1. McPromoter – an ancient tool to predict transcription start sites
Uwe Ohler
Institute for Genome Sciences and Policy
Duke University
(BDGP/Univ Erlangen)

2. An extremely simplified view of eukaryotic transcription
Specific information about functional context of genes: proximal promoter/enhancers
Binding sites of specific transcription factors confer activation at the right developmental stage or tissue
General information: the core promoter
Region around the transcription start site (TSS) where RNA polymerase II (pol-II) interacts with general transcription factors
Potentially far away from the translation start site

3. Probabilistic modeling of promoters
Goal: find TSS / proximal promoters ab initio
Alternative to cDNA alignments
Independent of and in addition to gene prediction
Probabilistic modeling allows to deal with uncertainty
Models for classes of related sequences
Models represent our knowledge about sequences in form of parameters
Parameters are automatically estimated using a representative set of sequences
Model gives probability of sequence to belong to class, here: promoter or non-promoter (coding, non-coding)

4. McPromoter system structure

5. Non-promoter classes: Stationary Markov chains
Probability of a sequence
Approximation: Restrict context to the last N symbols (N-th order chain)
Markov chain as tree
Every node corresponds to a context
Contains probability distribution
Typical order: 6 (4,096 overall parameters)
Variations on Markov chains
Variable Order: Leaves on different levels
Interpolated: Combination of parameter values from different levels

6. Promoter model Simple approach: Markov chain model
Better: Take structure into account
Generalized hidden Markov model
Each state contains a submodel for a specific promoter part, including an explicit length distribution
Interpolated Markov chains as submodels
Ohler et al., Bioinformatics 1999, PSB 2000

7. Example: stat6 promoter

8. Evaluation of ENCODE regions
Similar problem to alternative splicing: alternative transcription start sites
Traditionally, the window to count false positives has been very large (e.g., -2,000/+2,000), and close predictions within a large window are merged
Evaluate on a per gene basis, i.e. count a true positive if it hits at least one of the annotated TSSs
Second problem: False negatives
After GASP, counting only those predictions internal to the annotated transcripts is the de facto standard
435 genes / 1,022 different TSSs
Another problem: Circularity? (use of Eponine)
Reese et al., Genome Res (2000)

9. Results in the ENCODE region
Standard paramters, NO repeat masking, merging predictions within 2,000 nt: 695 predictions
Positive region -2,000/+2,000: 204 TP / 197 genes (sn 47%); FP (sp 73%); unknown
More stringent: -500/ TP (sn 39%) 101 FP (sp 63%)
Does it make sense to move towards a more detailed evaluation?

10. Thanks to... Berkeley Drosophila Genome Project
Gerry Rubin
Martin Reese
Suzi Lewis
Erlangen – Institute for Computer Science
Heinrich Niemann
Stefan Harbeck
Georg Stemmer