1. Microarray Synthesis through Multiple-Use PCR Primer Design
Research Proficiency Examination
Rohan Fernandes
Frequently, presenters must deliver material of a technical nature to an audience unfamiliar with the topic or vocabulary. The material may be complex or heavy with detail. To present technical material effectively, use the following guidelines from Dale Carnegie Training®.
Consider the amount of time available and prepare to organize your material. Narrow your topic. Divide your presentation into clear segments. Follow a logical progression. Maintain your focus throughout. Close the presentation with a summary, repetition of the key steps, or a logical conclusion.
Keep your audience in mind at all times. For example, be sure data is clear and information is relevant. Keep the level of detail and vocabulary appropriate for the audience. Use visuals to support key points or steps. Keep alert to the needs of your listeners, and you will have a more receptive audience.
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2. Biology Background:PCR
PCR animation (From the Dolan DNA Learning Center, CSHL)
Applications of PCR include
Genetic Fingerprinting.
Medical Diagnostics.
DNA Sequencing.
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3. What are Microarrays?
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4. What are Microarrays?
A grid with different DNA probes in each location.
Allows one to test a given sample for expression of multiple genes.
Can compare gene expression by using different colored fluorescent markers in two samples.
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5. Genomic Data Sequences are known for more than 800 organisms!
100 free-living species have been sequenced already.
But we know very little about most of these organisms’ biology.
Exploiting full-genome sequence data, requires investigators to have inexpensive custom microarrays.
If you have several points, steps, or key ideas use multiple slides. Determine if your audience is to understand a new idea, learn a process, or receive greater depth to a familiar concept. Back up each point with adequate explanation. As appropriate, supplement your presentation with technical support data in hard copy or on disc, , or the Internet. Develop each point adequately to communicate with your audience.
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6. Why Microarrays?
Microarray technology has revolutionized our understanding of gene expression.
Applications include
Cell cycle analysis.
Response of cells to environmental stress.
Impact of gene knockouts.
In your opening, establish the relevancy of the topic to the audience. Give a brief preview of the presentation and establish value for the listeners. Take into account your audience’s interest and expertise in the topic when choosing your vocabulary, examples, and illustrations. Focus on the importance of the topic to your audience, and you will have more attentive listeners.
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7. A Primer Design True Story!!
Project for Futcher and Leatherwood to design PCR primers for microarray synthesis.
Strict criteria for primer length, melting temperature, self-similarity were specified.
Designed primers for 5827 and 5012 genes for Cerevisiae and Pombe.
PCR done with sample set of primers designed for 96 genes each of S. Pombe and S. Cerevisiae was 100% successful.
Determine the best close for your audience and your presentation. Close with a summary; offer options; recommend a strategy; suggest a plan; set a goal. Keep your focus throughout your presentation, and you will more likely achieve your purpose.
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8. The 110,000 Dollar Problem
Good primer design can be crucial in synthesizing microarray DNA.
$110,000 out of a total budget of $220,000 for microarray synthesis was spent on PCR primers alone.
We propose an alternative method of PCR primer design to reduce costs.
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9. Efficiency of PCR
Usually, PCR primers are designed to occurs uniquely on the genome.
However, efficiency of PCR falls exponentially as length of product increases.
PCR becomes ineffective for product sizes beyond 1200 bases.
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10. Exploiting PCR Efficiency Drop-off
Amplification is significant only if primers hybridize near each other.
We can reuse primers to amplify several genes, provided each primer pair is unique.
We can save thousands of primers through reuse!
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11. Who can benefit?
The total cost of PCR primers may dissuade investigators of less studied organisms from using microarrays.
Our technique can reduce costs enough to make microarrays more attractive to less funded researchers.
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12. What is the potential win?
Let (n,m) be the (number of genes, minimum number of primers required to amplify them).
m primers can result in m(m+1)/2 unique primer pairs.
2n primers may be sufficient instead of 2n.
Conventional primer design requires 12,000 primers for 6,000 genes, but 110 might suffice.
In practice this lower bound will be unreachable but there will still be a large win.
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13. Potential Win? (Example)
Consider the cost of building a spotted microarray for a 20,000 gene organism.
Conventional techniques will require us to use 40,000 primers.
Cost : $160,000 at $4 a primer.
If 3,000 primers suffice, cost is only $12,000.
The best case is overoptimistic, but realistic wins are still impressive.
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14. Cost of Split Addressing
What is the probability that two random strings will occur in a long random string in a certain order and with no more than a certain gap?
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15. Split Addressing (Contd)
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16. Split Addressing – Conclusion
Total length of primers required to ensure uniqueness of hybridization increases only very slowly with the length of the genome.
The penalty for genome scale lengths and realistic PCR gap lengths amount to only additional 3-4 bases of primer over ungapped matching.
These results support the potential of multiple-use primers.
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17. Minimum Primer Set Problem
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18. Budgeted Primer Set Problem
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19. Hardness of problems
The Minimum Primer Set problem is NP-hard and hard to approximate to within a logarithmic factor.
The Budgeted Primer Set problem is NP-hard and seems to be related to densest k-subgraph problem.
Approximation bounds for densest k- subgraph problem are not encouraging.
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20. Reduction Gadget
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21. Reduction from Set Cover to Minimum Primer Set
(S, X) is a set cover instance.
S U, X W. Connect vertex in U to vertex in W iff corresponding set in S contains element from X.
Label (color) each edge by the name of the element vertex at its end.
MPS solution will include all element vertices and minimum number of set vertices which cover all sets. Q.E.D.
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22. A Heuristic to approximate MPS
Based on greedy heuristic to find densest subgraph.
Each edge is weighted with the value of (1/number of edges bearing that color).
Vertex weight is set to sum of adjoining edge weights.
Algorithm proceeds by removal of vertex with minimum weighted vertex without eliminating any color.
Algorithm terminates when no more vertices can be eliminated.
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23. Example Run of Algorithm (1)
Initially graph with vertex weights.
Color
Edges
Weight
Blue 2
1/2
Green 1
1/1
Red 3
1/3
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24. Example Run of Algorithm (2)
After removing minimum weighted vertex.
Color
Edges
Weight
Blue 1
1/1
Green
Red 3
1/3
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25. Example Run of Algorithm (3)
Final graph.
Color
Edges
Weight
Blue 1
1/1
Green
Red
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26. Performance of Heuristic
O(|V|.(|V|+|E|+|C|)) time and O(|V|+|E|+|C|) space.
This heuristic is too slow. It is quadratic in |V| hence very slow on large data sets.
For our largest dataset this heuristic produced a solution in two days as opposed to 25 minutes for the next heuristic.
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27. A Linear-time Heuristic
We select an edge of each color that has maximum colored adjacency to form our seed graph.
We switch an edge for a color if that saves us any vertices in the seed graph
If there are no savings but no additional vertices we switch edges with p=1/2.
Repeat above steps until no. of vertices is constant.
Eliminate all colors whose edges are not isolated.
Repeat above steps for remaining graph until no. of vertices is constant. Merge graph obtained.
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28. Selecting Seed Edges
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29. Replacing Seed Edges
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30. Retrying with Isolated Colored Edges
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31. Preparation of Experimental Data Sets
Candidate primer sets for S. Cerevisiae and S. Pombe prepared using Primer3.
Primer length range 8-12 bases.
PCR product size range from bases.
For each gene at most 10,000 pairs of primers were selected.
Three melting temperature ranges for each of S. Cerevisiae and S. Pombe were selected.
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32. Degenerate Data Sets
A degenerate primer is a mix of two or more primers usually differing in a small number of bases.
Degenerate primers can make resulting colored graph more dense by merging primers.
Created degenerate data sets by merging primers differing in at most one base.
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33. Summary of Results (Non-degenerate)
Yeast
T_m
Amplified Genes
Lower Bound
Cost (1)
Cost (2)
Savings (1)
Savings (2)
Cerevisiae
47-57
3775
3065
5483
5511
2067
2039
42-52
2700
1344
3130
3232
2270
2168
40-50
5313
1241
4753
5157
5863
5469
Pombe
45-55
3583
2622
4987
5058
2179
2108
43-53
4232
1988
4799
4951
3665
3513
3400
1380
3651
3852
3149
2948
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34. Summary of Results (Degenerate)
Yeast
T_m
Amplified Genes
Lower Bound
Cost (1)
Cost (2)
Savings(1)
Savings(2)
Cerevisiae
47-57
3775
1221
3638
3940
3912
3610
42-52
2700
475
2105
2481
3295
2919
Pombe
45-55
3583
1050
3283
3598
3883
3568
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35. Future Work Using longer primers would enable more efficient PCR.
Increasing order of degeneracy would give a more dense colored graph and potentially greater savings.
Combining the above two ideas is the focus of our current work.
Consider the use of existing software architecture to solve other primer design problems.
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36. Acknowledgements
Thanks to Steven Skiena, Bruce Futcher and Janet Leatherwood.
Sponsored by NSF Grant CCR
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