CSCE555 Bioinformatics Lecture 11 Promoter Predication

HAPPY CHINESE NEW YEAR

Meeting: MW 4:00PM-5:15PM SWGN2A21 Instructor: Dr. Jianjun Hu Course page: http://www.scigen.org/csce555 University of South Carolina Department of Computer Science and Engineering 2008 www.cse.sc.edu

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Outline

   

Introduction to DNA Motif Motif Representations (Recap) Motif database search Algorithms for motif discovery

4/27/2020 2

Search Space

Motif width = W Length = L Size of search space = (L – W + 1) N L=100, W=15, N=10  size  10 19 N

Worked Example

score 

k W

  1 ln    

N

6  3  !

i

 

a

,

c

,

g c

,

t ki

!

i

 

a

,

c

,

g

,

t

 

i

   c ki =

a c g t 1

0 4 0 0

2

2 0 1 1

3

0 2 2 0

4

3 1 0 0 Score = 1.99 - 0.50 + 0.20 + 0.60 = 2.29

N = 4 p i = ¼  

i

  

N

6  3  !



i N

 1 256  32 105

Gibbs Sampling Search

1 X 2 Suppose the search space is a 2D rectangle. (Typically, more than 2 dimensions!) Start at a random point X.

Randomly pick a dimension.

Look at all points along this dimension.

Move to one of them randomly, proportional to its score π.

Repeat.

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.

Does it Work in Practice?

      Only successful cases get published!

Seems more successful in microbes than in animals.

(bacteria & yeast) 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

1.

Go to our MEME server: 1.

2.

3.

4.

5.

http://molgen.biol.rug.nl/meme/website/meme.ht

ml Fill in your emailadres, 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”

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

Promoter Prediction

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What are promoters?

Three strategies for promoter prediction

◦ Signal based ◦ Comparative genomics/phylogenetic footprinting ◦ Expression profile base de-novo motif discovery algorthms

What is a Promoter?

Region of gene that binds RNA polymerase and transcription factors to initiate transcription

Promoters:

What signals are there?

Simple ones in prokaryotes 12

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

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What signals are there? Complex ones in eukaryotes

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Eukaryotic genes are transcribed by 3 different RNA polymerases

Recognize different types of promoters & enhancers:

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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 16

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”

 http://www.ncbi.nlm.nih.gov/books/bv.fcgi?rid=genomes.table.7039

TFs recognize specific short DNA sequence motifs “transcription factor binding sites” ◦ Several databases for these, e.g.

TRANSFAC

http://www.

generegulation .com/cgibin/pub/databases/transfac 17

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 18

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 19

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 20

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!) FirstEF Use promoter prediction program s Analyze motifs, etc. in sequence (TRANSFAC) 21

Automated promoter prediction strategies

1) Pattern-driven algorithms 2) Sequence-similarity based algorithms 3) Combined "evidence-based" BEST RESULTS?

Combined, sequential

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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 Binding sites are short variable (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 23

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 24

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)

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!

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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

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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

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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 29

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

Check out optional review & try associated tutorial:

Wasserman WW & Sandelin A (2004) Applied bioinformatics for identification of regulatory elements. Nat Rev Genet 5:276-287 http://proxy.lib.iastate.edu:2103/nrg/journal/v5/n4/full/nrg1315_fs.html

Check this out: http://www.phylofoot.org/NRG_testcases/ D Dobbs ISU - BCB 444/544X: Promoter Prediction (really!) 31

Annotated lists of promoter databases & promoter prediction software • • •

URLs from Mount Chp 9, available online Table 9.12

http://www.bioinformaticsonline.org/links/ch_09_t_2.html

Table in Wasserman & Sandelin Nat Rev Genet article

http://proxy.lib.iastate.edu:2103/nrg/journal/v5/n4/full/nrg1315_fs.htm

URLs for Baxevanis & Ouellette, Chp 5:

http://www.wiley.com/legacy/products/subject/life/bioinformatics/ch05.htm#links • • •

More lists:

http://www.softberry.com/berry.phtml?topic=index&group=programs&subgroup=prom oter http://bioinformatics.ubc.ca/resources/links_directory/?subcategory_id=104 http://www3.oup.co.uk/nar/database/subcat/1/4/ 32

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

Acknowledgement



Zhiping Weng (Boston Uni.)