UC Riverside Talk

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Engineering a Scalable
Placement Heuristic for DNA Arrays
A.B. Kahng (UCSD) P. Pevzner (UCSD) S. Reda
(UCSD) A. Zelikovsky (GSU)
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Outline

• DNA probe arrays and unwanted illumination
• Synchronous array design (2-D placement)
• Asynchronous array design (3-D placement)
• Experimental results
• Extensions
• Conclusions

3
DNA Arrays

• DNA arrays
• Short DNA probes bound to a glass substrate
• Detect matching single-strand DNA molecules

• Growing number of applications
• Diagnosis of genetically based conditions
• Point of care diagnosis (low-cost, real-time)
• Targeted treatment
• E.g., antibiotic sensitivity
• Drug discovery
• Sequencing, genotyping, gene expression
  monitoring
• Agricultural research, environmental impact,
  bio-warfare agents,

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Scaling Trends and Challenges

• Smaller is better for DNA arrays
• Reduced reagent consumption
• Higher reaction speed
• Higher parallelism

• but brings challenging system complexity new
  dominant physical effects
• ½ million probes / array ? 100 million probes /
  next generation array
• Unwanted illumination caused by light diffraction
• ? Emerging DNA array design automation field
• Need scalable design tools, mature methodologies
• Great potential for transfer of techniques and
  methodologies from VLSI design automation

5
Array Manufacturing Process

• Very Large-Scale Immobilized Polymer Synthesis
• Treat substrate with chemically
  protected linker molecules,
  creating rectangular array
• Site size approx. 10x10 microns
• Selectively expose array sites to light
• Light deprotects exposed molecules,
  activating further synthesis
• Flush chip surface with solution of
  protected A,C,G,T
• Binding occurs at previously
  deprotected sites
• Repeat steps 23 until desired
  probes are synthesized

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Unwanted Illumination Effect

• Unwanted illumination ? erroneous probes
• Effect gets worse with technology scaling

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Example Probe Synthesis
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Example Probe Synthesis
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Example Probe Synthesis
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Measure of Unwanted Illumination
Unwanted illumination ? border length
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Synchronous Synthesis

• Periodic deposition sequence, e.g., (ACTG)k

? border conflicts b/w adjacent probes 2 x
Hamming distance
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2D Placement Problem
Edge cost 2 x Hamming distance
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Previous Approaches

• Hubbell 90s
• Find TSP w.r.t. Hamming dist
• Thread TSP to grid row by row

• TSP-based methods do not scale to gt 106 probes
• ? Transfer scalable techniques from VLSI
  placement!

14
2D Placement Sliding-Window Matching

• Slide window over entire chip
• Repeat until improvement drops below certain
  threshold

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Effect of Window Size
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2D Placement Epitaxial Growth

• Simulates crystal growth

• O(N3/2) row-order implementation, where N
  probes

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Asynchronous Synthesis

• Probes grow at different speeds
• border conflicts b/w adjacent probes depends on
  their embedding into the nucleotide deposition
  sequence

? 3D placement problem
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Single-Probe Embedding

• Dynamic programming algorithm similar to LCS

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Post-Placement Embedding Optimization

• 2D placement fixed, allow only probe embeddings
  to change

• Greedy optimally re-embed probe with largest
  gain
• Chessboard Algorithm alternate re-embedding of
  red and green probes

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Embedding Optimization Results

• Chessboard is 5-6 better than greedy
• Within 21 of lower-bound

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Comparison of Placement Algorithms
Chip size 100x100 to 500x500

• SWM 600x faster (5 min. vs. 30 hours) with up to
  4 border conflict decrease
• 20 Row-Epitaxial 6-10 better than
  TSPThreading, gt10x faster for 500x500

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Practical Extensions

• Distant-dependent border conflict weights
• Take into account conflicts between 2-,3-hop
  neighbors rather than only immediate neighbors
• Position-dependent border conflict weights
• In alignment DP for two sequences take into
  account importance of conflicts in the middle of
  probes alignment cost has weights on conflicts
  which depend on conflict position
• Perfect match/mismatch probes
• Pairs of probes that differ only in middle
  position
• Should be placed and aligned together

23
Alignment DP for 2-SNPs
Optimal Embedding of AC,TT
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Summary

• Contributions
• Epitaxial placement ? reduces by extra 10 over
  the previously best known method
• Asynchronous placement problem formulation
• Postplacement improvement by extra 15.5-21.8
• Lower bounds
• Scalable Placements (1000x1000 in 20min)
• Ongoing work
• Comparison on industrial benchmarks
• Experiments with algorithms for extended
  formulations (SNPs, distance-dependent weights,
  etc.)

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Summary

• Results demonstrate effectiveness of VLSI
  placement techniques to DNA probe placement
• Currently exploring other VLSI placement
  techniques, e.g., recursive 4-way partition based
  on linear-time clustering methods

• Algorithms validated on industry data
• Extended to handle practical constraints such as
  control probes, match/mismatch probe pairs
• 5 border length improvement over industry
  placements
• ? Improved design results in fewer erroneous
  probes, smaller array area, and/or more probes
  per array

26
Simplified DNA Array Flow
Probe Selection
Probe Placement
Probe Alignment (Mask Design)
Mask Manufacturing

Array Manufacturing
Soft/Computational Domain
Hybridization Experiment

Analysis of Hybridization Intensities
Hard/Biochemistry Domain
Gene sequences, position of SNPs, etc.