1. Leveraging MinHash for rapid identification of nanopore data on mobile hardware
Brian Ondov
MinION Community Meeting
New York, NY
December 4th, 2015

2. Acknowledgement
This work was funded under Contract No. HSHQDC-07-C awarded to Battelle National Biodefense Institute by the Department of Homeland Security (DHS) Science and Technology Directorate for the management and operation of the National Biodefense Analysis and Countermeasures Center a Federally Funded Research and Development Center. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the DHS or the U.S. Government. DHS does not endorse any products or commercial services mentioned in this presentation.

3. Real time
Portable
High error

4. ?

5. Real time Portable High error Streaming Significance Fast Robust
Low memory
High error

6. K-mer based distance estimate
ACC
ATG
AGT
CAG
ATC
CAT
CCA
CCG
CGA
CGT
GAC
GAT
GGA
GCA
TGG
GTA
TAC
TCG
50%
= 0.50
Fan et al. 2015

7. K-mers Real time Portable High error Streaming Significance Fast
Robust
Low memory
High error

8. Reducing the problem
Andrei Broder, 1997: “On the resemblance and containment of documents”

9. MinHash S ACC ATG AGT ATC CAG CAT CCA CCG CGA GAC CGT GGA GAT TGG GCA
TAC
TCG S

10. MinHash Distance vs. ANI (500 E. coli)

11. RefSeq 600Gb 93Mb (6000x) Sketch: 26 cpuh Distance: 20 cpuh
k = 16, s = 400
600Gb
93Mb (6000x)
55,000 genomes
Acinetobacter baumannii
B. cereus group
Klebsiella pneumoniae
Escherichia coli & Shigella
Mycobacterium
tuberculosis
Streptococcus agalactiae

12. Querying RefSeq Reads 1s Sketch Bloom filter 1s Repeated K-mers 1s

13. Streaming E. coli reads Reads Coverage LCA (lowest distance ties)
P-value (best)
100
15%
Microbes (bacteria/archaea)
3.3e-1
200
27%
Enterobacteriaceae (family)
6.3e-5
300
34%
E. coli K12
2.6e-6
400
44%
2.8e-14
500
51%
1.9e-21
600
57%
3.8e-35
700
62%
6.5e-50
800
67%
5.8e-61
900
71%
2.3e-72
1000
75%
1.8e-86

14. Covering B. anthracis
x 30

15. MinHash Real time Portable High error Streaming Significance Fast
Robust
Low memory
High error

16. ?
87mm
54mm

17. Future applications
Andrei Broder, 1997: “On the resemblance and containment of documents”
metagenomics
Pre-alignment

18. MarBL NHGRI mash.readthedocs.org
Todd Treangen Nicholas Bergman Adam Mallonee Adam Phillippy Sergey Koren
github.com/
MarBL
NHGRI