genboree microbiome workbench 16s workshop part i march 11 th, 2014 julia cope emily hollister kevin...

Genboree Microbiome Workbench 16S Workshop Part I

March 11th, 2014Julia Cope

Emily HollisterKevin Riehle

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

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


• How to select samples for analysis


– 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)


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

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

Unequal sequencing depthWhat’s the problem?

http://www.cs.unc.edu/~lguan/Research.files/backgroundSubtractionResult.JPG

Being certain that you are seeing the full view (…or at least equivalent glimpses of the) of your communities


Unequal depthAvg Red = 5995 seqsAvg Blue = 11672 seqs

Same data setSampled are coloredby library sizeRed ~4000Orange ~5000Yellow ~6000Green 8,000-10,000Blues 11,000-17,000


Unequal depthAvg Red = 5995 seqsAvg Blue = 11672 seqs

Equal depthAll libraries weresub-sampled to ~4000 reads.


• 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

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

How frequently do chimeras occur?

– 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

Schloss et al. 2011 PLoS ONE 6(12):e27310

AATCGCGACCTGTTTAACCGTAGGTC


AAACGCTTACGGAGCTACACGAGTC

Query

Parent 1

Parent 2

AATCGCGACCTGTGCTACACGGGTA


AAACGCTTACGGAGCTACACGGGTA

Query

Parent 1

Parent 2

Likely Chimera

Non-chimera

An example from real data

Chimeric alignment from: Haas et al. 2011, Genome Research 21:494-504

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

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

How to screen chimeras in Genboree

– Run a QIIME job• INPUT = Sequence Import folder• OUTPUT Targets = Your database (required), your project (optional)

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


• Output– downloading and organization– making sense of the files

How do I get my files out?

– Entire folders can be archived/downloaded• INPUT = Folder to be archived• OUTPUT = Database to house archive


– 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


– 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

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

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)

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

Data Analysis - RDP

• How to select samples• Output– Downloading and organization– making sense of the files

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)

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


– Entire folders can be archived/downloaded• INPUT = Folder to be archived• OUTPUT = Database to house archive


– 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


– 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

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

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)

All of the plots produced during your run (e.g., heatmaps, stacked bar graphs)

Per sample summaries at various taxonomic levels, weighted by confidence of ID assignments (raw counts or normalized)

Individual Time

• Confirm user accounts are created.• Confirm users know where mock data or their

data set are.

genboree microbiome workbench 16s workshop part i march 11 th, 2014 julia cope emily hollister kevin...

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