This document summarizes a presentation about using de Bruijn graphs for genome assembly from short read sequencing data. It discusses how de Bruijn graphs make assembly of millions of reads tractable, and addresses challenges like choosing the right k-mer size, handling tips and bubbles, and dealing with memory issues in metagenome assembly. It also presents solutions to memory problems, describes how lossy de Bruijn graphs work, and explains how gap filling can correct assembly errors and account for strain variation.

1. Learning to love de Bruijn graphs
Ben Woodcroft,
Australian Centre for Ecogenomics (ACE)
Winter School in Bioinformatics, 2015

2. A slide from Torsten Seemann

3. K-mers and assembly
• For next-generation sequencing, comparison
of each read with each other read is
impossible.
– E.g. 10 million reads -> 107 x 107 read-read
comparisons. Slowww..
• K-mers and de Bruijn graphs help make things
tractable

4. K-mers and assembly

5. Forks

6. K-mer too small

7. K-mer too large

8. My favourite k-mer size

9. My favourite k-mer size
With a 100bp read, this can never happen with a k-mer size of 51

10. Less tips, more bubbles
As read lengths get longer, assemblers must move
from handling dead ends in the graph to handling
bubbles.

11. Tips and bubbles

12. Metagenome assembly
Me: “I know, why don’t I just assemble all my
data together?”
Run assembly
Wait 4 days
Out of memory allocating 18.4 million terabytes
of RAM.

13. Solutions to RAM issues
• Quality trimming
• Hard trimming
• Throwing away a proportion of reads
randomly
• Sequencing something else

14. Lossy de Bruijn graphs
The number of k-mers observed is vanishingly small
relative to the total number of possible k-mers
The human genome: ~3Gbp = ~3×109 k-mers
Total possible 51-mers: 451 = ~1030
0.00000000000000000002%
When making a list of k-mers, counting extra ones
probably has little effect on assembly.

15. Bloom filters
A low memory k-mer “store”

16. Is my k-mer in these reads?
From a bloom filter, the answer is either “no” or
“probably”

17. A finishing approach to assembly
A central assumption of this method is
that the genome is “mostly” complete

18. Scaffolding without mate pair data

19. Gap filling vs. assembly
• Regular assembly ain’t easy
• Re-assembly is more straightforward because
you are trying to get to somewhere

20. Gap filling can correct assembly errors
• Contigs often contain errors right at the ends
of contigs
• By starting to search a bit back (e.g. 200bp)
away from the end of the contig, these errors
can be overcome

21. Gap-filling can account for strain
variation
github.com/wwood/finishm

22. Thanks!
• Slideshare.com/benjwoodcroft
• Github.com/wwood
• Ecogenomic.org