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Genboree Microbiome Workbench 16S Workshop Part I

Genboree Microbiome Workbench 16S Workshop Part I. March 11 th , 2014 Julia Cope Emily Hollister Kevin Riehle. Genboree Workflow. Create Group Create Database Create Project Upload Files  Create Samples (Sample Import using metadata file) 

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Genboree Microbiome Workbench 16S Workshop Part I

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  1. Genboree Microbiome Workbench 16S Workshop Part I March 11th, 2014 Julia Cope Emily Hollister Kevin Riehle

  2. Genboree Workflow • Create Group • Create Database • Create Project • Upload Files  • Create Samples (Sample Import using metadata file)  • Link Samples to Sequence Files (Sample File Linker)  • QC and Attach Sequences (Sequence Import)  • QIIME    • RDP 

  3. Data Analysis - QIIME • How to select samples for analysis • Chimera removal and why you should be thinking about it • Output • downloading and organization • making sense of the files

  4. Data Analysis - QIIME • How to select samples for analysis

  5. Data Analysis - QIIME • Selecting samples for analysis • INPUT = One or more Sequence Import folders • All should be of the same variable region; ideally produced with the same primer and sequencing direction • OUTPUT Targets = Your database (required), your project (optional)

  6. Data Analysis - QIIME Caveats: • All samples in your input folder will be analyzed • This includes no-template controls and positive controls • The % variation explained by you PCoA may be influenced by the inclusion of these samples • QIIME on Genboree is not currently set up to allow users to subsample their data • This can be problematic if sequencing depth varies substantially across samples • It does however perform a “rounding up” normalization step

  7. A bit about sequencing depthHow deep should you go? There is no good answer Strong biological patterns can be detected with low sequencing depth • 10s to 100s of sequences can sometimes be enough • 1000s tend to be the norm Subtle biological patterns tend to require greater sequencing depth for detection Sequencing depth can be dictated by: • Sample quality • The number of samples placed on a run • Project budget Kuczynzski et al. 2010 Nature Methods 7: 813-819

  8. Unequal sequencing depthWhat’s the problem? Being certain that you are seeing the full view (…or at least equivalent glimpses of the) of your communities http://www.cs.unc.edu/~lguan/Research.files/backgroundSubtractionResult.JPG

  9. Unequal sequencing depthWhat’s the problem? Unequal depth Avg Red = 5995 seqs Avg Blue = 11672 seqs Same data set Sampled are colored by library size Red ~4000 Orange ~5000 Yellow ~6000 Green 8,000-10,000 Blues 11,000-17,000

  10. Unequal sequencing depthWhat’s the problem? Unequal depth Avg Red = 5995 seqs Avg Blue = 11672 seqs Equal depth All libraries were sub-sampled to ~4000 reads.

  11. Data Analysis - QIIME • Chimera removal and why you should be thinking about it • What is a chimeric sequence? • How frequently do they occur? • An example from real data • Why should you think about chimeras? • How to screen for chimeras using Genboree

  12. What is a Chimeric Sequence? • In Greek mythology: • A creature that was an amalgam of multiple animals • Body of a lion, head of a goat, tail resembling a snake • In your sequence data: • The combination of multiple sequences during PCR to create a hybrid • In sequence databases: • A not-so-small nightmare of junk data • Mis-annotation • Enhanced “discovery” of novel organisms Chimera generation figure from: Haas et al. 2011, Genome Research 21:494-504

  13. How frequently do chimeras occur? Parent 1 Parent 1 AATCGCGACCTGTTTAACCGTAGGTC AATCGCGACCTGTTTAACCGTAGGTC • Schloss et al 2011: • With mock communities of known composition: • ~8% of raw sequences were chimeric • Incidence increased with sequencing depth • Approaches for detection: • Multiple algorithms available • Genboree uses ChimeraSlayer • How it works: • The ends of each read (~30% of total length) are compared to a chimera-free reference database • Potential “parent” sequences are identified • Identity of potential chimera to in silico chimera evaluated Query Query AATCGCGACCTGTGCTACACGGGTA AATCGCGACCTGTTTAACCGTAGGTC Parent 2 Parent 2 AAACGCTTACGGAGCTACACGAGTC AAACGCTTACGGAGCTACACGGGTA Likely Chimera Non-chimera Schloss et al. 2011 PLoS ONE 6(12):e27310

  14. An example from real data Alignment of chimeric sequences derived from Streptococcus (top, red) and Staphylococcus (bottom, black) Sequences were generated from 4 replicate PCR reactions/454 runs of V3V5 sequence Chimeric alignment from: Haas et al. 2011, Genome Research 21:494-504

  15. Why should you think about chimeras? • Spurious results • Artificially increases estimates of richness and diversity • You may discover a “new” (but fake) species • Should you trust all flagged chimeras? • Most people do but….buyer beware • False-positive rates are in the 1-4% range • Some taxa are poorly represented in reference databases • Prevotella and Acinetobacter are known to produce false-positive results in ChimeraSlayer • How to verify (digging in to your QIIME output) • Obtain representative sequence(s) and verify their identity (e.g., BLAST vs. NCBI nt database, RDP SeqMatch) Sogin et al 2006 PNAS 103:12115-12120

  16. How to screen chimeras in Genboree • Run a QIIME job • INPUT = Sequence Import folder • OUTPUT Targets = Your database (required), your project (optional)

  17. How to screen chimeras in Genboree • Select “Remove Chimeras” in the Tool Settings dialogue box • Provide a study name • Provide a job name (TIP: add chimeras_removed to you job name so that your output reflects that you selected this option) • Click SUBMIT

  18. Data Analysis - QIIME • Output • downloading and organization • making sense of the files

  19. How do I get my files out? • Entire folders can be archived/downloaded • INPUT = Folder to be archived • OUTPUT = Database to house archive

  20. How do I get my files out? • Entire folders can be archived/downloaded • Provide and archive name • Choose your compression type • Decide if you want the directory structure to be preserved • SUBMIT

  21. How do I get my files out? • Single files, including archives, can be downloaded one by one • Click on your file of interest in the DATA SELECTOR window • Click on the “Click to Download File” link in the DETAILS window • Save the file to your computer or storage drive • Most file types will require decompression

  22. QIIME – making sense of the files • fasta.result.tar.gz • jobFile.json • mapping.txt • otu.table • phylogenetic.result.tar.gz • plots.result.tar.gz • raw.results.tar.gz • repr_set.fasta.ignore • sample.metadata • settings.json • taxonomy.result.tar.gz

  23. QIIME – making sense of the files • fasta.result.tar.gz: multiple sequence alignment of your representative sequences file. Rep seqs = representative sequence for each OTU. • jobFile.json: a log of the settings used by Genboree to run your analysis • mapping.txt: a QIIME-compatible metadata file, includes barcode information • otu.table: a spreadsheet of OTU by sample distributions • phylogenetic.result.tar.gz: a phylogenetic tree of your rep seqs, additional files required for iTOL • plots.result.tar.gz: figures, html files for all PCoA plots produced in your QIIME run • raw.results.tar.gz: mapping file, otu table, rep seqs file, distance matrices underlying all PCoA calculations • repr_set.fasta.ignore: RDP classification (with confidence scores) of each rep seq • sample.metadata: like the mapping.txt file, with additional file locations for Genboree • settings.json: similar to the jobFile.json file • taxonomy.result.tar.gz: taxonomic summaries (per sample, at the Kingdom, Phylum, Class, Order, Family, and Genus levels)

  24. Genboree Workflow • Create Group • Create Database • Create Project • Upload Files  • Create Samples (Sample Import using metadata file)  • Link Samples to Sequence Files (Sample File Linker)  • QC and Attach Sequences (Sequence Import)  • QIIME    • RDP 

  25. Data Analysis - RDP • How to select samples • Output • Downloading and organization • making sense of the files

  26. Data Analysis - RDP • Selecting samples for analysis • INPUT = One or more Sequence Import folders • All should be of the same variable region; ideally produced with the same primer and sequencing direction • OUTPUT Targets = Your database (required), your project (optional)

  27. Data Analysis - RDP Caveats: • All samples in your input folder will be analyzed • This includes no-template controls and positive controls • RDP on Genboree does not pre-filter for chimeric sequences • RDP on Genboree is not currently set up to allow users to subsample their data • Depending on your application, this may be problematic if sequencing depth varies substantially across samples • It does however perform a “rounding up” normalization step and presents data on a relative abundance basis

  28. How do I get my files out? • Entire folders can be archived/downloaded • INPUT = Folder to be archived • OUTPUT = Database to house archive

  29. How do I get my files out? • Entire folders can be archived/downloaded • Provide and archive name • Choose your compression type • Decide if you want the directory structure to be preserved • SUBMIT

  30. How do I get my files out? • Single files, including archives, can be downloaded one by one • Click on your file of interest in the DATA SELECTOR window • Click on the “Click to Download File” link in the DETAILS window • Save the file to your computer or storage drive • Most file types will require decompression

  31. RDP – making sense of the files • domain.result.tar.gz • phylum.result.tar.gz • class.result.tar.gz • order.result.tar.gz • family.result.tar.gz • genus.result.tar.gz • sample.metadata • settings.json • count.result.tar.gz • count.xlsx • count_normalized.xlsx • weighted.xlsx • weighted_normalized.xlsx • png.result.tar.gz

  32. RDP – making sense of the files • domain.result.tar.gz • phylum.result.tar.gz • class.result.tar.gz • order.result.tar.gz • family.result.tar.gz • genus.result.tar.gz • sample.metadata • settings.json • count.xlsx • count_normalized.xlsx • weighted.xlsx • weighted_normalized.xlsx • png.result.tar.gz Per sample summaries at various taxonomic levels, including raw counts and weighted values Per sample summaries at various taxonomic levels, raw counts or relative abundances (normalized) Per sample summaries at various taxonomic levels, weighted by confidence of ID assignments (raw counts or normalized) All of the plots produced during your run (e.g., heatmaps, stacked bar graphs)

  33. Individual Time • Confirm user accounts are created. • Confirm users know where mock data or their data set are.

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