clustering. Assign taxonomy BLAST, RDP Classifier Align sequences e.g., PyNAST, INFERNAL, MUSCLE, MAFFT Legend marker-gene data only general sample by observation data Build 'OTU table' i.e., sample by observation matrix Build phylogenetic tree e.g., FastTree, RAxML, ClearCut (i.e., 'upstream' step) Required step or input (i.e., 'downstream' step) Optional step or input

11 This work is licensed under the CreaYve Commons AZribuYon 3.0 United States License. To view a copy of this license, visit hzp://creayvecommons.org/licenses/by/3.0/us/ or send a lezer to CreaYve Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA. Feel free to use or modify these slides, but please credit me by placing the following azribuyon informayon where you feel that it makes sense: Greg Caporaso,

Data analysis of 16S rrna amplicons Computational Metagenomics Workshop University of Mauritius

Data analysis of 16S rrna amplicons Computational Metagenomics Workshop University of Mauritius Practical December 2014 Exercise options 1) We will be going through a 16S pipeline using QIIME and 454 data