My talk to the Biotechnology group at UC Davis in Jan 2016.

1. Biology, Big Data, Precision
Medicine, and Other
Buzzwords
C.Titus Brown
School ofVeterinary Medicine;
Genome Center & Data Science Initiative
1/15/16
#titusbuzz
Slides are on slideshare.

2. N.B.This talk is for the students!
(I heard they had to attend, and I couldn’t pass
up a guaranteed audience!)
Note: at end, I would like to take a question or two from grad students first!

3. My academic path
• Undergrad: math major
• Grad school: developmental biology/genomics
• Postdoc: developmental biology/genomics
• Asst Prof: genomics/bioinformatics
• Now: bioinformatics/data-intensive biology

4. My non-academic path:
• Open source programming.
• Two startups, one real one & one half-
academic thing.
• Some consulting on software engineering and
testing.

5. Outline
1. Research on how to deal with lots of data.
2. How biology, in particular, is unprepared.
3. My advice for the next generation of
researchers.

6. 1. My research!
Some background & then some information.

7. DNA sequencing rates continues
to grow.
Stephens et al., 2015 - 10.1371/journal.pbio.1002195

8. Oxford Nanopore sequencing
Slide viaTorsten Seeman

9. Nanopore technology
Slide viaTorsten Seeman

10. Scaling up --

11. Scaling up --

12. Slide viaTorsten Seeman

13. http://ebola.nextflu.org/

14. “Fighting EbolaWith a Palm-
Sized DNA Sequencer”
See: http://www.theatlantic.com/science/archive/2015/09/ebola-
sequencer-dna-minion/405466/

15. “DeepDOM” cruise: examination
of dissolved organic matter &
microbial metabolism vs physical
parameters – potential collab.
Via Elizabeth Kujawinski
Lots of data other than just sequencing!

16. Data integration between
different data types..
Figure 2. Summary of challenges associated with the data integration in the proposed project.
Figure via E. Kujawinski

17. => My research
Planning for ~infinite amounts of data, and
trying to do something effective with it.

18. Shotgun sequencing and coverage
“Coverage” is simply the average number of reads that overlap
each true base in genome.
Here, the coverage is ~10 – just draw a line straight down from the top
through all of the reads.

19. Random sampling => deep sampling needed
Typically 10-100x needed for robust recovery (30-300 Gbp for human)

20. Digital normalization

21. Digital normalization

22. Digital normalization

23. Digital normalization

24. Digital normalization

25. Digital normalization

26. Computational problem now scales with information
content rather than data set size.
Most samples can be reconstructed via de
novo assembly on commodity computers.

27. Digital normalization & horse
transcriptome
The computational demands for cufflinks
- Read binning (processing time)
- Construction of gene models (no of genes, no of splicing junctions, no of
reads per locus, sequencing errors, complexity of the locus like gene
overlap and multiple isoforms (processing time & Memory utilization)
Diginorm
- Significant reduction of binning time
- Relative increase of the resources
required for gene model construction
with merging more samples and tissues
- ? false recombinant isoforms
Tamer Mansour

28. Effect of digital normalization
** Should be very valuable for detection of ncRNA
Tamer Mansour

29. The khmer software package
• Demo implementation of research data structures &
algorithms;
• 10.5k lines of C++ code, 13.7k lines of Python code;
• khmer v2.0 has 87% statement coverage under test;
• ~3-4 developers, 50+ contributors, ~1000s of users (?)
The khmer software package, Crusoe et al., 2015. http://f1000research.com/articles/4-900/v1

30. khmer is developed as a true open
source package
• github.com/dib-lab/khmer;
• BSD license;
• Code review, two-person sign off on changes;
• Continuous integration (tests are run on each
change request);
Crusoe et al., 2015; doi: 10.12688/f1000research.6924.1

31. Literate graphing & interactive
exploration
Camille Scott

32. Research process
Generate new
results; encode
in Makefile
Summarize in
IPython
Notebook
Push to githubDiscuss, explore

33. This is standard process in lab --
Our papers now have:
• Source hosted on github;
• Data hosted there or onAWS;
• Long running data analysis =>
‘make’
• Graphing and data digestion =>
IPython Notebook (also in
github)
Zhang et al. doi: 10.1371/journal.pone.0101271

34. The buoy project - decentralized infrastructure
for bioinformatics.
Compute server
(Galaxy?
Arvados?)
Web interface + API
Data/
Info
Raw data sets
Public
servers
"Walled
garden"
server
Private
server
Graph query layer
Upload/submit
(NCBI, KBase)
Import
(MG-RAST,
SRA, EBI)
ivory.idyll.org/blog/2014-moore-ddd-award.html

35. The next questions --
(a) If you had all the data from all the things,
what could you do with it?
(b) If you could edit any genome you wanted, in
any way you wanted, what would you edit?

36. 2. Big Data, Biology, and how
we’re underprepared.
(Answers to previous qs: we are not that good
at using data to inform our models or our
experimental plans...)

37. My first 7 reasons --
1. Biology is very complicated.
2. We know very little about function in biology.
3. Very few people are trained in both data analysis and
biology.
4. Our publishing system is holding back the sharing of
knowledge.
5. We don’t share data.
6. We are too focused on hypothesis-driven research.
7. Most computational research is not reproducible.

38. Biology is complicated.
Sea urchin gene network for early development; http://sugp.caltech.edu/endomes/

39. We know very little, and a lot of
what we “know” is wrong.
One recent story that caught my eye – problems
with genetic testing & databases. (See URL below
for full story.)
• “1/4 of mutations linked to childhood diseases
are debatable.”
• In a study of 60,000 people, on average each had
53 “pathogenic” variants…
http://www.theatlantic.com/science/archive/2015/12/why-human-genetics-research-
is-full-of-costly-mistakes/420693/

40. Very few people are trained in
both data analysis and biology.
(More on this later)

41. Our publishing system has
become a real problem.
• The journal system costs more than $10bn/yr, with profit margins
estimated at 20-30% (see citation, below).
• Articles in high impact factor journals have lower statistical power.
• High-IF journals have higher rates of retractions (which cannot
solely be attributed to “attention paid”)
• We publish in PDF form, which is computationally opaque.
• Publishing is slow!
$10bn/year: http://www.stm-assoc.org/2015_02_20_STM_Report_2015.pdf

42. High-impact-factor articles have poor statistical
power.
Our current system rewards A but not B.
Brembs et al., 2013 -
http://journal.frontiersin.org/article/10.3389/fnhum.2013.00291/full

43. High impact factor => high retraction
index.
Brembs et al., 2013 -
http://journal.frontiersin.org/article/10.3389/fnhum.2013.00291/full

44. We just don’t share our data.
• Researchers have virtually no short-term
incentives to share data in useful ways.
• “46% of respondents reported they do not make
their data available to others” – study in ecology
(Tenopir et al., 2011)
• Some “great” stories from the rare disease
community – see NewYorker link, below.
http://www.newyorker.com/magazine/2014/07/21/one-of-a-kind-2

45. We are focused on hypothesis-
driven research.
• Granting agencies require specific
hypotheses, even when little is known.
• This focuses research on “known unknowns”,
and leaves “unknown unknowns” out in the
cold.

46. The problem of lopsided gene characterization is
pervasive: e.g., the brain "ignorome"
"...ignorome genes do not differ from well-studied genes in terms of connectivity in coexpression
networks. Nor do they differ with respect to numbers of orthologs, paralogs, or protein domains.
The major distinguishing characteristic between these sets of genes is date of discovery, early
discovery being associated with greater research momentum—a genomic bandwagon effect."
Ref.: Pandey et al. (2014), PLoS One 11, e88889. Via Erich Schwarz

47. Most computational research is
not reproducible.
I don’t know of a systematic study, but of papers that I
read, approximately 95% fail to include details necessary
for replication.
It’s very hard to build off of research like this.
(There’s a lot more to say about reproducibility and
replicability than I can fit in here…)

48. What am I doing about it?
1. Open science
2. “Culture hacking” to drive open data.
3. Training!
(I don’t have any guaranteed solutions.All I can do is think & work.)

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. UC Davis and training
My goal here is to support the coalescence and
growth of a local community of practice around
“data intensive biology”.

51. Summer NGS workshop (2010-2017)

52. General parameters:
• Regular intensive workshops, half-day or longer.
• Aimed at research practitioners (grad students & more
senior); open to all (including outside community).
• Novice (“zero entry”) on up.
• Low cost for students.
• Leverage global training initiatives.

53. Thus far & near future
~12 workshops on bioinformatics in 2015.
Trying out Q1 & Q2 2016:
• Half-day intro workshops (27 planned);
• Week-long advanced workshops;
• Co-working hours (“data therapy”).
dib-training.readthedocs.org/

54. 3. Advice to the next generation
(or two generations, if you want me to feel really old.)
a. Get involved with a broad group of people and
ideas (social media FTW!)
b. Learn something about both computing and
biology.
c. Realize that you have nothing but opportunity,
and that there has never been a better time to
be in bio research!

55. Precision Medicine?

56. Thanks for listening!
Please contact me at ctbrown@ucdavis.edu!
Note: I work here!
(I’d like to start with a grad student question?)