1. CSCE555 Bioinformatics Lecture 11 Promoter Predication
Meeting: MW 4:00PM-5:15PM SWGN2A21
Instructor: Dr. Jianjun Hu Course page:
HAPPY CHINESE NEW YEAR
University of South Carolina
Department of Computer Science and Engineering
2008

2. Outline Introduction to DNA Motif Motif Representations (Recap)
Motif database search
Algorithms for motif discovery
4/21/2017

3. Search Space Size of search space = (L – W + 1)N
Motif width = W N
Length = L
Size of search space = (L – W + 1)N
L=100, W=15, N=10  size  1019

4. Worked Example 1 2 3 4 a c g t N = 4 pi = ¼ cki =c g t
N = 4
pi = ¼
cki =
Score = = 2.29

5. Gibbs Sampling Search 1 2
Suppose the search space is a 2D rectangle. (Typically, more than 2 dimensions!)
Randomly pick a dimension. X
Start at a random point X.
Look at all points along this dimension.
Move to one of them randomly, proportional to its score π.
Repeat.

6. Gibbs Sampling for Motif Search
Choose a random starting state.
Randomly pick a sequence.
Look at all motif positions in this sequence.
Pick one randomly proportional to exp(score).
Repeat.

7. Does it Work in Practice?
Only successful cases get published!
Seems more successful in microbes (bacteria & yeast) than in animals.
The search algorithm seems to work quite well, the problem is the scoring scheme: real motifs often don’t have higher scores than you would find in random sequences by chance. I.e. the needle looks like hay.
Attempts to deal with this:
Assume the motif is an inverted palindrome (they often are).
Only analyze sequence regions that are conserved in another species (e.g. human vs. mouse).
As usual, repetitive sequences cause problems.
More powerful algorithm: MEME

8. Go to our MEME server:
Fill in your adres, description of the sequences
Open the fasta formatted file you just saved with Genome2d (click “Browse”)
Select the number of motifs, number of sites and the optimum width of the motif
Click “Search given strand only”
Click “Start search”

9. Something like this will appear in your email
Something like this will appear in your . The results are quite self explanatory.

10. Promoter Prediction What are promoters?
Three strategies for promoter prediction
Signal based
Comparative genomics/phylogenetic footprinting
Expression profile base de-novo motif discovery algorthms

11. What is a Promoter?
Region of gene that binds RNA polymerase and transcription factors to initiate transcription

12. Promoters:What signals are there? Simple ones in prokaryotes
Promoter Prediction (really)
10/26/05
Promoters:What signals are there? Simple ones in prokaryotes
D Dobbs ISU - BCB 444/544X

13. Prokaryotic promoters
Promoter Prediction (really)
10/26/05
Prokaryotic promoters
RNA polymerase complex recognizes promoter sequences located very close to & on 5’ side (“upstream”) of initiation site
RNA polymerase complex binds directly to these. with no requirement for “transcription factors”
Prokaryotic promoter sequences are highly conserved
-10 region
-35 region
D Dobbs ISU - BCB 444/544X

14. Promoter Prediction (really)
10/26/05
What signals are there? Complex ones in eukaryotes
D Dobbs ISU - BCB 444/544X

15. Eukaryotic genes are transcribed by 3 different RNA polymerases
Promoter Prediction (really)
10/26/05
Eukaryotic genes are transcribed by 3 different RNA polymerases
Recognize different types of promoters & enhancers:
D Dobbs ISU - BCB 444/544X

16. Eukaryotic promoters & enhancers
Promoter Prediction (really)
10/26/05
Eukaryotic promoters & enhancers
Promoters located “relatively” close to initiation site
(but can be located within gene, rather than upstream!)
Enhancers also required for regulated transcription
(these control expression in specific cell types, developmental stages, in response to environment)
RNA polymerase complexes do not specifically recognize promoter sequences directly
Transcription factors bind first and serve as “landmarks” for recognition by RNA polymerase complexes
D Dobbs ISU - BCB 444/544X

17. Eukaryotic transcription factors
Promoter Prediction (really)
10/26/05
Eukaryotic transcription factors
Transcription factors (TFs) are DNA binding proteins that also interact with RNA polymerase complex to activate or repress transcription
TFs contain characteristic “DNA binding motifs”
TFs recognize specific short DNA sequence motifs “transcription factor binding sites”
Several databases for these, e.g. TRANSFAC
D Dobbs ISU - BCB 444/544X

18. Zinc finger-containing transcription factors
Promoter Prediction (really)
10/26/05
Zinc finger-containing transcription factors
Common in eukaryotic proteins
Estimated 1% of mammalian genes encode zinc-finger proteins
In C. elegans, there are 500!
Can be used as highly specific DNA binding modules
Potentially valuable tools for directed genome modification (esp. in plants) & human gene therapy
D Dobbs ISU - BCB 444/544X

19. Promoter Prediction (really)
10/26/05
Predicting Promoters
Overview of strategies
 What sequence signals can be used?
What other types of information can be used?
Algorithms
Promoter prediction software
3 major types
many, many programs
D Dobbs ISU - BCB 444/544X

20. Promoter prediction: Eukaryotes vs prokaryotes
Promoter Prediction (really)
10/26/05
Promoter prediction: Eukaryotes vs prokaryotes
Promoter prediction is easier in microbial genomes
Why? Highly conserved
Simpler gene structures
More sequenced genomes!
(for comparative approaches)
Methods? Previously, again mostly HMM-based
Now:
similarity-based.
comparative methods (because so many genomes available)
De novo motif discovery
D Dobbs ISU - BCB 444/544X

21. Predicting promoters: Steps & Strategies
Promoter Prediction (really)
10/26/05
Predicting promoters: Steps & Strategies
Closely related to gene prediction
Obtain genomic sequence
Use sequence-similarity based comparison
(BLAST, MSA) to find related genes
But: "regulatory" regions are much less well-conserved than coding regions
Locate ORFs
Identify TSS (if possible!)
Use promoter prediction programs
Analyze motifs, etc. in sequence (TRANSFAC)
FirstEF
D Dobbs ISU - BCB 444/544X

22. Automated promoter prediction strategies
Promoter Prediction (really)
10/26/05
Automated promoter prediction strategies
Pattern-driven algorithms
Sequence-similarity based algorithms
Combined "evidence-based"
BEST RESULTS? Combined, sequential
D Dobbs ISU - BCB 444/544X

23. 1: Promoter Prediction: Pattern-driven algorithms
Promoter Prediction (really)
10/26/05
1: Promoter Prediction: Pattern-driven algorithms
Success depends on availability of collections of annotated binding sites (TRANSFAC & PROMO)
Tend to produce huge numbers of FPs
Why?
Binding sites (BS) for specific TFs often variable
Binding sites are short (typically 5-15 bp)
Interactions between TFs (& other proteins) influence affinity & specificity of TF binding
One binding site often recognized by multiple BFs
Biology is complex: promoters often specific to organism/cell/stage/environmental condition
D Dobbs ISU - BCB 444/544X

24. Solutions to problem of too many FP predictions?
Promoter Prediction (really)
10/26/05
Solutions to problem of too many FP predictions?
Take sequence context/biology into account
Eukaryotes: clusters of TFBSs are common
Prokaryotes: knowledge of  factors helps
Probability of "real" binding site increases if annotated transcription start site (TSS) nearby
But: What about enhancers? (no TSS nearby!)
& Only a small fraction of TSSs have been experimentally mapped
CpG islands before promoter around TSS
TATA Box, CCAAT box
Content Information: hexamer frequency
D Dobbs ISU - BCB 444/544X

25. Why we cannot rely on consensus sequence?
Inr (Initiator) consensus sequence will appear once every 512bp in random sequences
For TATA box, one for every 120bp
Short-sequence patterns can appear by chance with high likelihood (false postives)

26. 2: Promoter Prediction: Phylogenetic Footprinting
Promoter Prediction (really)
10/26/05
2: Promoter Prediction: Phylogenetic Footprinting
Assumption: common functionality can be deduced from sequence conservation
Comparative promoter prediction:
"Phylogenetic footprinting
rVista, ConSite, PromH, FootPrinter
For comparative (phylogenetic) methods
Must choose appropriate species
Different genomes evolve at different rates
Classical alignment methods have trouble with
translocations, inversions in order of functional elements
If background conservation of entire region is highly conserved, comparison is useless
Not enough data (Prokaryotes >>> Eukaryotes)
Biology is complex: many (most?) regulatory elements are not conserved across species!
D Dobbs ISU - BCB 444/544X

27. 3: Promoter Prediction: Co-expression based algorithms
Promoter Prediction (really)
10/26/05
3: Promoter Prediction: Co-expression based algorithms
Problems:
Need sets of co-regulated genes
Genes experimentally determined to be co-regulated (using microarrays??)
Careful: How determine co-regulation?
Alignments of co-regulated genes should highlight elements involved in regulation
Algorithms:
MEME
AlignACE, PhyloCon
D Dobbs ISU - BCB 444/544X

28. Examples of promoter prediction/characterization software
Promoter Prediction (really)
10/26/05
Examples of promoter prediction/characterization software
MATCH, MatInspector
TRANSFAC
MEME & MAST
BLAST, etc.
Others?
FIRST EF
Dragon Promoter Finder (these are links in PPTs)
also see Dragon Genome Explorer (has specialized promoter software for GC-rich DNA, finding CpG islands, etc)
JASPAR
D Dobbs ISU - BCB 444/544X

29. TRANSFAC matrix entry: for TATA box
Fields:
Accession & ID
Brief description
TFs associated with this entry
Weight matrix
Number of sites used to build (How many here?)
Other info

30. Global alignment of human & mouse obese gene promoters (200 bp upstream from TSS)

31. Check out optional review & try associated tutorial:
Promoter Prediction (really)
10/26/05
Check out optional review & try associated tutorial:
Wasserman WW & Sandelin A (2004) Applied bioinformatics for identification of regulatory elements. Nat Rev Genet 5:
Check this out:
D Dobbs ISU - BCB 444/544X: Promoter Prediction (really!)
D Dobbs ISU - BCB 444/544X

32. Annotated lists of promoter databases & promoter prediction software
Promoter Prediction (really)
10/26/05
Annotated lists of promoter databases & promoter prediction software
URLs from Mount Chp 9, available online
Table
Table in Wasserman & Sandelin Nat Rev Genet article
URLs for Baxevanis & Ouellette, Chp 5:
More lists:
D Dobbs ISU - BCB 444/544X

33. Summary Promoter & gene regulation
3 types of methods for promoter prediction
Many programs have sensitivity and specificity less than 0.5
Integrative algorithms are more promising

34. Acknowledgement
Zhiping Weng (Boston Uni.)