2014 nyu-bio-talk

SCALABLE APPROACHES
TO EXPLORING
MICROBIAL DIVERSITY
C. Titus Brown
ctb@msu.edu
Asst Professor, MMG / CSE; Michigan State University
1/15: Population Health & Reproduction, VetMed, UC Davis
Talk slides on slideshare.net/c.titus.brown

The central question of my lab --
How can we most effectively use computation to extract
information from large sequence data sets, for the purpose
of better understanding non- and semi-model organisms?
Focus on environmental microbes, marine animals,
& agricultural and veterinary animals.

Biology is becoming data rich – and a
rising tide lifts all boats!
http://susieinfrance.blogspot.com/2010/06/rising-tide-lifts-all-boats.html

…but sometimes the tide comes in a bit
fast.

Our foil for today:
Investigating soil microbial communities
Life on earth depends on soil microbes, but:
• 95% or more of soil microbes cannot be cultured in lab.
• Very little transport in soil and sediment =>
slow mixing rates.
• Estimates of immense diversity:
• Billions of microbial cells per gram of soil.
• Million+ microbial species per gram of soil (Gans et al, 2005)
• One observed lower bound for genomic sequence complexity =>
26 Gbp (Amazon Rain Forest Microbial Observatory)

“By 'soil' we understand (Vil'yams, 1931) a loose surface
layer of earth capable of yielding plant crops. In the physical
N. A. Krasil'nikov, SOIL MICROORGANISMS AND HIGHER PLANTS
http://www.soilandhealth.org/01aglibrary/010112krasil/010112krasil.ptII.h
tml
sense the soil represents a complex disperse system
consisting of three phases: solid, liquid, and gaseous.”
Microbes live in & on:
• Surfaces of
aggregate particles;
• Pores within
microaggregates;

Specific questions to address:
• Role of soil microbes in nutrient cycling?
• How does agricultural soil differ from native soil?
• How do soil microbial communities respond to climate
perturbation?
• Genome-level questions:
• What kind of strain-level heterogeneity is present in the population?
• What are the phage and viral populations & dynamics thereof?
• What species are where, and how much is shared between
different geographical locations?

Must use culture independent and
metagenomic approaches
• Many reasons why you can’t or don’t want to culture:
Cross-feeding, niche specificity, dormancy, etc.
• If you want to get at underlying function, 16s analysis
alone is not sufficient.
Single-cell sequencing & shotgun metagenomics are two
common ways to investigate complex microbial communities.

Shotgun metagenomics
• Collect samples;
• Extract DNA;
• Feed into sequencer;
• Computationally analyze.
“Sequence it all and let the
bioinformaticians sort it
Wikipedia: Environmental shotgun
sequencing.png
out”

Computational reconstruction of
(meta)genomic content.
http://eofdreams.com/library.html;
http://www.theshreddingservices.com/2011/11/paper-shredding-services-small-business/;
http://schoolworkhelper.net/charles-dickens%E2%80%99-tale-of-two-cities-summary-analysis/

Points:
• Lots of fragments needed! (Deep sampling.)
• Having read and understood some books will help quite a bit
(Reference genomes.)
• Rare books will be harder to reconstruct than common books.
• Errors in OCR process matter quite a bit. (Sequencing error)
• The more, different specialized libraries you sample, the more
likely you are to discover valid correlations between topics and
books. (We don’t understand most microbial function.)
• A categorization system would be an invaluable but not
infallible guide to book topics. (Phylogeny can guide
interpretation.)
• Understanding the language would help you validate &
understand the books.

Great Prairie Grand Challenge -
-SAMPLING LOCATIONS
2008

A “Grand Challenge” dataset (DOE/JGI)
600
500
400
300
200
100
0
Iowa,
Continuous
corn
Iowa, Native
Prairie
Kansas,
Cultivated
corn
Kansas,
Native
Prairie
MetaHIT (Qin et. al, 2011), 578 Gbp
Wisconsin,
Continuous
corn
Wisconsin,
Native
Prairie
Wisconsin,
Restored
Prairie
Wisconsin,
Switchgrass
Basepairs of Sequencing (Gbp)
GAII HiSeq
Rumen (Hess et. al, 2011), 268 Gbp
NCBI nr database,
37 Gbp
Total: 1,846 Gbp soil metagenome
Rumen K-mer Filtered,
111 Gbp

My algorithm research: 3 methods.
1. Adaptation of a suite of probabilistic data structures for
representing set membership and counting (Bloom filters
and CountMin Sketch). (Zhang et al., PLoS One, 2014.)
2. An online streaming approach to lossy compression of
sequencing data. (Brown et al., arXiv, 2012; Howe et al., PNAS, 2014.)
3. Compressible de Bruijn graph representation for
assembly. (Pell et al., PNAS, 2012.)

Method #2 - Digital normalization
(a computational version of library normalization)
Suppose you have a
dilution factor of A (10) to
B(1). To get 10x of B you
need to get 100x of A!
Overkill!!
This 100x will consume
disk space and, because
of errors, memory.
We can discard it for
you…

Assembling Iowa prairie and Iowa corn:
Total
Assembly
Total Contigs
(> 300 bp)
% Reads
Assembled
Putting it in perspective:
Total equivalent of ~1200 bacterial genomes
Human genome ~3 billion bp
Predicted
protein
coding
2.5 bill 4.5 mill 19% 5.3 mill
Adina Howe

Resulting contigs are all low coverage.
Howe et al., 2014
Figure11: Coverage (median basepair) dist ribut ion of assembled cont igs from soil metagenomes.

Iowa prairie & corn DNA abundances are
very even.
Corn Prairie
Howe et al., 2014

Assembly is a good idea:
Howe et al., 2014

Analyses of
metabolic potential
begin to illuminate
differences.
Howe et al., 2014

We see little strain variation in sample.
Top two allele frequencies
Position within contig
Can measure
by read
mapping.
Of 5000 most
abundant
contigs, only 1
has a
polymorphism
rate > 5%

Biogeography: Iowa sample overlap?
Corn and prairie content graphs have 51% nucleotide
overlap.
Corn Prairie
Suggests that at greater depth, samples may have similar
genomic content.

Biogeography of genomic DNA in soil
How much genomic richness is shared
between different sites?
Qingpeng Zhang

So, for soil:
• We really do need more data;
• But at least now we can assemble what we already have.
• Estimate required sequencing depth at 50 Tbp;
• Now also have 2-8 Tbp from Amazon Rain Forest
Microbial Observatory.
• …still not saturated coverage, but getting closer.
Iowa soil work has been published:
Howe et al., 2014, PNAS.

So, for soil:
Note! There are now much faster assembly approaches…!
See: Megahit, http://arxiv.org/abs/1409.7208
(Technology marches on!)

So, for soil:
• We really do need more data;
• But at least now we can assemble what we already have.
• Estimate required sequencing depth at 50 Tbp;
• Now also have 2-8 Tbp from Amazon Rain Forest
Microbial Observatory.
• …still not saturated coverage, but getting closer.
But, diginorm approach turns out to also be widely
useful.

Digital normalization is popular…
Estimated ~1000 users of our software.
Diginorm algorithm now included in Trinity
software from Broad Institute (~10,000 users)
Illumina TruSeq long-read technology now
incorporates our approach (~100,000 users)

The data problem: Looking forward 5
years…
Navin et al., 2011

Some basic math:
• 1000 single cells from a tumor…
• …sequenced to 40x haploid coverage with Illumina…
• …yields 120 Gbp each cell…
• …or 120 Tbp of data.
• HiSeq X10 can do the sequencing in ~3 weeks.
• The variant calling will require 2,000 CPU weeks…
• …so, given ~2,000 computers, can do this all in one
month.

Similar math applies:
• Pathogen detection in blood;
• Environmental sequencing;
• Sequencing rare DNA from circulating blood.
• Two issues:
•Volume of data & compute
infrastructure;
• Latency for clinical applications.

We face an infinite data problem.
• For all intents and purposes
• For example, Illumina estimates that 228,000 human
genomes will be resequenced this year, primarily by
researchers; this is only going to grow.
• Similar stories across all of biology (although #s lower :)

Current analysis approaches are multipass,
e.g. variant calling:
Data
Mapping
Sorting
Calling Answer
On infinite data, you really only want to look at the data once…

Streaming algorithms can be very efficient
Data
1-pass
Answer
See also eXpress, Roberts et al., 2013.

Some key points --
• Digital normalization is streaming.
• Digital normalizing is computationally efficient (lower
memory than other approaches; parallelizable/multicore;
single-pass)
• Currently, primarily used for prefiltering for assembly, but
relies on underlying abstraction (De Bruijn graph) that is
also used in variant calling.

Error correction as the solution for our ills
Current work: error correction (??)
Errors in sequencing data are at the root of many
problems:
• Assembly is 100x lower memory in the absence of errors.
• Mapping is computationally trivial when there are no
errors.
• Variant calling and genotyping become simple, as does
species detection.

We can error correct high-coverage shotgun data
with k-mer spectra:
Chaisson et al., 2009
True k-mers
Erroneous k-mers

Streaming error correction on E. coli data
(Early days…)
TP FP TN FN
1% error rate, 100x coverage.
Michael Crusoe, Jordan Fish, Jason Pell
Error
correction 3,494,631 3,865 460,601,171 5,533
(corrected) (mistakes) (OK) (missed)

Error correction  variant calling
Single pass, reference free, tunable, streaming
online variant calling.

Streaming with reads…
Sequence...
Graph
Sequence...
Sequence...
Sequence...
Sequence...
Sequence...
Sequence...
Sequence...
....
Variants

Analysis is done after sequencing.
Sequencing Analysis

Streaming with bases
k bases...
Graph
k+1
k bases... k+1
k+2
k bases... k+1
k bases... k+1
k bases... k+1
...
k bases... k+1
Variants

Integrate sequencing and analysis
Sequencing
Analysis
Are we done yet?

What does the future hold?
• More emphasis on training and infrastructure.
• Data integration!
• Identifying the function of unknown genes…

Summer NGS workshop (2010-2017)

The infrastructure challenge
In 5-10 years, we will have nigh-infinite data.
(Genomic, transcriptomic, proteomic, metabolomic,
…?)
We currently have no good way of querying,
exploring, investigating, or mining these data sets,
especially across multiple locations..

Distributed graph database server
Web interface + API
Compute server
(Galaxy?
Arvados?)
Data/
Info
Raw data sets
Public
servers
"Walled
garden"
server
Private
server
Graph query layer
Upload/submit
(NCBI, KBase)
Import
(MG-RAST,
SRA, EBI)

Data integration?
Once you have all the data, what do you do?
"Business as usual simply cannot work."
Looking at millions to billions of genomes.
(David Haussler, 2014)

My charge: We don’t know what most genes do.
Total
Assembly
Total Contigs
(> 300 bp)
% Reads
Assembled
Putting it in perspective:
Total equivalent of ~1200 bacterial genomes
Human genome ~3 billion bp
Predicted
protein
coding
Howe et al, 2014; pmid 24632729

Data Intensive Biology
Opportunities & challenges; how can we best support the
biology?
"I have traveled the length and breadth of this
country and talked with the best people, and I can
assure you that data processing is a fad that won't
last out the year." --The editor in charge of business
books for Prentice Hall, 1957

Thanks!
Key points:
• Facing nigh-infinite data situation;
• The first stages of sequence analysis, assembly and variant
calling, are computationally intensive (but we’re hoping to fix
that);
• Training in data intensive biology is critical to the future of
biology.
• Data sharing and data integration infrastructure is also critical.

Graph alignment can detect read saturation

Proposal: distributed graph database server
Web interface + API
Compute server
(Galaxy?
Arvados?)
Data/
Info
Raw data sets
Public
servers
"Walled
garden"
server
Private
server
Graph query layer
Upload/submit
(NCBI, KBase)
Import
(MG-RAST,
SRA, EBI)

Graph queries
across public & walled-garden data sets:
assembled
sequence
SIMILARITY TO ALSO CONTAINS
nitrite
reductase
ppaZ
raw
sequence
See Lee,
Alekseyenko, Brown,
paper in SciPy 2009:
the “pygr” project.

2014 nyu-bio-talk

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