This document introduces khmer, a platform for scalable sequence analysis. It discusses how khmer uses k-mers to provide implicit read alignments and assemble sequences using de Bruijn graphs. It also describes some of the challenges with k-mers, such as each sequencing error resulting in novel k-mers. The document outlines khmer's data structures and algorithms for efficiently counting k-mers and represents de Bruijn graphs. It discusses how khmer has been applied to real biological problems and highlights areas of current research using khmer, such as error correction, variant calling, and assembly-free comparisons of data sets.

1. Building khmer, a platform
for research in scalable
sequence analysis
C. Titus Brown
ctb@msu.edu

2. Hello!
Assistant Professor; Microbiology; Computer Science;
etc.
More information at:
• ged.msu.edu/
• github.com/ged-lab/
• ivory.idyll.org/blog/
• @ctitusbrown

3. Introducing k-mers
CCGATTGCACTGGACCGA (<- read)
CCGATTGCAC
CGATTGCACT
GATTGCACTG
ATTGCACTGG
TTGCACTGGA
TGCACTGGAC
GCACTGGACC
ACTGGACCGA

4. K-mers give you an
implicit alignment
CCGATTGCACTGGACCGATGCACGGTACCGTATAGCC
CATGGACCGATTGCACTGGACCGATGCACGGTACCG

5. K-mers give you an
implicit alignment
CCGATTGCACTGGACCGATGCACGGTACCGTATAGCC
CATGGACCGATTGCACTGGACCGATGCACGGTACCG
CATGGACCGATTGCACTGGACCGATGCACGGACCG
(with no accounting for mismatches or indels)

6. De Bruijn graphs –
assemble on overlaps
J.R. Miller et al. / Genomics (2010)

7. The problem with k-mers
CCGATTGCACTGGACCGATGCACGGTACCGTATAGCC
CATGGACCGATTGCACTCGACCGATGCACGGTACCG
Each sequencing error results in k novel k-mers!

8. Conway T C , Bromage A J Bioinformatics 2011;27:479-486
© The Author 2011. Published by Oxford University Press. All rights reserved. For Permissions,
please email: journals.permissions@oup.com
Assembly graphs scale with data size, not
information.

9. Practical memory
measurements (soil)
Velvet measurements (Adina Howe)

10. Counting k-mers
efficiently (RAM)

11. This leads to good things.
Efficient online
counting of k-mers
Trimming reads
on abundance
Efficient De
Bruijn graph
representations
Read
abundance
normalization

12. Data structures &
algorithms papers
• “These are not the k-mers you are looking for…”,
Zhang et al., arXiv 1309.2975, in review.
• “Scaling metagenome sequence assembly with
probabilistic de Bruijn graphs”, Pell et al., PNAS 2012.
• “A Reference-Free Algorithm for Computational
Normalization of Shotgun Sequencing Data”, Brown
et al., arXiv 1203.4802, under revision.

13. Data analysis papers
• “Tackling soil diversity with the assembly of large,
complex metagenomes”, Howe et al., PNAS, 2014.
• Assembling novel ascidian genomes &
transcriptomes, Lowe et al., in prep.
• A de novo lamprey transcriptome from large scale
multi-tissue mRNAseq, Scott et al., in prep.

14. Lab approach – not
intentional, but working out.
Novel data
structures and
algorithms
Implement at
scale
Apply to real
biological
problems

15. This leads to good things.
Efficient online
counting of k-mers
Trimming reads
on abundance
Efficient De
Bruijn graph
representations
Read
abundance
normalization
(khmer software)

16. Efficient online
counting of k-mers
Trimming reads
on abundance
Efficient De
Bruijn graph
representations
Read
abundance
normalization
Streaming
algorithms for
assembly,
variant calling,
and error
correction
Cloud assembly
protocols
Efficient graph
labeling &
exploration
Data set
partitioning
approaches
Assembly-free
comparison of
data sets
HMM-guided
assembly
Efficient search
for target genes
Currentresearch
(khmer software)

17. How is this feasible?!
Representative half-arsed lab software development
Version that
worked once, for
some publication.
Grad student 1
research
Grad student 2
research
Incompatible and broken code

18. Stable version
Grad student 1
research
Grad student 2
research
Stable, tested code
Run tests
Run tests
Run tests
Run tests
Run tests
A not-insane way to do software development

19. A not-insane way to do software development
Stable version
Grad student 1
research
Grad student 2
research
Stable, tested code
Run tests
Run tests
Run tests
Run tests
Run tests
Run tests
Run tests

20. Testing & version control
– the not so secret sauce
• High test coverage - grown over time.
• Stupidity driven testing – we write tests for bugs after
we find them and before we fix them.
• Pull requests & continuous integration – does your
proposed merge break tests?
• Pull requests & code review – does new code meet
our minimal coding etc requirements?
o Note: spellchecking!!!

21. Integration testing
• khmer is designed to work with other packages.
• For releases >= 1.0, we now have added
acceptance tests to make sure that khmer works
OK with other packages.
• These acceptance tests are based on integration
tests, than in turn come from an education &
documentation effort…

22. khmer-protocols

23. khmer-protocols:
• Provide standard “cheap”
assembly protocols for the cloud.
• Entirely copy/paste; ~2-6 days
from raw reads to assembly,
annotations, and differential
expression analysis. ~$150 per
data set (on Amazon rental
computers)
• Open, versioned, forkable,
citable….
Read cleaning
Diginorm
Assembly
Annotation
RSEM differential
expression

24. Literate testing
• Our shell-command tutorials for bioinformatics can
now be executed in an automated fashion –
commands are extracted automatically into shell
scripts.
• See: github.com/ged-lab/literate-resting/.
• Tremendously improves peace of mind and
confidence moving forward!
Leigh Sheneman

25. Doing things right
=> #awesomesauce
Protocols in English
for running analyses in
the cloud
Literate reSTing =>
shell scripts
Tool
competitions
Benchmarking
Education
Acceptance
tests

26. Benchmarking protocols
Data subset; AWS m1.xlarge
~1 hour
(See PyCon 2014 talk; video and blog post.)

27. Benchmarking protocols
Complete data; AWS m1.xlarge
~40 hours
(See PyCon 2014 talk; video and blog post.)

28. Efficient online
counting of k-mers
Trimming reads
on abundance
Efficient De
Bruijn graph
representations
Read
abundance
normalization
Streaming
algorithms for
assembly,
variant calling,
and error
correction
Cloud assembly
protocols
Efficient graph
labeling &
exploration
Data set
partitioning
approaches
Assembly-free
comparison of
data sets
HMM-guided
assembly
Efficient search
for target genes
Currentresearch

29. Genomic intervals shared
between data sets
Qingpeng Zhang
* Assembly free!

30. Error correction via graph
alignment
Jason Pell and Jordan Fish

31. Error correction on
simulated E. coli data
1% error rate, 100x coverage.
Jordan Fish and Jason Pell
TP FP TN FN
ideal 3,469,834 99.1% 8,186 460,655,449 31,731 0.9%
1-pass 2,827,839 80.8% 30,254 460,633,381 673,726 19.2%
1.2-pass 3,403,171 97.2% 8,764 460,654,871 98,394 2.8%
(corrected) (mistakes) (OK) (missed)

32. Single pass, reference free, tunable, streaming online
variant calling.
Streaming, online variant calling.
See NIH BIG DATA grant, http://ged.msu.edu/.

33. Novelty… to what power?
• “Novelty” requirements for “high impact
publishing”:
o Must do novel algorithm development
o …and apply to novel and interesting data sets.
o (See Josh Bloom, https://medium.com/tech-talk/dd88857f662)
• We’ve taken on the additional challenge of trying
to develop and maintain a core set of functionality
in research software: novelty cubed? :)

34. Reproducibility
Scientific progress relies on reproducibility of analysis.
(Aristotle, Nature, 322 BCE.)
All our papers now have:
• Source hosted on github;
• Data hosted there or on AWS;
• Long running data analysis =>
‘make’
• Graphing and data digestion
=> IPython Notebook (also in
github)
Qingpeng Zhang

35. Concluding thoughts
• API is destiny – without online counting, diginorm &
streaming approaches would not have been
possible.
• Tackle the hard problems – engineering
optimization would not have gotten us very far.
• Testing lets us scale development & process – which
means when something works, we can run with it.

36. Caveats
• Expense and effort – you can spend an infinite
amount of time on infrastructure & process!
o Advice: choose techniques that address actual pain points.
o (See: “Best Practices in Scientific Computing”, Wilson et al., 2014)
• Funders and reviewers just don’t care – adopt good
software practices for yourself, not others.
o Advice: briefly mention keywords in grants, papers.
• Advisors just don’t care – see above.
o These are 90% true statements :>

37. Can we crowdsource
bioinformatics?
We already are! Bioinformatics is already a tremendously
open and collaborative endeavor. (Let’s take advantage
of it!)
“It’s as if somewhere, out there, is a collection of totally free
software that can do a far better job than ours can, with
open, published methods, great support networks and
fantastic tutorials. But that’s madness – who on Earth
would create such an amazing resource?”
-
http://thescienceweb.wordpress.com/2014/02/21/bioinfor
matics-software-companies-have-no-clue-why-no-one-
buys-their-products/

38. Thanks!

39. Prospective: sequencing
tumor cells
• Goal: phylogenetically reconstruct causal “driver
mutations” in face of passenger mutations.
• 1000 cells x 3 Gbp x 20 coverage: 60 Tbp of
sequence.
• Most of this data will be redundant and not useful.
• Developing diginorm-based algorithms to eliminate
data while retaining variant information.

40. Where are we taking this?
• Streaming online algorithms only look at data
~once.
• Diginorm is streaming, online…
• Conceptually, can move many aspects of
sequence analysis into streaming mode.
=> Extraordinary potential for computational efficiency.