This document discusses the challenges of analyzing large genomic datasets and summarizes some of the speaker's research applying new techniques to address these challenges. The speaker develops tools to help biologists efficiently analyze vast amounts of sequencing data. One key technique is "digital normalization" which can speed up analysis by 20-200x while reducing data volume. The speaker has applied this to assemble genomes like the parasitic nematode H. contortus and soil metagenomes. The goal is to make downstream data analysis faster, better, and help answer biological questions.

1. I’ve got the Big Data Blues
C. Titus Brown
ctb@msu.edu
Microbiology, Computer Science, and
BEACON

2. Outline
1. Genetics 101 and 102 - what you need to know.
2. Marek’s Disease – chicken cancer.
3. Generating lots of data – the sequencing
revolution.
4. The problems of data analysis and data
integration.
5. Some preliminary results on Marek’s Disease
5. An apparent digression: chess and computers.
6. My actual research :)

3. Genetics 101: DNA to RNA to protein to phenotype…
Genome
(DNA)
Transcripts
(Genes; RNA)
Proteins
(Amino acids)
Animal
http://commons.wikimedia.org/wiki/File:Spombe_Pop2p_protein_stru
cture_rainbow.png;
http://commons.wikimedia.org/wiki/File:Protein_CA2_PDB_12ca.png

4. …plus diploidy (2x each chromosome)
Genome
(DNA)
Transcripts
(Genes; RNA)
Proteins
(Amino acids)
Animal
GT
A
C

5. …plus regulation and interaction.
Genome
(DNA)
Transcripts
(Genes; RNA)
Proteins
(Amino acids)
Animal
GT
A
C
Regulation
Interaction

6. PHYSICAL
AGENTS
INFECTIOUS
AGENTS
HORMONES RADIATION
GENETIC
FACTORS
CHEMICAL
CARCINOGENS
LIFESTYLE
FACTORS
(slide courtesy Suga Subramanian)

7. Herpesvirus and Cancer
• Epstein-Barr Virus
– Burkitt’s lymphoma
– Hodgkin’s lymphoma
– Nasopharyngeal
carcinoma
• Herpes Virus-8
– Kaposi’s sarcoma
– Multicentric lymphoma
• Mardivirus
– Marek’s Disease
• Viral neoplastic disease
• Alpha-herpesvirus
• Model for Burkitt’s lymphoma
(slide courtesy Suga Subramanian)

8. Clinical Signs Asymmetric Paralysis
http://partnersah.vet.cornell.edu/avian-atlas/

9. Visceral Lymphoma
Liver
NORMAL
LYMPHOMA
Courtesy: John Dunn, USDA

10. Importance of Marek’s Disease
• Agricultural Impact
– Economic losses (2 billion)
– Viral evolution: Increased virulence
– Current Vaccines: Not enough
– Long term viral persistence
• Model Sytem
– Human herpes viral infections
– Viral induced lymphoma
(slide courtesy Suga Subramanian)

11. MAREK’S DISEASE
VIRUS
(MDV)
INBRED CHICKEN
LINES
MD-RESISTANT
LINE
MD-SUSCEPTIBLE
LINE
LINE 62 LINE 73
GENETIC RESISTANCE TO
MAREK’S DISEASE
(slide courtesy Suga Subramanian)

12. What happens when we infect?
Genome
(DNA)
Transcripts
(Genes; RNA)
Proteins
(Amino acids)
Animal
GT
A
C
Regulation
Interaction
Infect with virus
?

13. …how does the virus specifically interact with
genes?
Genome
(DNA)
Transcripts
(Genes; RNA)
Proteins
(Amino acids)
Animal
GT
A
C
Regulation
Interaction
Infect with virus
?
Mechanism of regulation?

14. …and what are the mechanisms of resistance?
Genome
(DNA)
Transcripts
(Genes; RNA)
Proteins
(Amino acids)
Animal
GT
A
C
Regulation
Interaction
Infect with virus
?
Mechanism of resistance?

15. Digression: DNA sequencing
• Observation of actual DNA sequence
• Counting of molecules
Image: Werner Van Belle

16. Fast, cheap, and easy to generate.
Image: Werner Van Belle

17. Applying sequencing to Marek’s Disease
Genome
(DNA)
Transcripts
(Genes; RNA)
Proteins
(Amino acids)
Animal
GT
A
C
Regulation
Interaction
SEQUENCING

18. Differentially expressed genes (DEG) due to infection
Gene GO Analysis, IPA Pathway Analysis
DEGs in Md5-infected and not in Md5ΔMeq-infected groups
YES NO
Meq-dependent DEGs DEGs not dependent on Meq
DEGs in Line 6 and not in Line 7 DEGs in Line 7 and not in Line 6
YES NO NO YES
Meq-dependent
DEGs involved in
MD resistance
Meq-dependent
DEGs involved in
MD susceptibility
Meq-dependent DEGs
common to both lines
Back to Marek’s disease:
(slide courtesy Suga Subramanian)

19. LINE 6
MD-RESISTANCE: ROLE OF MEQ
MDV MDV-no Meq
Genes involved in
MD-resistance
that are regulated
by Meq
Genes involved in
MD-resistance that
are not regulated
by Meq
1031 1670
(slide courtesy Suga Subramanian)

20. Pathway Analysis: MD resistance
(slide courtesy Suga Subramanian)

21. LINE 7
MD-SUSCEPTIBILITY: ROLE OF MEQ
MDV MDV-no Meq
Genes involved in
MD-susceptibility
that are regulated
by Meq
Genes involved in
MD-susceptibility
that are not
regulated by Meq
650 540
(slide courtesy Suga Subramanian)

22. Pathway Analysis: MD susceptibility
(slide courtesy Suga Subramanian)

23. Next problem: data analysis &
integration!
• Once you can generate virtually any data set you
want…
• …the next problem becomes finding your answer
in the data set!
• Think of it as a gigantic NSA treasure hunt: you
know there are terrorists out there, but to find
them you to hunt through 1 bn phone calls a
day…

24. Digression: “Heuristics”
• What do computers do when the answer is
either really, really hard to compute exactly, or
actually impossible?
• They approximate! Or guess!
• The term “heuristic” refers to a guess, or
shortcut procedure, that usually returns a
pretty good answer.

25. Often explicit or implicit tradeoffs between
compute “amount” and quality of result
http://www.infernodevelopment.com/how-
computer-chess-engines-think-minimax-tree

26. My actual research focus
What we do is think about ways to get
computers to play chess better, by:
– Identifying better ways to guess;
– Speeding up the guessing process;
– Improving people’s ability to use the chess playing
computer
Now, replace “play chess” with
“analyze biological data”...

27. My actual research focus…
We build tools that help experimental biologists work
efficiently and correctly with large amounts of data, to help
answer their scientific questions.
This touches on many problems, including:
• Computational and scientific correctness.
• Computational efficiency.
• Cultural divides between experimental biologists and
computational scientists.
• Lack of training (biology and medical curricula devoid of
math and computing).

28. Not-so-secret sauce: “digital normalization”
• One primary step of one type of data
analysis becomes 20-200x faster, 20-150x
“cheaper”.

29. http://en.wikipedia.org/wiki/JPEG
Lossy compression

30. http://en.wikipedia.org/wiki/JPEG
Lossy compression

31. http://en.wikipedia.org/wiki/JPEG
Lossy compression

32. http://en.wikipedia.org/wiki/JPEG
Lossy compression

33. http://en.wikipedia.org/wiki/JPEG
Lossy compression

34. Raw data
(~10-100 GB)
Analysis "Information"
~1 GB
"Information"
"Information"
"Information"
"Information"
Database &
integration
Restated:
Can we use lossy compression approaches to make
downstream analysis faster and better? (Yes.)
~2 GB – 2 TB of single-chassis RAM

35. Some diginorm examples:
1. Assembly of the H. contortus parasitic nematode
genome.
2. Assembly of two Midwest soil metagenomes,
Iowa corn and Iowa prairie.
3. Reference-free assembly of the lamprey (P.
marinus) transcriptome.

36. 1. The H. contortus problem
• A sheep parasite.
• ~350 Mbp genome
• Sequenced DNA 6 individuals after whole genome
amplification, estimated 10% heterozygosity (!?)
• Significant bacterial contamination.
(w/Robin Gasser, Paul Sternberg, and Erich Schwarz)

37. H. contortus life cycle
Refs.: Nikolaou and Gasser (2006), Int. J. Parasitol. 36, 859-868;
Prichard and Geary (2008), Nature 452, 157-158.

38. Assembly after digital normalization
• Diginorm readily enabled assembly of a 404
Mbp genome with N50 of 15.6 kb;
• Post-processing led to 73-94% complete
genome.
• Diginorm helped by making analysis possible.
– Highly variable population.
– Lots of contamination from microbes.

39. Next steps with H. contortus
• Publish the genome paper 
• Identification of antibiotic targets for
treatment in agricultural settings (animal
husbandry).
• Serving as “reference approach” for a wide
variety of parasitic nematodes, many of which
have similar genomic issues.

40. 2. Soil metagenome assembly

41. A “Grand Challenge” dataset (DOE/JGI)
0
100
200
300
400
500
600
Iowa,
Continuous
corn
Iowa, Native
Prairie
Kansas,
Cultivated
corn
Kansas,
Native
Prairie
Wisconsin,
Continuous
corn
Wisconsin,
Native
Prairie
Wisconsin,
Restored
Prairie
Wisconsin,
Switchgrass
BasepairsofSequencing(Gbp)
GAII HiSeq
Rumen (Hess et. al, 2011), 268 Gbp
MetaHIT (Qin et. al, 2011), 578 Gbp
NCBI nr database,
37 Gbp
Total: 1,846 Gbp soil metagenome
Rumen K-mer Filtered,
111 Gbp

42. Putting it in perspective:
Total equivalent of ~1200 bacterial genomes
Human genome ~3 billion bp
Assembly results for Iowa corn and prairie
(2x ~300 Gbp soil metagenomes)
Total
Assembly
Total Contigs
(> 300 bp)
% Reads
Assembled
Predicted
protein
coding
2.5 bill 4.5 mill 19% 5.3 mill
3.5 bill 5.9 mill 22% 6.8 mill
Adina Howe

43. 3. Sea lamprey gene expression
• Non-native
• Parasite of
medium to
large fishes
• Caused
populations of
host fishes to
crash
Li Lab / Y-W C-D

44. Transcriptome results
• Started with 5.1 billion reads from 50 different tissues.
(4 years of computational research, and about 1 month of compute
time, GO HERE)
• Final assembly contains ~95% of genes (est.)
• This is an extra 40% over previous work.
• Enabling studies in –
– Basal vertebrate phylogeny
– Biliary atresia
– Evolutionary origin of brown fat (previously thought to be
mammalian only!) – J Exp Biol. 2013
– Pheromonal response in adults

45. What are the tissue level changes in gene expression that support
regeneration? Transcriptome analysis of a regenerating vertebrate after SCI
brain
spinal cord
RNA-Seq to determine
differential expression
profile after injury
Sampling >weekly
-/+ Dex
Ona Bloom

46. Challenges ahead
• We need more people working at the interface
– “Priesthood” model doesn’t scale!
– Cultural shifts in biology needed…
• We need more data!
– Data often only makes sense in context of other data
– This is a hard sell: “if you give us 1000x as much data,
we might start to develop some idea of what it
means.”
• We actually know very little about biology still!

47. Open science & sharing
• Science, and biology in particular, is in the
middle of a transition to a “data intensive”
field.
• The sharing ethos is not incentivized properly;
you get more credit for discovering new stuff
than for discoveries resulting from sharing.
• We are focused on sharing: methods,
programs, educational materials…

48. Being disruptive?
Possible initiative from my lab:
“We will analyze your data for you if we can
make your data openly available in 1 yr.”
Will it work, or sink like a stone? Ask me in a
year 

49. MSU’s role in my research
• MSU provides nice infrastructure, great
administrative support, and a truly excellent
community (students, profs, and other
researchers).
• MSU is also uniquely interdisciplinary in many
ways; very few “hard” boundaries in biology
research.

50. Credits
• Marek’s Disease: Suga Subramanian and Hans Cheng (USDA)
• Haemonchus: Erich Schwarz (Caltech/Cornell), Paul Sternberg
(Caltech), Robin Gasser (U. Melbourne)
• Lamprey: Weiming Li (MSU), Ona Bloom (Feinstein), Jen
Morgan (MBL/Woods Hole)
• Great Prairie: Jim Tiedje (MSU), Janet Jansson (LBL), Susanna
Tringe (Joint Genome Inst.)
Funding: MSU; USDA; NSF; NIH.
Drop me a line – ctb@msu.edu