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RIDING THE BIG DATA

TIDAL WAVE OF MODERN

MICROBIOLOGY

Adina Howe

Argonne National Laboratory / Michigan State University

Iowa State University, Ag & Biosystems Engr (January)

Understanding community

dynamics

Who is there?

What are they doing?

How are they doing it?

Understanding community

dynamics

Who is there?

What are they doing?

How are they doing it?

Kim Lewis, 2010

Gene / Genome Sequencing

Collect samples

Extract DNA

Sequence DNA

“Analyze” DNA to identify its content and origin

Taxonomy

(e.g., pathogenic E. Coli)

Function

(e.g., degrades cellulose)

Effects of low cost

sequencing…

First free-living bacterium sequenced

for billions of dollars and years of

analysis

Personal genome can be

mapped in a few days and

hundreds to few thousand

dollars

The experimental continuum

Single Isolate

Pure Culture

Enrichment

Mixed CulturesNatural systems

The era of big data in biology

Stein, Genome Biology, 2010

Computational Hardware

(doubling time 14 months)

Sanger Sequencing


NGS (Shotgun) Sequencing


1990 1992 1994 1996 1998 2000 2003 2004 2006 2008 2010 2012

Year

0

1

10

100

1,000

10,000

100,000

1,000,000

Dis

k S

tora

ge,

Mb/$

0.1

1

10

100

1,000

10,000

100,000

1,000,000

DN

A S

equencin

g, M

bp

per $

10,000,000

100,000,000

0.1

1

10

100

1,000

10,000

100,000

1,000,000

10,000,000

100,000,000

Postdoc experience with data

2003-2008 Cumulative sequencing in PhD = 2000 bp

2008-2009 Postdoc Year 1 = 50 Gbp

2009-2010 Postdoc Year 2 = 450 Gbp

2014 = 50 Tbp

2015 = 500 Tbp budgeted

“Soil Census” to “Soil Catalogs”: Who is there?

Targetting conserved regions

of known genes

Most popular:

16S ribosomal RNA gene –

conserved in bacteria and

archaea

Who is there - community

profiling based on sequence

similarity

Must have previous

knowledge of genes

Must infer function based on

phylogeny – not advised

TARGETTED SEQUENCING

STRATEGY

“Soil Census” to “Soil Catalogs”: Who is there?

Targetting conserved regions

of known genes

Most popular:

16S ribosomal RNA gene –

conserved in bacteria and

archaea

Who is there - community

profiling based on sequence

similarity

Must have previous

knowledge of genes

Must infer function based on

phylogeny – not advised

$15 / sample

TARGETTED SEQUENCING

STRATEGY

Tackling Soil Biodiversity

Source: Chuck Haney

C. Titus Brown, James Tiedje, Qingpeng Zhang, Jason Pell (MSU)

Janet Jansson, Susannah Tringe (JGI)

THE DIRT ON SOIL

Biodiversity in the dark, Wall et al., Nature Geoscience, 2010 Jeremy Burgress

MAGNIFICENT BIODIVERSITY

THE DIRT ON SOIL

SPATIAL HETEROGENEITY

http://www.fao.org/ www.cnr.uidaho.edu

THE DIRT ON SOIL

DYNAMIC

THE DIRT ON SOIL

INTERACTIONS: BIOTIC, ABIOTIC, ABOVE, BELOW, SCALES

Philippot, 2013, Nature Reviews Microbiology

Our shared challenges

Climate Change

Energy Supply

USGCRP 2009

www.alutiiq.com

http://guardianlv.com/

Human Health

An understanding

of microbial ecology

SOIL MICROBIOLOGY: CARBON

REGULATION

The anthropogenic CO2 production is only 10% of that of the soil

Sustainable agriculture permits carbon

sequestration in the range of 0.3 – 1 ton of

C/ha.yr ~ 10% of all carbon emitted by cars

(Denman et al., 2007; Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change)


Source: Chuck Haney



Lesson #1: Accessing information in

data

http://siliconangle.com/files/2010/09/image_thumb69.png

de novo assembly

Compresses dataset size significantly

Improved data quality (longer sequences, gene order)

Reference not necessary (novelty)

Raw sequencing data (“reads”) Computational algorithms Informative genes / genomes

Metagenome assembly…a scaling

problem.

Shotgun sequencing and de novo

assembly

It was the Gest of times, it was the wor

, it was the worst of timZs, it was the

isdom, it was the age of foolisXness

, it was the worVt of times, it was the

mes, it was Ahe age of wisdom, it was th

It was the best of times, it Gas the wor

mes, it was the age of witdom, it was th

isdom, it was tIe age of foolishness

It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness

Practical Challenges – Intensive

computing

Howe et al, 2014, PNAS

Months of

“computer

crunching” on a

super computer

Practical Challenges – Intensive

computing


Months of

“computer

crunching” on a

super computerAssembly of 300 Gbp can be

done with any assembly program

in less than 14 GB RAM and less

than 24 hours.

Natural community characteristics

Diverse

Many organisms

(genomes)


Diverse

Many organisms

(genomes)

Variable abundance

Most abundant organisms, sampled

more often

Assembly requires a minimum amount

of sampling

More sequencing, more errors

Sample 1x


Diverse

Many organisms

(genomes)

Variable abundance


more often


of sampling


Sample 1x Sample 10x


Diverse

Many organisms

(genomes)

Variable abundance


more often


of sampling


Sample 1x Sample 10x

Overkill

Digital normalization

Brown et al., 2012, arXiv

Howe et al., 2014, PNAS

Zhang et al., 2014, PLOS One

Digital normalization

Brown et al., 2012, arXiv

Howe et al., 2014, PNAS

Zhang et al., 2014, PLOS One

Scales datasets for assembly up to 95% - same assembly

outputs.

Genomes, mRNA-seq, metagenomes (soils, gut, water)


Source: Chuck Haney



The reality?

More like…

Source: Chuck HaneyHowe et. al, 2014, PNAS

SOIL METAGENOME REALITY CHECK

Grand Challenge effort –

10% of soil biodiversity

sampled

Incredible soil biodiversity

(estimate required 10

Tbp/sample)

“To boldly go where no man

has gone before”: >60%

Unknown

0

100

200

300

400

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ino a

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meta

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carb

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rt

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y

Tota

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ount

KO

corn and prairie

corn only

prairie only


Managed agriculture soils exhibit less

diversity, likely from its history of

cultivation.

Frustrating, but helpful

“Low input, high throughput, no output?” (Sean Eddy / Sydney Brenner)

Evaluation of sequencing as a tool

Broad characterization

“Right” kind of data

How much should I sequence?

Data characteristics

Breadth vs. depth of sampling

Computational tool development

Tr

Lesson #2: Connecting the dots

from data to information

If 80% is

unknown…what

can one do?

The idea of co-occurrence

Co-occurrence to detect novelty

Williams et al., 2014, Frontiers of Micro

Causation vs correlation

Wikipedia.com

Success story in Human

Microbiome

#3 Is more data better?

Bottlenecks for the emerging

microbiologists

Technical challenges – many

solutions

Access to data and its value

Access to resources

Data volume and velocity “clog”

Data is very heterogeneous

Software Developers

Computer Scientists

Clinicians

PIs

Data generators

Microbiologists

Data Analyzers

Statisticians

Bioinformaticians

http://ivory.idyll.org/blog/2014-the-emerging-field-of-data-intensive-biology.html

Data intensive microbiology

Social obstacles – the main

challenge

Shift of costs do not mean shift of

expectations

http://www.deluxebattery.com/25-hilarious-expectation-vs-reality-photos/

Dear PI,

It will take longer than

the time it took you to do

your experiment to

analyze the data. Please

do not write me for

results within 24 hours of

your sequences

becoming available.

- Adina

Culture of sharing

http://www.heathershumaker.com/

Metagenomic Datasets

Training / Incentives

Emails between collaborators don’t contain as

much “science” as I’d like:

All analysis: accessible,

reproducible, and automated

RIDING THE BIG DATA


MICROBIOLOGY

Adina Howe



“”

RIDING THE BIG DATA


MICROBIOLOGY

Adina Howe



Acknowledgements

C. Titus Brown (MSU)

James Tiedje (MSU)

Daina Ringus (UC)

Folker Meyer (ANL)

Eugene Chang (UC)

NSF Biology Postdoc Fellowship

DOE Great Lakes Bioenergy Research Center

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