Coin-Application Mediator Interface. http://arxiv.org/abs/1602.03031

1. A Cryptocurrency with DNA
Sequence Alignment as Proof-of-
work
Halil I. Ozercan*, Atalay M. Ileri*, Alper Gundogdu, A.
Kerim Senol, M. Yusuf Ozkaya, Can Alkan
Bilkent University, Ankara, Turkey

2. Atalay Mert Ileri
Idea, protocols
Introduction to Research course project
H. Ibrahim Ozercan, M. Yusuf Ozkaya,
A. Kerim Senol, Alper Gundogdu
Developers, Bitcoin enthusiasts
Senior Design Project
Undergrad power
No grad students were harmed during the making of this project

3. HTS read alignment
 Aligning HTS reads is a compute intensive
task
 ~35 CPU days per 30X genome using BWA
 ~18K human genomes / year can be sequenced
using HiSeqX Ten
 630K CPU days = ~1800 CPU years per HiSeqX Ten
 Estimated 1 million genomes by the end of 2017
 35 million CPU days = ~100K CPU years for alignment
only

4. HTS read alignment (2)
 Additionally, reference human genome gets an
update every 3-4 years
 Fixes minor alleles
 Fixes collapsed duplications
 Fixes contig orientation (i.e. incorrect inversions)
 Adds new sequence
 For better reliability it is best to remap existing
data to new reference
 All 1000 Genomes Project data are being remapped to
GRCh38

5. Remapping old, or mapping new?
 Large clusters are not infinite resources
 While remapping old data, more new data are
generated, which typically have higher priority
 Computational burden keeps increasing
Proposal: volunteer grid computing

6. Volunteer grid computing: BOINC
 Berkeley Open Infrastructure Network Computing
 Volunteers download “problem sets” from the server,
solve them in “spare time”, upload results back
 Made popular with the SETI@home project
 Some bioinformatics applications are ported
(Rosetta@home, RNAworld, DENIS@home)
 Total computational power of 8.68 PetaFLOPs

7. Read mapping w/BOINC
 Data privacy, making sure
the alignments are correct,
other potential problems
 Main Problem: HTS read
mapping uses more
compute resources on
CPU, RAM, and disk. More
unlikely for volunteers to
dedicate such resources
 Solution: Motivating
volunteers

8. Cryptocurrencies
 Digital “money” that uses cryptography to
ensure security in transactions and to control
creation of new units.
 Bitcoin, Dogecoin, Litecoin, etc.
 Two parts
 Mining: generation of new “block”s
 Transaction: money exchange between peers

9. Bitcoin
 Most popular cryptocurrency
 Invented in 2008, open-source software in
2009
 Block chain is the source of transactions
 Completely decentralized
 In 2013: 2,798,377 GH/s
 As of now: 353,633,397 GH/s

10. Useful in Amsterdam

11. Bitcoin blocks
Nonce: a number such that when the block content is hashed with the nonce, the
result is numerically smaller than the difficulty target.
Proof-of-work: finding the nonce.
• Hard to calculate
• Easy to verify

12. Coinami: BOINC/Bitcoin hybrid
 Calculating the nonce in Bitcoin is simply
burning up compute power.
 No practical use.
 Idea: replace the nonce calculation with
something useful, while keeping the rest of
the cryptocurrency intact
 Coinami: Coin-Application Mediator Interface
 “Application” can be anything that is hard to
compute, easy to verify

13. Coinami: Features
 Not decentralized, but many-centralized.
 Approved sequencing centers are signing authorities
 Root authority merely keeps track of the signing authorities
 Multiplexing reads from multiple samples prevent FASTQ file
reconstruction & enables data privacy
 BWA read aligner, but can be changed
 Uses decoy reads for verification: real reads with previously-known
alignment locations.
 Used to check whether the returned BAM is real BWA output, or forged.
 Read names are also encrypted, not possible to distinguish run IDs,
sample names, decoy vs. queries
 Demultiplexing samples and verification (decoy map checking) are
done simultaneously
 O(1) verification

14. Coinami: Mining

15. Coinami Workflow

16. Coinami Workflow

17. Coinami Workflow

18. Coinami Workflow

19. Sample Job
//These two reads are coming from SAMPLE 1
@SAMPLE1.Read425/1
CCTTNATACTTCCTGGACACCAACTGTTATACNNNGGNNNNNNNNNNNNAATGTCNNNNNCCTGGCCTTTCAAAAGCATAGGGGAATAAATTNNTCAATAA
+
CCCC#EEEEEHHHHHHHHHHHHHHGHHHHH@@###69############;>;<;=#####:9;;;HDHHHEDAEDEEEEEEHHHEEHGGH48##7:<=:<H
@ SAMPLE1.Read425356/1
ACCTAGAAGGCATGAAAAGATTAAGGAAATTTTTTAAAAAGATATTCAATGAAGAAAATATTTTGTTTTGGCTAGCATGTAAAGATTTCTTTTTTTAATGC
+
HHHHGHHHHHHHHGHHHHHHHHHHHHHHHHHHHHHHHHHHGHHDHHHGHHFGHHGHHHHFHHHHHB@DDEHHEEDGE8EDFFFIF@GGGGHHHHHHFGFHB
//These are from SAMPLE 2
@SAMPLE2.Read2340294/1
GGTAACGCTCTATGATCCAGTCGATTTTCAGAGAGACGATGGCCGAGAGATCCGGCTTACGACACTGCCCAAGGGATTAGTAGAACAACAGTGCCACAGGA
+
D@5EEGGGGBFFD8GBDDCDFEEBDADDD########################################################################
@ SAMPLE2.Read4983594/1
GGTGATGACCATGTTTTTGGTTTATCGGCGGCCCCCCCCGCTGGCGGGGGTTTTTTTGCTATCCACCATTTTGGCGGCGCACCACTCTTGAGGGTGGTGCA
+
>6,6/:@;;>BEFAGGGGE7FGDCD?E=CD#######################################################################
//DECOY read
@DECOY.1.156433.100M.MD:Z:35T64/1
AGACAAGGCAAATTAAAGGTTTAGTAAGCTAAGTGTTCATGAACACATGACAAAAACGTGCCTGCTACTATTGTTGGGTGGCATTCTATAAATGAAATTAA
+
HHHHHHHHHHHEDHHGHFHCHHFHHGHHHHFCFFHHHFHFHHHHFHHHHHHFHHHGHEFGHD@HCEGG@FFFHEDGFG<EGEEFG=GEEFEGGG=G@GEFF

20. Sample Job - Encrypted
@BF0C691315C8761672AEBD1F2A42ED43B4D0F9197BD3209B6CC13B27711CC946B21C6DAE1A008F75508C290B1C324EDB/1
TTGCTAAATATGCTGAAATATTCGGATTGACCTCTGCGGAAGCCAGTAAGGATATACGGCAGGGATTGAAGAGTTTCGCCGGGGAGGGAG
GGGGTTTTTAT
+
GGFFGGGFGGGGFEFFE?GGGGDFGGGGGGGBGGBFGGGGBFEEFGA?GG8DD=DFGGGFFFB##################################
####
@C480AC6C6D59F77BB873186F1A5E524039D3FFE6567A40559D9434D888FAF7239FF2ECEFD07C79B2762E777D2A074BB3/1
GCCCTCACCGACTGCCATTGTCCCTAATGCACCGTAACGGGTGTGGCTGTCTGAGCCGAGGCATATTTTTGCGCCGCCTGGCATTATCTC
CAGCACATATT
+
F@DCFB@ABBDB=CD>BDC8@4@@?<EFFDFFFBDEEAEEEEE=EDDBDA###################################################
@A78878C3BE292C0FE0F3E64D2AE9FB2640FFC6D006BC15CF107EA587DD6F0E0395E7F3ECA36A7A867C0DA19D16585146/1
GAAGAGAGCTTTATGAGTCTCATGGCTAAATCTACACTGATGAGGGCAGTGACCCGGAGGCTGGTTTATTAGTATGAAAAAGTACGTCCAC
TGATAAAACT
+
FEE=FF@EE8CDDCC>@@DD299@;+>:@<19<@>E;EEE2,@:=EEE=-7,7<:ADA@9B4B46<AA#################################
@FEAB1E450AF92466520964FD2B39E052AE07D3ECCE6C92460399749F597405B2FEB75F602573E255148F745AE88145BF/1
GTTCAGGGTGAGTCGAATGATCCCTTGCCCGCATTCAGCGGAACTGTTGAATATGGGCAAATTCAGGGAACAATAGACAACTTTCAGGAAC
TCAATGTGCA
+
HHHHFHHHDFE@FFFBGGEBCGEGGFGHHFHGHGCGGHGHGHGGHCC>=FDC?CDBEEBE+>A;5@AB;?0<<0@@C@ABEEE/.@:>::.7>>>
@:6?:A
Public key encryption + base64 encoding

21. Future directions
 Complete decoupling of read mapping as
proof-of-work
 Docker-based plugins to change the “work”
 Miners -> employees
 Authority servers -> employers
 Root authority -> central bank
 Web-based GUI for “job descriptions”
 A job bulletin board for different employers

22. Conclusions
 HTS data is monotonically increasing
 Computational analysis is the bottleneck
 Additional burden due to reference updates
 But (fortunately) embarrassingly parallel problem
 Voluntary grids may help
 “Market will decide”
 Coins give motivation to miners since alignment
is compute intensive
 Decentralized transaction with centralized
mining

23. Resources
 Coinami web page (created as part of senior
project)
 https://coinami.github.io/
 GitHub page (code not public yet)
 https://github.com/coinami

24. Acknowledgements
Bilkent
Atalay Mert İleri (now at MIT)
Halil İbrahim Özercan (now senior student)
Alper Gündoğdu (now at Facebook)
Ahmet Kerim Şenol (now at Google)
M. Yusuf Özkaya (now at Georgia Tech)
Travel fellowship to Halil I. Özercan

25. Minin’, minin’, minin’
Though the reads are mappin’
Keep them coins signing’
Rawhide!