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Promoter prediction assessment

ENCODE Workshop 2005 at Sanger Institute. Promoter prediction assessment. by Vladimir B Bajic. Predictors. ENCODE participants (3): 7-80-8 (McPromoter1) 7-81-8 (McPromoter2) 41-108-8 (Fprom) additional predictors Beyond ENCODE participants (4) ( out of competition )

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Promoter prediction assessment

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  1. ENCODE Workshop 2005 at Sanger Institute Promoter prediction assessment by Vladimir B Bajic

  2. Predictors • ENCODE participants (3): • 7-80-8 (McPromoter1) • 7-81-8 (McPromoter2) • 41-108-8 (Fprom) additional predictors • Beyond ENCODE participants (4) (out of competition) • DBTSS (reference experimental dataset of capped flcDNA) • FirstEF • Dragon Gene Start Finder • Dragon Promoter Finder

  3. Goals • How good are promoter predictors? • Does performance change on this dataset? • Implications for future developments

  4. Data (1) Category “Known genes with CDS” (category = 2) • 1061 annotated transcripts • 1009 -> 994 unique starts of transcripts (TSSs) • 319 unique TSSs in Encode ‘training’ set (13 regions) • 675 unique TSSs in Encode test set • Length of ENCODE regions 29,998,060 bp • Length of ‘training’ regions 8,538,447 bp • Length of testing regions 21,459,613 bp

  5. Data (2)

  6. Method for counting TP and FP All hits to ‘orange’ count as FPs Only one hit within A, B, or C counts as TP for unique position of TSS (3 hits within C will count only as 1 TP) Only minimum distance from all TSSs counts

  7. Results • Different measures of success • Test ENCODE regions • Also: comparison with other participants (test + all regions)

  8. Se, ppv, AE (average positional error)

  9. DIP1, DIP2, CC, ASM

  10. Comments • Compared to previous whole human genome analysis, now we use a more strict distance constraint: max allowed distance 1000 nt (vs. previous 2000 nt) • Previously: Se [0.4 – 0.8], ppv [0.25 – 0.67] • Now, for experimental DBTSS data: • Positional error ~100 nt, Se 0.61, ppv 0.93 • Computational promoter prediction (CPP) (using single genome, no transcripts): positional error 200-300 nt (2-3 fold larger than DBTSS) (positive surprise) • Se [0.32-0.62] (negative surprise but expected) • (reason poor G+C content of some of the test regions) • CPP: ppv >80 (in some cases >90%) (positive surprise) • Having in mind the type of information used for ab initio promoter finding, we see no dramatic difference in 5’ end prediction by methods class 1 and 3, and CPP (positive surprise); however, Se and ppv are better with methods of class 1 and class 3 for obvious reasons.

  11. Future developments • Combine TSS predictors and gene finding programs or transcript info (positive effects of this are visible in Fprom, 20-76-4 and 20-76-5, since in these cases the TSS search space is effectively restricted) • This, however, requires retuning of TSS predictors and some change in their design philosophy • Expected performance should be similar or better than in class 1 and class 3 systems as TSS finding systems should be more specialized for the 5’end type of signals • More emphasis should be given to positional accuracy of TSS predictors

  12. Thank you for your time You may wake up now

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