2014 bangkok-talk

NON-INTENSIVE BIOLOGY:
OPPORTUNITIES AND
CHALLENGES OF NEXT-GEN
SEQUENCING
C. Titus Brown
Assistant Professor
MMG / CSE

Lansing, Michigan -> Davis, California

We practice open science!
Everything discussed here:
• Code: github.com/ged-lab/ ; BSD license
• Blog: http://ivory.idyll.org/blog (‘titus brown blog’)
• Twitter: @ctitusbrown
• Grants on Lab Web site: http://ged.msu.edu/research.html
• Papers available as preprints.
• All my talks are available at slideshare.net/c.titus.brown/

Sequencing!
• Sequencing of DNA and RNA.
• Single genomes
• Transcriptomes
• Natural populations (tags)
• Environmental samples/microbial populations (metagenomics)
• Cheap and massively scalable sequencing of DNA and
RNA.

Sequencing technology
• Major, dramatic changes in our ability to sequence DNA
and RNA quickly and cheaply.
• Majority of deployed techniques depend on (variations of)
a single trick: “polony” sequencing. No cloning.
• Single-molecule sequencing coming along fast, but not
yet ready for prime time.

Two specific concepts:
• First, sequencing everything at random is very much
easier than sequencing a specific gene region. (For
example, it will soon be easier and cheaper to shotgun-sequence
all of E. coli then it is to get a single good
plasmid sequence.)
• Second, if you are sequencing on a 2-D substrate (wells,
or surfaces, or whatnot) then any increase in density
(smaller wells, or better imaging) leads to a squared
increase in the number of sequences.

Some numbers
• For under $1,000 per sample, the Illumina HiSeq machine
will generate:
• 200,000,000 reads
• Each of length ~150
• In under a week.
• x 16 samples/run.
• That’s almost 500 Gbp of sequence, or just over 160x
human genome…

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

The challenges of non-model sequencing
• Missing or low quality genome reference.
• Evolutionarily distant.
• Most extant computational tools focus on model
organisms –
• Assume low polymorphism (internal variation)
• Assume reference genome
• Assume somewhat reliable functional annotation
• More significant compute infrastructure
…and cannot easily or directly be used on critters of interest.

Shotgun sequencing analysis goals:
• Assembly (what is the text?)
• Produces new genomes & transcriptomes.
• Gene discovery for enzymes, drug targets, etc.
• Counting (how many copies of each book?)
• Measure gene expression levels, protein-DNA
interactions
• Variant calling (how does each edition vary?)
• Discover genetic variation: genotyping, linkage
studies…
• Allele-specific expression analysis.

Shotgun sequencing & assembly
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/

Assembly
It was the best of times, it was the wor
, it was the worst of times, it was the
isdom, it was the age of foolishness
mes, it was the age of wisdom, it was th
It was the best of times, it was the worst of times, it was the
age of wisdom, it was the age of foolishness
…but for lots and lots of fragments!

Mapping: locate reads in reference
http://en.wikipedia.org/wiki/File:Mapping_Reads.png

Variant detection after mapping
http://www.kenkraaijeveld.nl/genomics/bioinformatics/

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.

The Data Deluge
(a traditional requirement for these talks)

Lab approach: Lossy compression
(Reduce volume of data & compute infrastructure
requirements)
Raw data
(~10-100 GB) Analysis
"Information"
~1 GB
"Information"
"Information"
"Information"
"Information"
Database &
integration
Compression
(~2 GB)
Lossy compression can substantially
reduce data size while retaining
information needed for later (re)analysis.

http://en.wikipedia.org/wiki/JPEG
Lossy compression

Outline
• The Molgulid story: investigating non-model
ascidians ( this is the biology)
• Meditations on data analysis.
• Methods, methods, methods.
•Training, training, training.
• Concluding thoughts

The Molgula Story – an int’l collaboration
Elijah Lowe
(MSU; Naples?)
Billie Swalla (UW, BEACON)
Lionel Christiaen (NYU);
Claudia Racioppi (Naples; NYU)

…to the urochordateswe go!
Putnam et al., 2008,
Modified from SwaNllaa t2u0r0e1.

Filter feeding adults
Molgula oculata
Molgula occulta
Molgula oculata Ciona intestinalis
Elijah Lowe; collaboration w/Billie Swalla

Challenging organisms to work on!
Molgula occulta & M. oculata:
• Only spawn ~1 month out of the year
• Located off the northern coast of France
• Hybrids not found outside of lab conditions
• Species cannot be cultured
•Wet lab techniques are not fully developed for
species
• No genomic resources (as of 2008).

Billie Swalla, Nadine Peyriéras, Alberto Stolfi

Tail loss and notochord
a) M. oculata b) hybrid (occulta egg x oculata sperm) c) M. occulta
Notochord cells in orange Swalla, B. et al. Science, Vol 274, Issue 5290, 1205-1208 , 15 November 1996

Molgula clades – tail loss is derived

Solitary ascidians
have determinant
and invariant cleavage.
Some species have
colored cytoplasms.
(Boltenia villosa)
The cell lineage is very
similar in Ciona, Phallusia,
Halocynthia roretzi &
Molgula oculata.

Molgula occidentalis
Ciona intestinalis

Notochord formation (convergence &
extension) in ascidians is highly
conserved.
Ciona savignyi Jiang and Smith, 2007

Notochord Formation
in Molgulids
Molgula oculata notochord
(40 cells, converged & extended)
Molgula occulta no notochord
(20 cells, not converged & extended)
Hybrid notochord
(20 cells, converged & extended)
Swalla and Jeffery, 1996

First we applied mRNAseq…
Lowe et al., in review (PeerJ). https://peerj.com/preprints/505/

…which gave us entire transcriptomes…
Lowe et al., in review (PeerJ). https://peerj.com/preprints/505/

…then we sequenced their genomes...
• 3 species:
Molgula occidentalis (tailed) – “MOXI”
Molgula oculata (tailed) – “MOCU”
Molgula occulta (tail-less) – “MOCC”
• 3 lanes: 300-400 bp; 650-750 bp; 900-1000 bp
• ≥ 200X coverage each genome
De novo assembly by Elijah Lowe (MSU)
Stolfi et al., eLife, 2014; http://dx.doi.org/10.7554/eLife.03728

…which gave us most of their genes (and
regulatory elements?)
Genome assembly statistics:

Shift in differentially expressed genes from
gastrulation to neurulation
M. ocu vs. M. occ gastrula M. ocu vs. M. occ neurula
Differentially expressed during neurulation in M. ocu vs M. occ
Elijah Lowe

Notochord gene expression similar to tailed
species
-10 -5 0 5 10 15
-10 -5 0 5 10 15
Expression difference Hybrid vs Parent species
log2(hybrid)-log2(oculata)
log2(hybrid)-log2(occulta)
Elijah Lowe

Heterochronic Shift in MolgulidaeDevelopment
*79 genes examined
across six species

Transgenics of reporter constructs
(“Mutual intelligibility” across ~350 my)

Prickle is a key part of the notochord program.
Veeman, M., et al., 2007
•Planar cell
polarity (PCP)
pathway
•Involved in
convergence and
extension

Prickle expressed in notochord cells of
tailless ascidians.
Mita et al Zool. Sci., 2010
M. occulta gastrulation
Ciona intestinalis
Satoh Nature Reviews Genetics 4, 2003
FGF Bra Pk
Elijah Lowe

(Re)booting the Molgula --
• Determined conservation of cardiopharyngeal
developmental program, despite shifts in cis-regulatory
sequences (Stolfi et al, eLife, 2014).
• Examining heterochronic shifts in developmental timing
(tail loss) (Maliska et al., in preparation).
• Connecting evolutionary shifts in developmental gene
regulatory networks with conserved molecular profiles
(Lowe et al, submitted; Lowe et al., in preparation).

More thoughts on Molgula
• One grad student, two transcriptomes, three genomes,
four years…
• Genomic resources are enabling a sprawling international
collaboration (UW/BEACON, MSU/BEACON, NYU,
Naples, Paris)
• ! Methods development key!

How Science Works
Data
Analysis
Data generation

Luckily, data analysis is cheap and easy!

Err, well, actually…
Data generation
Data Analysis
http://www.pixelpog.com/ftpimages/GnomesAttack.jpg

It is now easy to generate sequencing
data sets of such a size and scale that
the first round analysis cannot even be
completed.

My research:
theoretical => applied solutions to scale.
Theoretical advances
in data structures and
algorithms
Practically useful & usable
implementations, at scale.
Demonstrated
effectiveness on real data.

My research: three 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…

Digital normalization retains information, while
discarding data and errors

Digital normalization approach
A digital analog to cDNA library normalization, diginorm:
• Streaming & single pass: looks at each read at most
once;
• Does not “collect” the majority of errors;
• Keeps all low-coverage reads;
• Smooths out coverage of sequencing.
=>
Enables analyses that are otherwise completely
impossible.

Witness the power of this fully operational
set of sequence analysis methods:
1. Assembling soil metagenomes.
Howe et al., PNAS, 2014 (w/Tiedje)
2. Understanding bone-eating worm symbionts.
Goffredi et al., ISME, 2014.
3. An ultra-deep look at the lamprey transcriptome.
Scott et al., in preparation (w/Li)
4. Understanding development in Molgulid ascidians.
Stolfi et al, eLife 2014; etc.

Open science
Guiding principle: methods that aren’t broadly
available aren’t very useful.
(=> Preprints, open source code, blog posts, Twitter,
training, etc.)
Estimated ~1000 users of our software.
Diginorm now included in Trinity software from Broad
Institute (~10,000 users)
Illumina TruSeq long-read technology now
incorporates our approach (~100,000 users)

Current research:
Compressive algorithms for sequence
analysis
Raw data
(~10-100 GB) Analysis
"Information"
~1 GB
"Information"
"Information"
"Information"
"Information"
Database &
integration
Compression
(~2 GB)
Can we enable and accelerate sequence-based
inquiry by making all basic analysis
easier and some analyses possible?

The data challenge in biology
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..
Moreover, most data is unavailable until after
publication…
…which, in practice, means it will be lost.

Infrastructure: 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 Intensive Biology”
• Increasingly, relevant data is out there or can be
generated fairly inexpensively.
• But what does the data mean? How can we get it to yield
putative answers? How can we integrate it with other
people’s data?
• Virtually nobody in biology is trained to do this.
• Virtually nobody in biology is being trained in how to do
this.

Summer NGS workshop (2010-2017)

Perspectives on training
• Prediction: The single biggest
challenge facing biology over the
next 20 years is the lack of data
analysis training (see: NIH DIWG
report)
• Data analysis is not turning the
crank; it is an intellectual exercise
on par with experimental design or
paper writing.
• Training is systematically
undervalued in academia (!?)

Training - looking forward
• NIH “Big Data 2 Knowledge” (BD2K) will be investing
~$20-40m in training each year (my estimate).
Biomedical science increasingly depends on data
analysis.
• Moore, Sloan Foundations are investing heavily in training
(see: Software Carpentry)
• NSF BIO Centers have stated that “training is the second
most important problem that all of us have”.
We need to figure out solutions…

Students and postdocs
Former:
• Dr. Jason Pell (Google NYC)
• Asst Professor Adina Howe (Iowa State)
• Current:
• Dr. Likit Preeyanon (MMG)
• Elijah Lowe (CSE)
• Qingpeng Zhang (CSE)
• Jaron Guo (MMG)
• Camille Scott (CSE)
• Michael Crusoe
• Luiz Irber (CSE)
• Dr. Sherine Awad (MMG)

2014 bangkok-talk

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2014 bangkok-talk