2. Outline
• The Molgulid story: investigating non-model
ascidians ( this is the biology)
• Meditations on data analysis.
• Methods, methods, methods.
•Training, training, training.
• Concluding thoughts
3. The Molgula Story – an int’l collaboration
Elijah Lowe
(MSU; Naples?)
Billie Swalla (UW, BEACON)
Lionel Christiaen (NYU);
Claudia Racioppi (Naples; NYU)
6. 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).
8. 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
10. 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.
14. 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
15. First we applied mRNAseq…
Lowe et al., in review (PeerJ). https://peerj.com/preprints/505/
16. …which gave us entire transcriptomes…
Lowe et al., in review (PeerJ). https://peerj.com/preprints/505/
17. …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
18. …which gave us most of their genes (and
regulatory elements?)
Genome assembly statistics:
Stolfi et al., eLife, 2014; http://dx.doi.org/10.7554/eLife.03728
19. 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
20. 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
22. Transgenics of reporter constructs
(“Mutual intelligibility” across ~350 my)
Stolfi et al., eLife, 2014; http://dx.doi.org/10.7554/eLife.03728
23. Prickle is a key part of the notochord program.
Veeman, M., et al., 2007
•Planar cell
polarity (PCP)
pathway
•Involved in
convergence and
extension
24. 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
25. (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).
26. 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!
29. Err, well, actually…
Data generation
Data Analysis
http://www.pixelpog.com/ftpimages/GnomesAttack.jpg
30. It is now easy to generate sequencing
data sets of such a size and scale that
the first round analysis cannot even be
completed.
31. 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.
32. 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.)
33. 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…
41. 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.
42. 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.
43. 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)
44. 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?
45. 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.
46. 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)
47. “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.
49. 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 (!?)
50. 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”.
51. 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…)
52. 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
53. Want a faculty job?
http://www.ascb.org/ascbpost/index.php/compass-points/item/
285-where-will-a-biology-phd-take-you
54. 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
56. 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.
57. 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.
