This document summarizes Adina Howe's presentation on the rapid advances in DNA sequencing technologies and their applications to microbial ecology. Some key points include: - Next generation sequencing (NGS) has led to drastically reduced costs of DNA sequencing, enabling large-scale metagenomic projects. - Computational challenges remain for assembling metagenomic datasets due to their diversity, variability in abundance between organisms, and sequencing errors. Methods like digital normalization help address these challenges. - NGS is revolutionizing fields like microbial ecology by allowing sequencing of entire communities directly from environmental samples without requiring isolation or culture of individual organisms.

1. THERE AND BACK AGAIN
4th Annual BCB Symposium, March 30, 2018
Adina Howe
Assistant Professors
Department of Agricultural and
Biosystems Engineering
Iowa State University
www.germslab.org
NGS SEQUENCING

2. HOW DID WE GET HERE

3. Understanding community dynamics
¨ Who is there?
¨ What are they doing?
¨ How are they doing it?
Kim Lewis, 2010

4. Gene / Genome Sequencing
¨ Collect samples
¨ Extract DNA
¨ Sequence DNA
¨ “Analyze” DNA to identify its content and origin
Taxonomy
(e.g., pathogenic E. Coli)
Function
(e.g., degrades cellulose)

5. Cost of Sequencing
Stein, Genome Biology, 2010
E. Coli genome 4,500,000 bp ($4.5M, 1992)
1990 1992 1994 1996 1998 2000 2003 2004 2006 2008 2010 2012
Year
0.1
1
10
100
1,000
10,000
100,000
1,000,000
DNASequencing,Mbpper$
10,000,000
100,000,000

6. Rapidly decreasing costs with
NGS Sequencing
Stein, Genome Biology, 2010
Next Generation Sequencing
4,500,000 bp (E. Coli, $100, presently)
1990 1992 1994 1996 1998 2000 2003 2004 2006 2008 2010 2012
Year
0.1
1
10
100
1,000
10,000
100,000
1,000,000
DNASequencing,Mbpper$
10,000,000
100,000,000

7. Effects of low cost sequencing…
First free-living bacterium sequenced for
billions of dollars and years of analysis
Personal genome can be
mapped in a few days and
hundreds of dollars

8. The experimental continuum
Single Isolate
Pure Culture
Enrichment
Mixed Cultures
Natural systems

9. The era of big data in biology
Stein, Genome Biology, 2010
Computational Hardware
(doubling time 14 months)
Sanger Sequencing
(doubling time 19 months)
NGS (Shotgun) Sequencing
(doubling time 5 months)
1990 1992 1994 1996 1998 2000 2003 2004 2006 2008 2010 2012
Year
0
1
10
100
1,000
10,000
100,000
1,000,000
DiskStorage,Mb/$
0.1
1
10
100
1,000
10,000
100,000
1,000,000
DNASequencing,Mbpper$
10,000,000
100,000,000
0.1
1
10
100
1,000
10,000
100,000
1,000,000
10,000,000
100,000,000

10. Postdoc experience with data
2003-2008 Cumulative sequencing in PhD = 2000 bp
2008-2009 Postdoc Year 1 = 50 Gbp
2009-2010 Postdoc Year 2 = 450 Gbp
2014 = 50 Tbp
today = 0.5 to 1 Tbp budgeted per project (PhD student)

11. Tackling Soil Biodiversity
Source: Chuck Haney
C. Titus Brown, James Tiedje, Qingpeng Zhang, Jason Pell (MSU)
Janet Jansson, Susannah Tringe (JGI)

13. THE DIRT ON SOIL
Biodiversity in the dark, Wall et al., Nature Geoscience, 2010 Jeremy Burgress
MAGNIFICENT BIODIVERSITY

14. THE DIRT ON SOIL
SPATIAL HETEROGENEITY
http://www.fao.org/ www.cnr.uidaho.edu

15. THE DIRT ON SOIL
DYNAMIC

16. THE DIRT ON SOIL
INTERACTIONS: BIOTIC, ABIOTIC, ABOVE, BELOW, SCALES
Philippot, 2013, Nature Reviews Microbiology

17. Data compression
http://siliconangle.com/files/2010/09/image_thumb69.png

18. de novo assembly
vCompresses dataset size significantly
vImproved data quality (longer sequences, gene order)
vReference not necessary (novelty)
Raw sequencing data (“reads”) Computational algorithms Informative genes / genomes

19. Metagenome assembly…a scaling
problem.

20. Shotgun sequencing and de novo
assembly
It was the Gest of times, it was the wor
, it was the worst of timZs, it was the
isdom, it was the age of foolisXness
, it was the worVt of times, it was the
mes, it was Ahe age of wisdom, it was th
It was the best of times, it Gas the wor
mes, it was the age of witdom, it was th
isdom, it was tIe age of foolishness
It was the best of times, it was the worst of times, it was the age of
wisdom, it was the age of foolishness

21. Practical Challenges – Intensive
computing
Howe et al, 2014, PNAS
Months of
“computer
crunching” on a
super computer

22. Practical Challenges – Intensive
computing
Howe et al, 2014, PNAS
Months of
“computer
crunching” on a
super computer
Assembly of 300 Gbp (70,000
genomes worth) can be done with
any assembly program in less than
14 GB RAM and less than 24 hours.
50 Gbp = 10,000 genomes

23. Four main challenges for de novo
sequencing of metagenomes.
¨ Repeats.
¨ Low coverage.
¨ Errors - these introduce breaks in the construction of
contigs.
¨ Variation in coverage – transcriptomes and
metagenomes (PCR amplification biases)
Challenge: assembler must distinguish between
erroneous connections and real connections.

24. Natural community characteristics
u Diverse
è Many organisms
(genomes)

25. Natural community characteristics
u Diverse
è Many organisms
(genomes)
u Variable abundance
è Most abundant organisms, sampled
more often
è Assembly requires a minimum amount
of sampling
è More sequencing, more errors
Sample 1x

26. Natural community characteristics
u Diverse
è Many organisms
(genomes)
u Variable abundance
è Most abundant organisms, sampled
more often
è Assembly requires a minimum amount
of sampling
è More sequencing, more errors
Sample 1x Sample 10x

27. Natural community characteristics
u Diverse
è Many organisms
(genomes)
u Variable abundance
è Most abundant organisms, sampled
more often
è Assembly requires a minimum amount
of sampling
è More sequencing, more errors
Sample 1x Sample 10x
Overkill

28. Digital normalization
True sequence (unknown)
Reads
(randomly sequenced)
Brown et al., 2012, arXiv
Howe et al., 2014, PNAS
Zhang et al., 2014, PLOS One
Titus Brown , Jason Pell, Qingpeng Zhang

29. Digital normalization
True sequence (unknown)
Reads
(randomly sequenced)
X
Brown et al., 2012, arXiv
Howe et al., 2014, PNAS
Zhang et al., 2014, PLOS One
Titus Brown , Jason Pell, Qingpeng Zhang

30. Digital normalization
True sequence (unknown)
Reads
(randomly sequenced)
X
X
X
X
X
X
X
X
X
X
X
Brown et al., 2012, arXiv
Howe et al., 2014, PNAS
Zhang et al., 2014, PLOS One
X = Sequencing errors L
Titus Brown , Jason Pell, Qingpeng Zhang

31. Digital normalization
True sequence (unknown)
Reads
(randomly sequenced)
X
X
X
X
X
X
X
X
X
X
X
Brown et al., 2012, arXiv
Howe et al., 2014, PNAS
Zhang et al., 2014, PLOS One
Titus Brown , Jason Pell, Qingpeng Zhang

32. Digital normalization
True sequence (unknown)
Reads
(randomly sequenced)
X
X
X
X
X
X
X
X
X
If next read is from a high
coverage region - discard
X
X
Brown et al., 2012, arXiv
Howe et al., 2014, PNAS
Zhang et al., 2014, PLOS One
Titus Brown , Jason Pell, Qingpeng Zhang

33. Digital normalization
True sequence (unknown)
Reads
(randomly sequenced)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Redundant reads
(not needed for assembly)
Brown et al., 2012, arXiv
Howe et al., 2014, PNAS
Pell et al, 2012, PNAS
Zhang et al., 2014, PLOS One
v Scales datasets for assembly up to 95% - same assembly
outputs.
v Genomes, mRNA-seq, metagenomes (soils, gut, water)

34. Tackling Soil Biodiversity
Source: Chuck Haney
C. Titus Brown, James Tiedje, Qingpeng Zhang, Jason Pell (MSU)
Janet Jansson, Susannah Tringe (JGI)

35. The reality?

36. More like…
Source: Chuck HaneyHowe et. al, 2014, PNAS

37. The Future
¨ More data, more samples, better references
¨ Expense will be in sampling – not sequencing or
even data analysis
¨ All biologists will need to know how to use a pipette
and write computer programs
¨ Large-scale, collaborative projects rather than
single PI efforts

38. Soils @ ISU
¨ CABBI DOE Bioenergy Research Center / ISU: Can
we predict where to grow the best bioenergy
crops? How come some bioenergy crops are so
good at finding nitrogen and some not?
¨ USDA ARS / ISU : What happens during
composting? Does the compost source matter?
What is the timing of nutrients? Can we manage it?
¨ USDA ARS / ISU : Soil as an environmental
pathway: What happens in the soil in traditionally
managed agriculture? Manure-amended soils?

39. PART II.
Two blog posts on this:
adina.github.io
“Job Hunt Wrap Up”
“My job application to ISU”

40. Background
Purdue University, BSME,
Mechanical Engineering
Purdue University, MS,
Environmental Engineering
(Sustainability)

41. Background
Purdue University, BSME,
Mechanical Engineering
Purdue University, MS,
Environmental Engineering
(Sustainability)
University of Iowa, PhD,
Environmental Engineering
(Microbiology/Bioremediation)

42. Background
Purdue University, BSME,
Mechanical Engineering
Purdue University, MS,
Environmental Engineering
(Sustainability)
University of Iowa, PhD,
Environmental Engineering
(Microbiology/Bioremediation)
Michigan State University
NSF Postdoc Math and Biology Fellow (cross-training)
Microbial Ecology (Jim Tiedje)
Bioinformatics (Titus Brown)

43. Background
Purdue University, BSME,
Mechanical Engineering
Purdue University, MS,
Environmental Engineering
(Sustainability)
University of Iowa, PhD,
Environmental Engineering
(Microbiology/Bioremediation)
Michigan State University
NSF Postdoc Math and Biology Fellow (cross-training)
Microbial Ecology (Jim Tiedje)
Bioinformatics (Titus Brown)
Computational Biologist
Microbiology / Microbial Ecology

44. Business vs. govt vs. academics

47. What I turned to…

48. Three main questions
¨ When do I start looking for jobs?
¨ Where do I look for jobs?
¨ What should I say about myself?

49. Job Hunt Statistics
¨ 2 Postdocs
¨ 2 first author papers
¨ 3 non-first author papers
¨ $3 million grant funded (co-PI)
¨ $325K grants historically (PI)
¨ Backup plan – full staff scientist at Argonne
National Laboratory

50. Job hunt statistics (cont.)
¨ Environmental engineering, agricultural engineering,
biology, microbiology, ecology and evolution
¨ 30 applications
¤ Prioritized < 10 applications, 3 versions
¤ Research statement, teaching statement, CV, references
¨ 5 on-site interviews (see blog for questions I was asked)
¨ 1 phone interview
¨ 8 rejection communications (dream offer tangent…)
¨ 2 offers

51. Evaluate yourself.
Pros
¨ Skill set unique (for now)
¨ Diverse systems
¨ Engr / Biology /
Ecology
¨ Comfortable
¨ Great reference writers
¤ Note to talk about
reference writer packet
Cons
¨ Defining “who” I am
¨ Publication productivity
¨ Husband required

52. Want it.
¨ Personal Impact
¤ Research
¤ Mentorship
¨ Enjoy what you’re selling
¤ Grant writing
¤ Publishing
¤ Presenting
¨ No man is an island

53. Believe it.
¨ I am very good at what I
do.
¨ My work deserves to be
funded.
¨ No imposter syndrom-ing
during application writing

54. Prioritize the effort.
¨ Job hunting
requires:
¤ Mood
¤ Time
¤ Emotions
¤ Focus
¨ You have another
job
¨ Wrote applications
on weekend (half
day)

55. Only apply if you can imagine yourself
accepting the offer.

56. Stay organized
¨ Addressees
¨ Specifics
¨ Deadlines
¨ References
¨ Save postings &
application

57. Be positive.
¨ It is about fit.
¨ Many factors you cannot control.
¨ This is not an evaluation of your worth.
¨ Resilience is key.

58. Let it be “Out the door”

59. Manage your own expectations.
¨ Know myself.
¨ Let myself win & lose.
¨ Get jazzed during the interview.
¨ Find nice, hardy laughing people.
¨ Find really smart people that would help mentor
me..
¨ Work hard but find balance.
¨ Dream it.

60. The ISU job posting

62. Thank you!
BCB:
Shane Dooley
Schuyler Smith
Paul Villenueva
www.germslab.org