A practical introduction to handling 16S data

Mads AlbertsenInternal workshop 2013

CENTER FOR MICROBIAL COMMUNITIES

• Introduction

• Case story: GAO Reactors

• Generating OTU tables (Hands on)

• Analyzing data in Excel (Hands on)

• Analyzing data in R (Hands on)

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY

Agenda

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY

Who - when, where and why?

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY

Who - when, where and why?

http://phil.cdc.gov/phil/details.asp?pid=2226http://en.wikipedia.org/wiki/File:EBPR_FISH_Floc.jpg P. Larsen 2012

Accumulibacter Competibacter Bacillus anthracis

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY

The affinities of all the beings of the same class have sometimes been represented by a great tree... The green and budding twigs may represent existing species; and those produced during former years may represent the long succession of extinct species.

C. Darwin, 1872

http://tolweb.org

Nothing in biology makes sense, except in the light of evolution.

T. Dobzhansky, 1973

Taking advantage of evolution

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY

Why do we use the 16S gene?

Ribosomes are universal

rRNA = Structural RNAhttp://www.rna.icmb.utexas.edu/SAE/2B/ConsStruc/Diagrams/cons.16.b.Bacteria.pdf

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY

Why do we use the 16S gene?

http://www.rna.icmb.utexas.edu/SAE/2B/ConsStruc/Diagrams/cons.16.b.Bacteria.pdf

8F8F Universal primer

8F

8F

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY

Why do we use the 16S gene?

http://www.rna.icmb.utexas.edu/SAE/2B/ConsStruc/Diagrams/cons.16.b.Bacteria.pdf

Ashelford et al. AEM. 2005;71:7724-7736

• Advantages:• Universal gene (No horizontal gene transfer)• Conserved regions• Variable regions• Great databases and alignments

• Problems:• Variable copy number• No universal (unbiased) primers• (Not directly correlated with activity)• (Lack of functional information)

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY

Typical workflow

Sampling SequencingExtraction Sample prep Bioinformatics

The focus of the workshop is bioinformatics. However, the preceding steps influences how we handle the data.

Sampling SequencingExtraction Sample prep Bioinformatics

• Standardisation, standardization, standardizasion..!

• Use biological replicates and evaluate your variation…!

• Design a good experiment with realistic expectations to the outcome (Most studies fail here!!!)

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY

Typical workflow

AAU activated sludge standard @ midasfieldguide.org

Sampling SequencingExtraction Sample prep Bioinformatics

eDNA removal

Input (mg)

Bead beating

Storage

Intensity (ms-1)D

ura

tio

n (

s)4 6

400160

804020

1 2 4 9 22• Fresh• 24 h @ 4°C• 24 h @ 20 °C

PMA650 W 10 min

+ N+ CH3

NH2

N3

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY

Typical workflow

AAU activated sludge standard @ midasfieldguide.org

Sampling SequencingExtraction Sample prep Bioinformatics

Bp

Mea

n f

req

uen

cy o

f m

ost

co

mm

on

res

idu

e in

50

bp

win

do

w

0 500 1000 1500

1.0

0.8

0.6 V1V2 V3

V4 V5V6

V7V8

V9

V1.3 V4V3.4

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY

Typical workflow

AAU activated sludge standard @ midasfieldguide.org

Ashelford et al. AEM. 2005;71:7724-7736

Sampling SequencingExtraction Sample prep Bioinformatics

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY

Typical workflow

PCR with modified 16S primers

5’-AATGATACGGCGACCACCGAGATCTACAC GTACGTACG GT AGAGTTTGATCCTGGCTCAG-3’

5’-CAAGCAGAAGACGGCATACGAGAT TCCCTTGTCTCC ACGTACGTAC CCG ATTACCGCGGCTGCTGG-3’

Illumina adapter Barcode Pad linker 534R

Illumina adapter Pad linker 27F

////Target region

//

1.

2.

3.

AAU activated sludge standard @ midasfieldguide.org

PCR Cycle

Sampling SequencingExtraction Sample prep Bioinformatics

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY

Typical workflow

Mardis, 2008 (PMID 18576944)

≈ 500 bp target amplicon

Sampling SequencingExtraction Sample prep Bioinformatics

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY

Typical workflow

Read 1: 300 bp

Read 2: 300 bp

Read 1Read 2Barcode

≈ 500 bp target amplicon

After Sequencing:

Sampling SequencingExtraction Sample prep Bioinformatics

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY

Typical workflow

How many sequences are needed? It depends on your question!

(although 50.000 raw sequences per sample is usually fine)

AAU raw kit and chemical costs (DKK) Cost Cost v2

DNA extraction 105 70a

Library preparation 40 40

Sequencing (min 100k reads / sample) 190b 70c

Total 335 180a Kits discountedb 50 samples per runc 150 samples per run (can run up to 300)

Sampling SequencingExtraction Sample prep Bioinformatics

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY

Typical workflow

Merge Cluster

31131

OTU Count

Assign taxonomy (Compare to database)

3 Accumulibacter11 Unkown

3 Competibacter1 Bacillus anthracis

OTU Count OTU table

Sampling SequencingExtraction Sample prep Bioinformatics

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY

Typical workflow

Merge Cluster

2 1 3 83 01 0

OTU A B

Assign taxonomy (Compare to database)

AAAAAAAAABBBBBBBBB

Barcode

2 1 Accumulibacter3 8 Unkown3 0 Competibacter1 0 Bacillus anthracis

OTU A BOTU table

Sampling SequencingExtraction Sample prep Bioinformatics

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY

Typical workflow

Sequence errors, chimera’s and weird stuff..

The chance of a perfect read as function of the read length

Chimera’s

Sampling SequencingExtraction Sample prep Bioinformatics

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY

Typical workflow

Merge Cluster

3113

OTU Count

Assign taxonomy (Compare to database)

3 Accumulibacter11 Unkown

3 Competibacter

OTU Count OTU table

Removing unique sequences makes the subsequent steps 10-100x faster and removes

the majority of errors and chimera’s

Dependent on sequencing depth and sample complexity! Be careful!

Sampling SequencingExtraction Sample prep Bioinformatics

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY

AAU workflow

16SAMP-14516SAMP-14616SAMP-14716SAMP-14816SAMP-14916SAMP-150

16S.V13.workflow.sh

Find sample ID’s on Google drive

OTU table (+ R version)

Plain text file

2 1 Accumulibacter3 8 Unkown3 0 Competibacter

OTU A B

Sampling SequencingExtraction Sample prep Bioinformatics

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY

AAU workflow

What 16S.V13.workflow.sh does:1. Find and unpack your samples2. Optional subsampling3. Remove potential phiX contamination (bowtie2)4. Merge read 1 and read 2 (flash)5. Remove reads outside length criteria6. Optional removal of unique reads and subsampling to even depth7. Format reads for QIIME8. Cluster reads to OTUs (Uclust, QIIME)9. Assign taxonomy (RDP classifier, QIIME + database: MiDAS, Greengnes or Silva)10. Generate OTU table (QIIME)