My talk at the Appl & Env Microbiology Gordon Research Seminar.

1. Complex metagenome
assembly, + bonus
career thoughts.
C. Titus Brown
UC Davis
ctbrown@ucdavis.edu

3. Hello!
Research background:
Computing, modeling, and data analysis: 1989-2000
(high school & undergrad+)
Molecular biology, genomics, and data analysis:
2000-2007
(grad school + postdoc)
Bioinformatics, data analysis, and Comp Sci: 2007-
present
(assistant professor)
Genomics and Veterinary Medicine (?)

4. Two topics for this talk:
1. Metagenome assembly.
2. Careers & a “middle class” of
bioinformaticians.

5. Shotgun metagenomics
Collect samples;
Extract DNA;
Feed into sequencer;
Computationally analyze.
Wikipedia: Environmental shotgun
sequencing.png

6. To assemble, or not to
assemble?
Goals: reconstruct phylogenetic content and predict
functional potential of ensemble.
Should we analyze short reads directly?
OR
Do we assemble short reads into longer contigs first, and
then analyze the contigs?

7. Assembly: good.
Howe et al., 2014, PMID 24632729
Assemblies yield much
more significant
homology matches.

8. But!
Does assembly work well!?
(Short reads, chimerism, strain
variation, coverage, compute
resources, etc. etc.)

9. Yes: metagenome assemblers
recover the majority of known
content from a mock community.
Velvet IDBA Spades
Total length (>= 0 bp) 1.6E+08 2.0E+08 2.0E+08
Total length (>= 1000 bp) 1.6E+08 1.9E+08 1.9E+08
Largest contig 561,449 979,948 1,387,918
# misassembled contigs 631 1032 752
Genome fraction (%) 72.949 90.969 90.424
Duplication ratio 1.004 1.007 1.004
Results: Dr. Sherine AwadReads from Shakya et al., 2013; pmid 23387867

10. But!
A study of the Rifle site comparing long read
(Moleculo/TruSeq) and short read/assembly content
concluded that their short read assembly was not
comprehensive.
“Low rate of read mapping (18-30%) is typically indicative of
complex communities with a large number of low
abundance genomes or with high degree of
species and strain variations.”
Sharon et al., Banfield lab; PMID 25665577

11. The dirty not-so-secret (?)
about sequence assembly:
The assembler will simply discard two types of data.
1. Low coverage data - can’t be reconstructed with
confidence; may be erroneous.
2. Highly polymorphic data – confuses the assembler.

12. So: why didn’t the Rifle data
assemble?
There are no published approaches that will
discriminate between low coverage and strain
variation.
But we’ve known about this problem for ages.
So we’ve been working with something called
“assembly graphs”.

13. Assembly graphs.
Assembly “graphs” are a way of representing
sequencing data that retains all variation;
assemblers then crunch this down into a single
sequence.
Image from Iqbal et al., 2012. PMID 23172865

14. Our work on assembly graphs
enables:
Evaluation of data set coverage profiles prior to assembly.
Variant calling and quantification on raw metagenomic
data.
Analysis of strain variation.
Evaluation of “what’s in my reads but not in my assembly”.
(See http://ivory.idyll.org/blog/2015-wok-notes.html
for details.)

15. Rifle: Low coverage? (Yes.)
Assembly starts to work @ ~10x

16. Rifle: strain variation? (Maybe.)
ATTCGTCGATTGGCAAAAGTTCTTTCCAGAGCCTACGGGAGAAGTGTA
|||||||||||||||||||||||||||||||| |||||||||||||||
ATTCGTCGATTGGCAAAAGTTCTTTCCAGAGCTTACGGGAGAAGTGTA
GTCAAAATAAGGTGAGGTTGCTAATCCTCGAACTTTTCAC
||||||||||||||| |||||||| ||||||| |||||||
GTCAAAATAAGGTGAAGTTGCTAACCCTCGAATTTTTCAC
A typical subalignment between all short reads & one long read:
If we saw many medium-high coverage alignments with this
level of variation, => strain variation.

17. My thoughts on metagenome
assembly & Rifle data:
The Rifle short-read data is low coverage, based
on both indirect (in paper) and direct (our)
observations. This is the first reason why it didn’t
assemble well.
Strain variation is also present, within the limits of
low coverage analysis. That will cause problems
in future
=> Your methods limit and bias your results.

18. The problem:
Assembly graphs are coming to all of
genomics.
Because they are fundamentally different
they require a completely new bioinformatics
tool chain. (They don’t use FASTA…)
For better or for worse, us bioinformaticians
are not going to write tools that are easy to
use.
It’s hard;
There’s little incentive;
The tool/application needs are incredibly

19. Who ya gonna call??
…to do your bioinformatics?

20. Choices
(1) Focus on biology and avoid computation as much as
possible.
(2) Integrate large scale data analysis into your biology.
(3) Become purely computational
https://commons.wikimedia.org/wiki/File:Three_options_-_three_choices_scheme.png

21. Choices
(1) Focus on biology and avoid computation as much as
possible.
(2) Integrate large scale data analysis into your biology.
(3) Become purely computational.

22. Towards a “bioinformatics
middle class”
Most bioinformaticians are quite ignorant of the biology
you’re doing; biologists are often more aware of the
bioinformatics they’re using.
There is amazing opportunity at the intersection of
biology and computing.
I think of it as a “bioinformatics middle class” –
biologists who are comfortable with computing, and
deploy large scale data analysis in the service of their
biological work.

23. Towards a “bioinformatics
middle class”
We need many more biologists who have an
intuitive & deep understanding of the
computing.
Such people are rare, and there is no defined
“pipeline” for them. Training must be self-
motivated.
(And higher ed has really abdicated its
responsibilities in this area.)

24. My top four suggestions
(more at end)
1. Don’t avoid computing; embrace it.
2. Invest in the Internet and social media
(blogs, Twitter) – seqanswers, biostars, etc.
3. Be patient and aware of the time it takes time
to effectively cross-train.
4. Seek out formal training opportunities.

25. If you’re a senior scientist, or
know any:
Ask them to lobby for funding at this
intersection.
Ask them to lobby for good (nay, excellent)
funding for training opportunities.
Make sure they respect the challenges and
opportunities of large scale data analysis and
modeling (along with those who do it).

26. Career benefits of doing large-
scale data analysis.
Alternative career paths (i.e. “jobs actually exist in this
area.”)
Flexibility in work hours & location.
Work with an even broader diversity of people and
projects.

27. Dangers:
It’s easy to get caught up in the computing and ignore
the biology!
…but right now training & culture are tilted too much
towards experimental and field research, which
presents its own problems in a data-intensive era of
research.

28. What’s coming?
Lots more data.

29. Where am I going?
Data integration.
Figure 2. Summary of challenges associated with the data integration in the proposed project.
Figure via E. Kujawinski

30. An optimistic message
This is a great time to be alive and doing
research!
We can look at & try to understand
environmental microbes with many new tools
and new approaches!
The skills you need to do this extend across
disciplines, across the public and private
sectors, and cannot be automated or
outsourced!

31. Thank you for listening!
I’ll be here all week; really
looking forward to it!

32. More advice.
don’t avoid computing
teach and train what you do know; put together classes and
workshops;
host and run software and data carpentry workshops, then put
together more advanced workshops;
do hackathons or compute-focused events where you just sit
down in groups and work on data analysis.
(push admin to support all this, or just do it without your admin);
invest in the internet and social media – blogs, twitter, biostars…
take a CS prof to lunch, seek joint funding, do a sabbatical in a
purely compute lab, etc.
support open source bioinformatics software
invest in reproducibility
be aware that compute people’s time is as or more
oversubscribed as yours & prospectively value it.