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.

Novel genome sequencing

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.

Wikipedia: Environmental shotgun sequencing.png

“Sequence it all and let the bioinformaticians sort it

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/

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.

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

Lossy compression can substantially reduce data size while retaining

information needed for later (re)analysis.

(Reduce volume of data & compute infrastructure requirements)

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

Lossy compression

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

Lossy compression

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

Lossy compression

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

Lossy compression

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 urochordates we go!

Putnam et al., 2008, Nature.Modified from Swalla 2001

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 Peyrieras, Alberto Stolfi

Tail loss and notochord

a) M. oculata b) hybrid (occulta egg x oculata sperm) c) M. occultaNotochord 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.

Jiang and Smith, 2007Ciona savignyi

Molgula oculata notochord(40 cells, converged & extended)

Molgula occulta no notochord(20 cells, not converged & extended)

Hybrid notochord(20 cells, converged & extended)

Notochord Formation in Molgulids

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?)

Stolfi et al., eLife, 2014; http://dx.doi.org/10.7554/eLife.03728

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 speciesElijah Lowe

Heterochronic Shift in Molgulidae Development*79 genes examined across six species

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

Stolfi et al., eLife, 2014; http://dx.doi.org/10.7554/eLife.03728

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 gastrulationCiona intestinalis

Satoh Nature Reviews Genetics 4, 2003FGF

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

Luckily, data analysis is cheap and easy!

Err, well, actually…

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.

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

Digital normalization

Digital normalization

Digital normalization

Digital normalization

Digital normalization

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 scienceGuiding 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

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

“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…

Funding

Students and postdocsFormer:• 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)

Students and postdocsFormer:• 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)