The document discusses the challenges and opportunities that will arise from the exponential growth of biological data in the coming years. It outlines four key areas: 1) Research approaches will need to effectively analyze infinite amounts of data. 2) Software and decentralized infrastructure will be needed to process the data. 3) Open science and reproducible research practices are important for data-driven biology. 4) Training the next generation of biologists in data analysis skills will be a major challenge. The document advocates for open source tools, reproducible research methods, and expanded training programs to help biology take advantage of the coming data deluge.

1. A data intensive future:
How can biology take full advantage of
the coming data deluge?
C.Titus Brown
School ofVeterinary Medicine;
Genome Center & Data Science Initiative
1/22/16
#titusplantz
Slides are on slideshare.net/c.titus.brown/

2. (My one plant collaboration)
Helping Shamoni Maheshwari (Comai Lab)
w/analysis of ChIP-seq data from Arabidopsis.

3. Outline
0. Background: what’s coming?
1. Research: what do we do with infinite data?
2. Development: software and infrastructure.
3. Open science & reproducibility.
4. Training

4. 0. Background
What is going to be happening in the next 5
years with biological data generation?

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

6. (2015 was a good year)

7. Oxford Nanopore sequencing
Slide viaTorsten Seeman

8. Nanopore technology
Slide viaTorsten Seeman

9. Scaling up --

10. Scaling up --

11. Slide viaTorsten Seeman

12. http://ebola.nextflu.org/

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

14. “DeepDOM” cruise: examination
of dissolved organic matter &
microbial metabolism vs physical
parameters – potential collab.
Via Elizabeth Kujawinski
Another challenge beyond volume and velocity – variety.

15. Data integration.
Figure 2. Summary of challenges associated with the data integration in the proposed project.
Figure via E. Kujawinski

16. CRISPR
The challenge with genome editing is fast
becoming what to edit rather than how to do.

17. A point for reflection…
Increasingly, the best guide to the next 10 years
of biology is science fiction ...

18. 1. Research
Working with ~infinite amounts of data, and
doing something effective with it.

19. 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.

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

21. Digital normalization

22. Digital normalization

23. Digital normalization

24. Digital normalization

25. Digital normalization

26. Digital normalization

27. Computational problems now scale with information
content rather than data set size.
Most samples can be reconstructed via de
novo assembly on commodity computers.

28. A local collaboration:
The horse genome &
transcriptome
Tamer Mansour w/Bellone, Finno, Penedo, &
Murray labs.

29. Input data
Tissue Library length #samples #frag(M) #bp(Gb)
BrainStem PE fr.firststrand 101 8 166.73 33.68
Cerebellum PE fr.firststrand 100 24 411.48 82.3
Muscle PE fr.firststrand 126 12 301.94 76.08
Retina PE fr.unstranded 81 2 20.3 3.28
SpinalCord PE fr.firststrand 101 16 403 81.4
Skin PE fr.unstranded 81 2 18.54 3
SE fr.unstranded 81 2 16.57 1.34
SE fr.unstranded 95 3 105.51 10.02
Embryo ICM PE fr.unstranded 100 3 126.32 25.26
SE fr.unstranded 100 3 115.21 11.52
Embryo TE PE fr.unstranded 100 3 129.84 25.96
SE fr.unstranded 100 3 102.26 10.23
Total 81 1917.7 364.07

30. equCabs current status -
NCBI Annotation
Feature Acc Annotation GFF Refseq DB
Total no of genes 25565
ptn coding genes 19686
Coding RNA NM BestRefSeq 764 1097
Coding RNA XM Gnomon 31578 31346
Non coding RNA NR BestRefSeq 348 726
Non coding RNA XR Gnomon 3311 3310
Total 36001 36479
32342 coding transcripts encoded by 19686 genes
(average 1.6 transcript per gene)
There are 3034 pseudo genes
(with no annotated transcripts)
Status count
reviewed 4
Validated 267
Provisional 540
Predicted 7
inferred 279
Tamer Mansour

31. Library prep
Read
trimming
Mapping to ref
Merge rep.
Trans Ass.
Merge byTiss.
Predict ORF
VariantAna
Update dbvar
Haplotype ass
Pool/diginorm
Predict ncRNA
Filter & Compare Ass.
filter knowns
Compare to public ann. Merge All Ass.
Mapping to ref
Trans Ass.
Tamer Mansour

32. Digital normalization & (e.g.)
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

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

34. The ORF problem
Hestand et al 2014: “we identified 301,829 positions with SNPs or small indels within
these transcripts relative to EquCab2. Interestingly, 780 variants extend the open reading
frame of the transcript and appear to be small errors in the equine reference genome”
Tamer Mansour

35. We merged the assemblies into six tissue-specific transcription
profiles for cerebellum, brainstem, spinal cord, retina, muscle and
skin.The final merger of all assemblies overlaps with 63% and 73% of
NCBI and Ensembl loci, respectively, capturing about 72% and 81%
of their coding bases. Comparing our assembly to the most recent
transcriptome annotation shows ~85% overlapping loci. In addition, at
least 40% of our annotated loci represent novel transcripts.
Tamer Mansour

36. 2. Software and infrastructure
Alas, practical data analysis depends on
software and computers, which leads to
depressingly practical considerations for
gentleperson scientists.

37. Software
It’s all well and good to develop new data
analysis approaches, but their utility is greater
when they are implemented in usable software.
Writing, maintaining, and progressing research
software is hard.

38. 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 (?)
• Developed as a “true” open source project.
The khmer software package, Crusoe et al., 2015. http://f1000research.com/articles/4-900/v1

39. Challenges:
Research vs stability!
Stable software for users, & platform for future
research;
vs research “culture”
(funding and careers)

40. Infrastructure issues
Suppose that we have a nice ecosystem of bioinformatics &
data analysis tools.
Where and how do we run them?
Consider:
1. Biologists hate funding computational infrastructure.
2. Researchers are generally incompetent at building and
maintaining usable infrastructure.
3. Centralized infrastructure fails in the face of infinite data.

41. 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

42. 3. Open science and
reproducibility
In my experience, most researchers* cannot
replicate their own computational analyses, much
less reproduce those published by anyone else.
*This doesn’t apply to anyone in this
audience; you’re all outliers!

43. IPython Notebook: data + code =>
IPython)Notebook)

44. To reproduce our papers:
git clone <khmer> && python setup.py install
git clone <pipeline>
cd pipeline
wget <data> && tar xzf <data>
make && cd ../notebook && make
cd ../ && make

45. 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

46. Literate graphing & interactive
exploration
Camille Scott

47. Why do we do this?
• We work faster and more reliably.
• We can build on our own (and others’)
research.
• Robust computational research, released
early, gives us a competitive advantage.

48. 4.Training
Methods and tools do little without a trained
hand wielding them, and a trained eye
examining the results.

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.
• Leveraging 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. The End.
• If you think 5-10 years out, we face significant
practical issues for data analysis in biology.
• We need new algorithms/data structures,
AND good implementations, AND better
computational practice,AND training.
(It’s a pretty good time to be doing biology.)

55. Thanks for listening!
Please contact me at ctbrown@ucdavis.edu!