glbio/ccbc metagenomics workshop

GLBIO/CCBC Microbiome Analysis Workshop: Metagenomics

Morgan G.I. LangilleAssistant Professor

Dalhousie UniversityMay 16, 2016

Learning Objectives• Contrast 16S and metagenomic sequencing

• Taxonomy from metagenomes

• Function from metagenomes

• Applicability of assembling and gene calling with metagenomic data

• Metagenomic inference and limitations

• Tutorial on processing metagenomic data to determine functional and taxonomic profiles

16S vs Metagenomics• 16S is targeted sequencing of a single gene which acts as a

marker for identification• Pros

– Well established– Sequencing costs are relatively cheap (~50,000 reads/sample)– Only amplifies what you want (no host contamination)

• Cons– Primer choice can bias results towards certain organisms– Usually not enough resolution to identify to the strain level – Different primers are needed for archaea & eukaryotes (18S)– Doesn’t identify viruses

16S vs Metagenomics

• Metagenomics: sequencing all the DNA in a sample• Pros

– No primer bias– Can identify all microbes (euks, viruses, etc.)– Provides functional information (“What are they doing?”)

• Cons– More expensive (millions of sequences needed)– Host/site contamination can be significant– May not be able to sequence “rare” microbes– Complex bioinformatics

TAXONOMIC PROFILESWho is there?

Metagenomics: Who is there?

• Goal: Identify the relative abundance of different microbes in a sample given using metagenomics

• Problems:– Reads are all mixed together – Reads can be short (~100bp)– Lateral gene transfer

• Two broad approaches1. Binning Based2. Marker Based

Binning Based

• Attempts to group or “bin” reads into the genome from which they originated

• Composition-based– Uses sequence composition such as GC%, k-mers (e.g. Naïve

Bayes Classifier)– Generally not very precise

• Sequence-based– Compare reads to large reference database using BLAST (or

some other similarity search method)– Reads are assigned based on “Best-hit” or “Lowest Common

Ancestor” approach

LCA: Lowest Common Ancestor • Use all BLAST hits above a threshold and assign taxonomy at the lowest

level in the tree which covers these taxa.

• Notable Examples:– MEGAN: http://ab.inf.uni-tuebingen.de/software/megan5/

• One of the first metagenomic tools• Does functional profiling too!

– MG-RAST: https://metagenomics.anl.gov/• Web-based pipeline (might need to wait awhile for results)

– Kraken: https://ccb.jhu.edu/software/kraken/• Fastest binning approach to date and very accurate. • Large computing requirements (e.g. >128GB RAM)

Marker Based

• Single Gene• Identify and extract reads hitting a single marker gene (e.g.

16S, cpn60, or other “universal” genes)• Use existing bioinformatics pipeline (e.g. QIIME, etc.)

• Multiple Gene• Several universal genes

– PhyloSift (Darling et al, 2014)» Uses 37 universal single-copy genes

• Clade specific markers– MetaPhlAn2 (Truong et al., 2015)

Marker or Binning?

• Binning approaches– Similarity search is computationally intensive– Varying genome sizes and LGT can bias results

• Marker approaches– Doesn’t allow functions to be linked directly to

organisms– Genome reconstruction/assembly is not possible– Dependent on choice of markers

MetaPhlAn2

• Uses “clade-specific” gene markers• A clade represents a set of genomes that can be as

broad as a phylum or as specific as a species• Uses ~1 million markers derived from 17,000 genomes

– ~13,500 bacterial and archaeal, ~3,500 viral, and ~110 eukaryotic

• Can identify down to the species level (and possibly even strain level)

• Can handle millions of reads on a standard computer within a few minutes

MetaPhlAn Marker Selection

MetaPhlAn Marker Selection

Using MetaPhlan• MetaPhlan uses Bowtie2 for sequence similarity

searching (nucleotide sequences vs. nucleotide database)

• Paired-end data can be used directly

• Each sample is processed individually and then multiple sample can be combined together at the last step

• Output is relative abundances at different taxonomic levels

Absolute vs. Relative Abundance

• Absolute abundance: Numbers represent real abundance of thing being measured (e.g. the actual quantity of a particular gene or organism)

• Relative abundance: Numbers represent proportion of thing being measured within sample

• In almost all cases microbiome studies are measuring relative abundance– This is due to DNA amplification during sequencing

library preparation not being quantitative

Relative Abundance Use Case• Sample A:

– Has 108 bacterial cells (but we don’t know this from sequencing)– 25% of the microbiome from this sample is classified as Shigella

• Sample B:– Has 106 bacterial cells (but we don’t know this from sequencing)– 50% of the microbiome from this sample is classified as Shigella

• “Sample B contains twice as much Shigella as Sample A”– WRONG! (If quantified it we would find Sample A has more Shigella)

• “Sample B contains a greater proportion of Shigella compared to Sample A”– Correct!

FUNCTIONAL COMPOSITIONWhat are they doing?

What do we mean by function?

• General categories– Photosynthesis– Nitrogen metabolism– Glycolysis

• Specific gene families– Nifh – EC: 1.1.1.1 (alchohol dehydrogenase)– K00929 (butyrate kinase)

Various Functional Databases• COG

– Well known but original classification (not updated since 2003)

• SEED– Used by the RAST and MG-RAST systems

• PFAM– Focused more on protein domains

• EggNOG– Very comprehensive (~190k groups)

• UniRef– Has clustering at different levels (e.g. UniRef100, UniRef90, UniRef50)– Most comprehensive and is constantly updated

• KEGG– Very popular, each entry is well annotated, and often linked into “Modules” or “Pathways” – Full access now requires a license fee

• MetaCyc– Becoming more widely used.– More microbe focused than KEGG

KEGG• We will focus on using the

KEGG database during this workshop

• KEGG Orthologs (KOs)– Most specific. Thought to be

homologs and doing the same exact “function”

– ~12,000 KOs in the database– These can be linked into KEGG

Modules and KEGG Pathways, – Identifiers: K01803, K00231, etc.

KEGG (cont.)• KEGG Modules

– Manually defined functional units– Small groups of KOs that function together– ~750 KEGG Modules– Identified: M00002, M00011, etc.

KEGG (cont.)• KEGG Pathways

– Groups KOs into large pathways (~230)

– Each pathway has a graphical map

– Individual KOs or Modules can be highlighted within these maps

– Pathways can be collapsed into very general functional terms (e.g. Amino Acid Metabolism, Carbohydrate Metabolism, etc.)

Metagenomic Annotation Systems• Web-based

– Provide functional and taxonomic analysis, plus hosts your data.– EBI Metagenomics Server– MG-RAST– IMG/M

• GUI based– MEGAN

• Taxonomy and functional annotation– ClovR

• Virtual Machine based, contains SOP, hasn’t been updated recently

• Command-line based– MetAMOS

• Built in assembly, highly customizable, some features can be buggy

– Humann• Functional annotation

– DIY• Set up your own in-house custom computational pipeline

Humann

(Abubucker et al. 2012)

Humann Step 1• Reads are searched against a protein database (e.g. KEGG)

– Can use BLASTX, but much faster methods now available (e.g. BLAT, USEARCH, RapSearch2, DIAMOND)

Buchfink et al., 2015

Humann

(Abubucker et al. 2012)

Humann Step 2

• Normalize and weight search results• The relative abundance of each KO is calculated:

– Number of reads mapping to a gene sequence in that KO

– Weighted by the inverse p-value of each mapping

– Normalized by the average length of the KO

Humann

(Abubucker et al. 2012)

Humann Step 3

• Reduce number of pathways• A KO can map to one or more KEGG Pathways

– Just because a KO is found in a pathway doesn’t mean that complete pathway exists in the community

– If a pathway has 20 KOs and only 2 KOs are observed in the community (but at high abundances) what should be the abundance of the pathway?

– MinPath (Ye, 2009) attempts to estimate the abundance of these pathways and remove spurious noise

Humann

(Abubucker et al. 2012)

Humann Step 4

• Reduce false positive pathways further and normalize by KO copy number

• Using the organism information from the KEGG hits– Pathways that are not found to be in any of the

observed organisms AND are made up mostly of KOs mapping to a different pathway are removed

– KO abundance can be divided by the estimated copy number of that KO as observed from the KEGG organism database

Humann

Humann Step 5

• Smoothing pathways by gap filling– Sequencing depth or poor sequence searches

could lead to some KOs within pathways being absent or in low abundance

– KOs with 1.5 interquartile ranges below the pathway median are raised to the pathway median

Humann

(Abubucker et al. 2012)

What about assembly?

• Assembly is often used in genomics to join raw reads into longer contigs and scaffolds

Assembly for Metagenomics?• Pros

– Less computation time for similarity search (sequences are collapsed)– Can allow annotation when reads are too short (<100bp)– Can sometimes (partially) reconstruct genomes

• Cons– Assembly is computationally intensive (high memory machines needed)– Collapsed reads must be added back to get relative abundances (not all

assemblers do this natively) – Low read depth and high diversity can cause assemblers to fail– Reads are not all from the same genome so chimeras are possible– Some organisms/genes will assemble easier (e.g. more abundant) which

could lead to annotation bias

What about gene calling?• In genomics, normally you would predict the start and stop

positions of genes using a gene prediction program before annotating the genes

• In metagenomics:– Pros:

• May result in less false positives from annotating “non-real” genes• Lowers the number of similarity searches

– Cons• Computationally intensive • No good learning dataset• Raw reads will not cover an entire gene• Often requires assembled data

– Possible tools: FragGeneScan, MetaGeneAnnotator– Alternative: Do 6 frame-translation (e.g. BLASTX)

Community Function Potential

• Important that this is metagenomics, not metatranscriptomics, and not metaproteomics

• These annotations suggest the functional potential of the community

• The presence of these genes/functions does not mean that they are biologically active (e.g. may not be transcribed)

PICRUSTPredicting function from 16S profiles

Sample 1 Sample 2 Sample 3

OTU 1 4 0 2

OTU 2 1 0 0

OTU 3 2 4 2

16S rRNA gene

QIIME

Shotgun Metagenomics

HUMAnN

Sample 1 Sample 2 Sample 3

K00001 20 15 18

K00002 1 2 0

K00003 4 5 4

MetaPhlAn

PICRUSt

STAMPSTAMP

41

• PICRUSt

• Phylogenetic Investigation of Communities by Reconstruction of Unobserved States

• http://picrust.github.com

PICRUSt: How does it work?

Predicting the abundance of a single function

Known gene abundanceAncestral gene abundancePredicted gene abundance

Predicting the abundance of a single function

Known gene abundanceAncestral gene abundancePredicted gene abundance

Repeat for each function (~8000X)

Repeat for all unknown tips (>100,000)

PICRUSt: Predicting Metagenomes

S1 S2 S3

12345 10 0 567890 1 0 066666 4 8 2

16S Copy Number

12345 567890 166666 2

S1 S2 S3

12345 2 0 167890 1 0 066666 2 4 1

Normalized OTU TablePICRUST 16S PredictionsOTU Table

PICRUSt: Predicting Metagenomes

S1 S2 S3

12345 10 0 567890 1 0 066666 4 8 2

16S Copy Number

12345 567890 166666 2

K0001 K0002 K0003

12345 4 0 267890 1 0 066666 2 4 2

S1 S2 S3

12345 2 0 167890 1 0 066666 2 4 1

S1 S2 S3

12345 2 0 167890 1 0 066666 2 4 1

S1 S2 S3

K0001 13 8 6K0002 8 16 4K0003 8 8 4

Normalized OTU Table

Metagenome Prediction

PICRUST 16S Predictions

PICRUST KEGG Predictions

OTU Table

• PICRUSt predictions across body sites

47Langille et al., 2013, Nature Biotechnology

48

49

50

VISUALIZATION AND STATISTICSWhat is important?

Visualization and Statistics

• Various tools are available to determine statistically significant taxonomic differences across groups of samples– Excel– SigmaPlot– Past– R (many libraries)– Python (matplotlib)– STAMP

STAMP

STAMP Plots

STAMP• Input

1. “Profile file”: Table of features (samples by OTUs, samples by functions, etc.)• Features can form a heirarchy (e.g. Phylum, Order, Class, etc) to allow

data to be collapsed within the program

2. “Group file”: Contains different metadata for grouping samples• Can be two groups: (e.g. Healthy vs Sick) or multiple groups (e.g. Water

depth at 2M, 4M, and 6M)

• Output– PCA, heatmap, box, and bar plots– Tables of significantly different features

METAGENOMICS WORKFLOWPutting it all together

Microbiome Helper

• Standard Operating Procedures (SOPs)– 16S – Shotgun Metagenomics

• Scripts to wrap and integrate existing tools– Available as an Ubuntu Virtualbox

• Tutorials/Walkthroughs

• https://github.com/mlangill/microbiome_helper/wiki

IMR: Integrated Microbiome Resource

• Offers sequencing and bioinformatics for microbiome projects (http://cgeb-imr.ca)

QUESTIONS?

Tutorial