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A Content-Addressable DNA Database with Learned
Sequence EncodingsKendall Stewart1, Yuan-Jyue Chen2, David Ward1, Xiaomeng Liu1,
Georg Seelig1, Karin Strauss2, and Luis Ceze1
1 University of Washington 2 Microsoft Research
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Search by Image
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Bus
Traditional Storage
CPU
Database
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Bus
Traditional Storage
CPU
Database
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Bus
Traditional Storage
CPU
Database
❌
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Bus
Traditional Storage
CPU
Database
❌
Von-NeumannBottleneck
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DNA Data Storage
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DNA Data Storage
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DNA Data Storage
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DNA Data Storage
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Input File
DNA Data Storage
Sequences
Full Data Encoding
ATCGA…TCGAT…GATAC{ {{ PrimerPrimer Payload
ATCGA…GATCT…GATACATCGA…TGACA…GATACATCGA…GTGTA…GATAC
Organick et al., Nat. Bio. 2018
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Input File Sequences
DNA Data Storage
Full Data Encoding
ATCGA…TCGAT…GATAC{ {{ PrimerPrimer Payload
ATCGA…GATCT…GATACATCGA…TGACA…GATACATCGA…GTGTA…GATAC
Can we probe the payloads?
Organick et al., Nat. Bio. 2018
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Input File Sequences
DNA Data Storage
Full Data Encoding
ATCGA…TCGAT…GATAC{ {{ PrimerPrimer Payload
ATCGA…GATCT…GATACATCGA…TGACA…GATACATCGA…GTGTA…GATAC
Robust encoding requires randomization,
redundancy, ECC, segmentation…
Can we probe the payloads?
Organick et al., Nat. Bio. 2018
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Input File
Content-Based Encoding
Content-Addressable Storage
ATCGA…GGACGGAATAC{ {Content-Based
Probe RegionFile ID
(Payload)
Metadata Sequence
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Input File
Feature Extraction [0.1, 0.2, -0.3, 0.8, …]
Feature Vector
Content-Addressable Storage
Sequence Encoding ATCGA…GGACGGAATAC
{ {Content-BasedProbe Region
File ID (Payload)
Metadata Sequence
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Input File
Feature Extraction [0.1, 0.2, -0.3, 0.8, …]
Feature Vector
Content-Addressable Storage
Sequence Encoding ATCGA…GGACGGAATAC
{ {Content-BasedProbe Region
File ID (Payload)
Metadata Sequence
Focus of this talk
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Input File
Feature Extraction [0.1, 0.2, -0.3, 0.8, …]
Feature Vector
Content-Addressable Storage
Sequence Encoding ATCGA…GGACGGAATAC
{ {Content-BasedProbe Region
File ID (Payload)
Metadata Sequence
“Address” in Feature Space
Focus of this talk
18Feature Space (2D Projection)
Feature Space
19Feature Space (2D Projection)
Feature Space
20Feature Space (2D Projection)
Feature Space
21Feature Space (2D Projection)
Feature Space
22Feature Space (2D Projection)
Vector Quantization
23Feature Space (2D Projection)
ATCGA…
GATCG…
TGTAT…GCTAT…
Vector Quantization
Reif & LaBean,DNA 6 (2000)
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Vector Quantization
GCTAT
GCTAT
GCTAT
GCTAT
CGATA
CGAGA
ATCGACGAGA
TGTATGATCG
25Feature Space (2D Projection)
Vector Quantization
Neighbors in different clusters
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Similarity Preserving Encoding
GCTTT
GTTATGCTAA
CCTAT
CGATA
CGCGA
ATCGA
CGAGATGTAT
GATCG
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Similarity Preserving Encoding
GCTTT
GTTATGCTAA
CCTAT
CGATA
CGCGA
ATCGA
CGAGATGTAT
GATCG
Naive Encoding:[0.1, 0.7, 0.3, 0.8, …]
[0.00, 0.25) = A [0.25, 0.50) = T [0.50, 0.75) = C [0.75, 1.00] = G
ACTG?
Semantic Hashing
Adapted from Salakhutdinov et al. 2007
Images
Binary Addresses
Similar inFeature Space
(Euclidean)
Similar in Address Space
(Hamming)
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Semantic Hashing
Images
DNA Sequences
Similar inFeature Space
(Euclidean)
Similar in Sequence Space
(Hybridization Yield)
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Semantic Hashing
Images
DNA Sequences
Similar inFeature Space
(Euclidean)
Similar in Sequence Space
(Hybridization Yield)
Training a neural network efficiently requires
differentiable operations
NUPACK calculation of hybridization yield is not
differentiable!
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Approximating Yield
AGTC
AGAC
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Approximating Yield
AGTC
AGACA T C G
A T C G
0 1 2 3
0 1 2 3
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Approximating Yield
AGTC
AGACA T C G
A T C G
0 1 2 3
0 1 2 3
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Approximating Yield
AGTC
AGACA T C G
A T C G
0 1 2 3
0 1 2 3
1� u · v||u|| ||v||
cosine distance(u,v) =
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Approximating Yield
AGTC
AGACA T C G
A T C G
0 1 2 3
0 1 2 3
Mean Cosine Distance = (0 + 0 + 1 + 0) / 4 = 0.25
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Approximating Yield
AGTC
AGACA T C G
A T C G
0 1 2 3
0 1 2 3
Mean Cosine Distance = (0 + 0 + 1 + 0) / 4 = 0.25
Equal to Hamming Distance
when representations are “one-hot”
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Approximating Yield
AGTC
AGACA T C G
A T C G
0 1 2 3
0 1 2 3
Mean Cosine Distance = (0 + 0 + 1 + 0) / 4 = 0.25
Equal to Hamming Distance
when representations are “one-hot”
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Approximating Yield
A T C G
0 1 2 3
…
302927 28
AGTC CAGC…
Neural network outputs are not exactly one-hot
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Approximating Yield
A T C G
0 1 2 3
…
302927 28
AGTC CAGC…
But the approximation is good enough!
Random Pairs of Neural Net Outputs
Mean CosineDistance: 0.29
Neural NetOutput
ATCG
A T G C C T A C G G C T HammingDistance: 0.33
Mean CosineDistance: 0.33
Sequence
One-HotEncoding
ATCG
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Closing the LoopA T C G
0 1 2 3
…
302927 28
AGTC CAGC…
A T C G
0 1 2 3
…
302927 28
GTAC GGTA…
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Closing the LoopA T C G
0 1 2 3
…
302927 28
AGTC CAGC…
A T C G
0 1 2 3
…
302927 28
GTAC GGTA…
Mean Cosine Distance
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Closing the LoopA T C G
0 1 2 3
…
302927 28
AGTC CAGC…
A T C G
0 1 2 3
…
302927 28
GTAC GGTA…
Mean Cosine Distance
Approximate Yield
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Closing the LoopA T C G
0 1 2 3
…
302927 28
AGTC CAGC…
A T C G
0 1 2 3
…
302927 28
GTAC GGTA…
Mean Cosine Distance
Approximate Yield
Images Similar? (Distance < 0.2)
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Closing the LoopA T C G
0 1 2 3
…
302927 28
AGTC CAGC…
A T C G
0 1 2 3
…
302927 28
GTAC GGTA…
Mean Cosine Distance
Approximate Yield
Images Similar? (Distance < 0.2)
Cross-Entropy Loss
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Closing the LoopA T C G
0 1 2 3
…
302927 28
AGTC CAGC…
A T C G
0 1 2 3
…
302927 28
GTAC GGTA…
Mean Cosine Distance
Approximate Yield
Images Similar? (Distance < 0.2)
Cross-Entropy Loss
Gradients
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Database Strand Design
FP d(T) IP f(T) RP18 nt 18 nt19 nt 30 nt5 nt
Image 209
Allows Sequencing
Allows Sequencing
Allows Protection
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Database Strand Design
FP d(T) IP f(T) RP18 nt 18 nt19 nt 30 nt5 nt
Allows Protection
Double-stranded region prevents interference
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Query Strand Design
FP d(T) IP f(T) RP18 nt 18 nt19 nt 30 nt5 nt
f(Q)* RP[:6]* B30 nt 6 nt
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Query Strand Design
FP d(T) IP f(T) RP
f(Q)* RP[:6]* B
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Dataset ConstructionTargetsQueries TargetsQueries
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Queries TargetsQueries
Dataset Construction
FP d(T) IP f(T) RP
For each T out of 100 target images
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TargetsQueries
Dataset Construction
FP d(T) IP f(T) RPf(Q)* RP[:6]* BFor each Q out of 10 query images
TargetsQueries
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Experimental Results
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Ideal Data
Experimental Results
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Chance Retrieval
Experimental Results
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Query Image:
Experimental Results
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All Queries
Experimental Results
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All Queries
Experimental Results
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Query Image:
Experimental Results
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Query Image: Query Image:
Experimental Results
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Experimental Results
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R2: -0.30 R2: 0.64
Experimental Results
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R2: -0.30 R2: 0.64
Thank you!
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