1. Analysis of SAGE Data: An Introduction Kevin R. Coombes Section of Bioinformatics

2. Outline Description of SAGE method Preliminary bioinformatics issues Description of analysis methods introduced in early paper Review of literature: statistics and SAGE

3. What is SAGE? Serial Analysis of Gene Expression Method to quantify gene expression levels in samples of cells Open system –Can potentially reveal expression levels of all genes: “unbiased” and “comprehensive” –Microarrays are closed, since they only tell you about the genes spotted on the array Ref: Velculescu et al., Science 1995; 270:484-487

4. How does SAGE work? 1. Isolate mRNA. 2.(b) Synthesize ds cDNA. 2.(a) Add biotin-labeled dT primer: 4.(a) Divide into two pools and add linker sequences: 4.(b) Ligate. 3.(c) Discard loose fragments. 3.(a) Bind to streptavidin-coated beads. 3.(b) Cleave with “anchoring enzyme”. 5. Cleave with “tagging enzyme”. 6. Combine pools and ligate. 7. Amplify ditags, then cleave with anchoring enzyme. 8. Ligate ditags. 9. Sequence and record the tags and frequencies.

5. From ditags to counts Locate the punctuation “CATG” Extract ditags of length 20-26 between the punctuation Discard duplicate ditags (including in reverse direction) -- probably PCR artifacts Take extreme 10 bases as the two tags, reversing right-hand tag Discard linker sequences Count occurrences of each tag SAGE software available at http://www.sagenet.org

6. What does the data look like?

7. From tags to genes Collect sequence records from GenBank that are represented in UniGene Assign sequence orientation (by finding poly- A tail or poly-A signal or from annotations) Extract 10-bases 3’-adjacent to 3’-most CATG Assign UniGene identifier to each sequence with a SAGE tag Record (for each tag-gene pair) –#sequences with this tag –#sequences in gene cluster with this tag Maps available at http://www.ncbi.nlm.nih.gov/SAGE

8. From tags to genes Ideal situation: –one gene = one tag True situation –one gene = many tags (alternative splicing; alternative polyadenylation) –one tag = many genes (conserved 3’ regions)

9. Sequencing Errors Estimated sequencing error rate: –0.7% per base (range 0.2% - 1%) Affect –ditags in a SAGE experiment can improve by using phred scores and discarding ambiguous sequences –tag-gene mappings from GenBank RNA better than EST

10. Reliable tag-gene assignments

11. SAGE and cancer Ten SAGE libraries, two each from –normal colon –colon tumors –colon cancer cell lines –pancreatic tumors –pancreatic cell lines Pooled each pair Ref: Zhang et al., Science 1997; 276:1268-1272

12. Variability in SAGE libraries

13. Distribution of tags 303,706 total tags 48,471 distinct tags Distribution –85.9% seen up to 5 times (25% of mass) –12.7% between 5 and 50 times (30%) –0.1% between 50 and 500 times (26%) –0.1% more than 500 times (19%) Ref: Zhang et al., Science 1997; 276:1268-1272

14. How many tags were missed? They simulated to find 92% chance of detecting tags at 3 copies/cell Using binomial approximation –Get 95% chance for 3 copies/cell –Only get 63% chance for 1 copy/cell Most of what they saw occurred at 1-5 copies per cell

15. Differential Expression Found 289 tags differentially expressed between normal colon and colon cancer (181 decreased; 108 increased) Method: Monte Carlo simulation. –100000 sims per transcript for relative likelihood of seeing observed difference –Used observed distribution of transcripts to simulate 40 experiments. Ref: Zhang et al., Science 1997; 276:1268-1272

16. Sensitivity Claim: 95% chance of detecting 6-fold difference Method: Monte Carlo –200 simulations, assuming abundance of 0.0001 in first sample and 0.0006 in second sample Ref: Zhang et al., Science 1997; 276:1268-1272

17. Weaknesses in Analysis Failed to account for intrinsic variability in samples (which changes depending on abundance) in assessing significance Monte Carlo used observed distribution, which is definitely not true distribution. Sensitivity only measured at one abundance level.

18. Alternative Analytic Methods Audic and Claverie, Genome Res 1997; 7:986-995 Chen et al., J Exp Med 1998; 9:1657-1668 Kal et al., Mol Biol of Cell 1999; 10:1859-1872 Michiels et al., Physiol Genomics 1999; 1:83- 91 Stollberg et al., Genome Res 2000; 10:1241- 1248 Man et al., Bioinformatics 2000; 16:953-959

19. Audic and Claverie Main goal: confidence limits for differential expression Use Poisson approximation for number of times x you see the same tag. Put a uniform prior on the Poisson parameter; get posterior probability of see tag y times in new experiment p( y | x ) = ( x + y )! / [ x ! y ! 2^( x + y +1)] Generalizes to unequal sample sizes

20. Chen et al. Assume –equal sample sizes –tag has concentration X, Y in two samples Look at W = X/(X+Y) Use a symmetric Beta prior distribution with a peak near 0.5 (since most genes don’t change) Use Bayes theorem to compute posterior probability of threefold difference in expression

21. Unequal sample sizes This analysis generalizes easily to the case of unequal size SAGE libraries –Lal et al., Cancer Res 1999; 59:5403-5407 This method is used at the NCBI SAGEmap web site for online differential expression queries –http://www.ncbi.nlm.nih.gov/SAGE

22. Kal et al. Assume the proportion of times you see a tag has binomial distribution Replace with a normal approximation to compute confidence limits Used at http://www.cmbi.kun.nl/usage Equivalent to chi-square test on 2x2 table:

23. Michiels et al. First perform overall chi-square test to decide if the two SAGE libraries being compared are different. Get significance by Monte Carlo simulation Perform gene-by-gene chi-square tests and use them to rank genes in order of “most likely to be different”

24. Stollberg et al. Assume binomial distributions Model the binomial parameters as a sum of two exponentials –fit to the Zhang step function data Simulate from this model, adding –sequencing errors –nonuniqueness of tags –nonrandomness of DNA sequences

25. Stollberg et al. Key finding: –Naively using observed data to fit model parameters cannot recover the observed data by simulation –Maximum likelihood estimate of parameters that recover the observed data give very different looking parameters

26. Stollberg et al.

27. Man et al. Compares specificity and sensitivity of different tests for differential expression –Audic and Claverie –Kal –Fisher’s exact test Monte Carlo simulation of experiments Findings –Similar power at high abundance –Kal has highest power at low abundance

28. Questions Sample size computations: –How many tags should we sequence if we want to see tags of a given frequency? –How many tags should we sequence if we want to see a given percentage of tags? How many tags are expressed in a sample? Best method for identifying differential expression?

29. Additional SAGE references Review –Madden et al., Drug Disc Today 2000; 5:415-425 Online Tools –Lash et al., Genome Res 2000; 10:1051-1060 –van Kampen et al., Bioinformatics 2000; 16:899-905 Comparison of SAGE and Affymetrix –Ishii et al., Genomics 2000; 68:136-143 Combine SAGE and custom microarrays –Nacht et al., Cancer Res 1999; 59:5464-5470 Mapping SAGE data onto genome –Caron et al., Science 2001; 291:1289-1292 Data mining the public SAGE libraries –Argani et al., Cancer Res 2001; 61:4320-4324