Accurate Method for Fast Design of Diagnostic Oligonucleotide Probe Sets for DNA Microarrays

1. Accurate Method for Fast Design of Diagnostic Oligonucleotide Probe Sets for DNA Microarrays Nazif Cihan Tas ctas@cs CMSC 838 Presentation

2. Motivation • DNA microarrays techniques are used intensely for identification of biological agents • Gene Expression Studies • Diagnostic Purposes • Identification of Microorganisms in samples • Item Extraction • Complex Problem • Find the necessary probes and the temperature • Probe sets should be reliably detect and differentiate target sequences • Large Databases • NEW!! Homologous Genes (how to find specific probes) CMSC 838T – Presentation

3. Talk Overview • Overview of talk • Motivation • Problem Statement • Algorithm • Mathematical Aspects • Experimentation • Discussion CMSC 838T – Presentation

4. Problem Statement • Positive Probes • Database set S0 • Target S1 • For each sequence in S1, find at least one probe • For S0 - S1 try to avoid it (but do not care if happens) • High Specificity: # of non-target matches are minimized • High Sensitivity: # of covered target seq. is maximized S0 S1 CMSC 838T – Presentation

5. Problem Statement • Negative Probes • Determine as few as possible probes which together hybridizes with all sequences in S0 - S1 but with NONE in S1. • High Specificity: No seq. in S1 may hybridize • High Sensitivity: Max # of seq. in S0 - S1 be covered S0 S1 CMSC 838T – Presentation

6. Probe Design Constraints • Sequence Related • Length of probes • Deviation of melting temperature of probe-target hybrids must be low (for physical reasons) • No self complementary regions longer than four nucleotides (not descriptive enough) • Melting temperatures of target and non-target seq. must be larger than a predefined (too close, too hard to identify) • Ensuring a minimum number of mismatches is enough (homologous sequences) • System Related • Execution Time • Usability CMSC 838T – Presentation

7. Algorithm • Overview • Probe Generation • Hybridization Prediction • Probe Selection CMSC 838T – Presentation

8. AlgorithmProbe Generation • Subproblem: • Generate probe candidates for the sequences • Keep the set as small as possible without losing any optimal candidate (exclude infeasible ones) • Suffix Tree • Why? • Allows fast recognition of repetitive subsequences • Identifies non-unique probes (i.e. with more than one target) • Efficient for memory and for T computation (reduce time) • How? • Tree is constructed from the sequences • Traversed (Watson-Crick complement) CMSC 838T – Presentation

9. Suffix Tree • Input: TACTACA • TACTACA • ACTACA • CTACA • TACA • ACA • CA • A • $ denotes end of string • Constructed in linear time CMSC 838T – Presentation

10. Probe Generation • Further Improvements • Filters applied for cut off • Probe length (predefined) • G-C content (for temperature) • Self-complementarity • Probes should not contain complements as subsequences • Finally, remove highly conserved (non-specific) regions • Insert into hashtables according to their lengths CMSC 838T – Presentation

11. Algorithm CMSC 838T – Presentation

12. AlgorithmHybridization Prediction • Subproblem: • Search for the right probe • Search is expensive, Intelligent Hashing used • Design • A frame is moved over target and nontarget seqs. with several lengths • Previous algorithm (Kaderali 2002): Use the suffix tree • At each step, hash values are calculated. If hit, predict melting temperature, store in hybridization matrix. • If there are too many hits for a probe, then it is not unique, remove it • Why intelligent? • Hash time is linear • Allows inexact matching because of hashing (No analysis) • Parallelization • Several threads are searching for probe targets. • Tree and hashtables are fixed. • One thread writes to the final matrix CMSC 838T – Presentation

13. Hybridization Prediction • Empirical Simulation: • One million random probe-target pairings generated • Four mismatches or one insertion or deletion plus one strong central mismatch chosen • T<20 C for 93% • Complexity is O ( |S0| |S1| ) • Possible probe candidates is |S1| (linear) • Each position of database S0 must be checked CMSC 838T – Presentation

14. Algorithm CMSC 838T – Presentation

15. AlgorithmProbe Selection • Subproblem: • Use the hybridization matrix to finalize the probe selection • We have positive probes and negative probes to proceed • Algorithm Analysis: • For each probe candidate • g: #of matches in S1 • b: #of matches in S0 - S1 • t: highest melting point in S1 • Probes for which g or b values is too large, are removed • Sort according to g,b and t. • Apply Depth First Search • Advantages • Performs well (No comparison though) • Guarantees to choose all specific probes if any were found. • Disadvantages • can NOT guarantee optimal selection in terms of coverage CMSC 838T – Presentation

16. Negative Probe Selection • Let S2 =S0 - S1 and B subset of S2 . The probes that detect S1 also detects some of B elements. • Algorithm for Negative Probes • Apply probe generating and preselection for B. • Conduct hybridization on B U S1 . • Remove the probes which hybridizes with S1 . • Sort the remaining probes according to their hit number. • Successively select the probes which covers most target seq. • Guarantees optimal solution for coverage and probe number usage CMSC 838T – Presentation

17. AlgorithmProbe Selection CMSC 838T – Presentation

18. Mathematical Aspects CMSC 838T – Presentation

19. Experimentation • Parallelized on SMP platform • Classic worker-producer • Intel Dual Pentium III 933 MHz, 1 GB memory • Test data • ssu rRNA of ARB project • 20.282 ssu rRNA sequences • 1.401 < lengths < 4.179 • %97 of them are shorter than 2.000 bases CMSC 838T – Presentation

20. Discussion • High Performance • Execution is linear with size of database, decreases if longer probes are used • Low Memory Consumption • Depends on the size of the sequence selection, NOT database size • Automatic Design of Group Probes and negative probes • High Quality Probe Design CMSC 838T – Presentation

21. Discussion • Comparison with previous work • vs. ARB • Not suited for large scale probe design • vs. LCF • Does not consider highly conserved data • Memory consumption is high • Works well with short probes only • vs. others • Mostly can not deal with insertion and deletions • Execution is slow CMSC 838T – Presentation