2014 mmg-talk

NON-MODEL ORGANISMS AND DATA-INTENSIVE BIOLOGY

C. Titus Brown

Assistant Professor

MMG / CSE

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”.

My training efforts – looking backwards

• Approximately $600k of my funding has been received for developing and implementing training.

• “Students” have included about a dozen associate & full professors; over 120 alumni of summer course in total.

• Invited talks, collaborations, problem discovery, networking, interaction with program managers, and volleyball.

• Strong pushback from every level of the administration at MSU!? But enthusiastic support from many research-active faculty.

(Invest in data science should be part of MMG’s vision for the future…)

About those STEM career paths…

Quote:

“…foisting graduates upon a carcass-strewn jobless dystopia.”

Dr. Rebecca Schuman, https://chroniclevitae.com/news/702-crimes-against-dissertation-humanity

Want a faculty job?

http://www.ascb.org/ascbpost/index.php/compass-points/item/285-where-will-a-biology-phd-take-you

Want a faculty job? Don’t count on it.

< 10% of entering PhD students will become tenure track faculty.*

53% rank research professorships as their desired career.*

(Optimism is great! But…)

Note: universities have little provision for permanent non-tenure-track positions.

* http://www.ascb.org/ascbpost/index.php/compass-points/item/285-where-will-a-biology-phd-take-you

(Sorry. I thought you should all know.)

Alternatives to tenure track.

PhD research prepares you marvelously for tackling an immense range of problems!!

Biotech, startups, research institutes, teaching, science communication…

(PhD advisors generally do not do such a good job of preparing you for non-tenure track positions.)

Papers are necessary to graduate but insufficient to get you a non-academic job afterwards.

Wrapping it all up• There are great opportunities in our increasing ability to

generate data!

• Data analysis is rapidly becoming a first class citizen in biology.

• We aren’t training people in data analysis approaches.

• …this would help them find jobs, too.

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)

Support network

Dr. Vivien Bonazzi, my fairy NIH program officer.

Dr. Jim Tiedje, He Who Comes with Sequence

Support network

Co-conspirators / family

Thanks!

(1994)