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CSCE555 Bioinformatics Lecture 11 Promoter Predication Meeting: MW 4:00PM-5:15PM SWGN2A21...
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Transcript of CSCE555 Bioinformatics Lecture 11 Promoter Predication Meeting: MW 4:00PM-5:15PM SWGN2A21...
CSCE555 BioinformaticsCSCE555 Bioinformatics
Lecture 11 Promoter Predication
Meeting: MW 4:00PM-5:15PM SWGN2A21Instructor: Dr. Jianjun HuCourse page: http://www.scigen.org/csce555University of South CarolinaDepartment of Computer Science and Engineering2008 www.cse.sc.edu.
HAPPY CHINESE NEW YEAR
OutlineOutline
Introduction to DNA MotifMotif Representations (Recap)Motif database searchAlgorithms for motif discovery
04/19/23 2
Search SpaceSearch Space
N
Length = L
Motif width = W
Size of search space = (L – W + 1)N
L=100, W=15, N=10 size 1019
Worked ExampleWorked Example
W
k tgcai
ci
tgcaiki
kipcN1 ,,,,,,
!!3
6lnscore
1 2 3 4
a 0 2 0 3
c 4 0 2 1
g 0 1 2 0
t 0 1 0 0
2561
41 N
i
cikipcki =
N = 4pi = ¼
10532
!36
i
cikip
N
Score = 1.99 - 0.50 + 0.20 + 0.60 = 2.29
Gibbs Sampling SearchGibbs Sampling Search
1
2
Suppose the search space is a 2D rectangle. (Typically, more than 2 dimensions!)
X
Start at a random point X.
Randomly pick a dimension.
Look at all points along this dimension.
Repeat.
Move to one of them randomly, proportional to its score π.
Gibbs Sampling for Motif Gibbs Sampling for Motif SearchSearch
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?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
1. Go to our MEME server:
http://molgen.biol.rug.nl/meme/website/meme.html
1. Fill in your emailadres, description of the sequences
2. Open the fasta formatted file you just saved with Genome2d (click “Browse”)
3. Select the number of motifs, number of sites and the optimum width of the motif
4. Click “Search given strand only”
5. Click “Start search”
Something like this will appear in your email. The results are quite self explanatory.
Promoter PredictionPromoter PredictionWhat 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?What is a Promoter?
Region of gene that binds RNA polymerase and transcription factors to initiate transcription
12
Promoters:Promoters:What signals are there?What signals are there?
Simple ones in prokaryotesSimple ones in prokaryotes
Prokaryotic promoters 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
13
14
What signals are there? What signals are there? Complex ones in Complex ones in
eukaryoteseukaryotes
15
Eukaryotic genes are transcribed by Eukaryotic genes are transcribed by 3 different RNA polymerases3 different RNA polymerases
Recognize different types of promoters & enhancers:
Eukaryotic promoters & Eukaryotic promoters & enhancers 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 Eukaryotic transcription factors 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/transfac17
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Zinc finger-containing Zinc finger-containing transcription factors 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
Predicting PromotersPredicting 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
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Promoter prediction: Promoter prediction: Eukaryotes vs prokaryotesEukaryotes vs prokaryotes
20
Promoter prediction is easier in microbial genomes
Why? Highly conservedSimpler gene structuresMore 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
Predicting promoters: Steps & Predicting promoters: Steps & StrategiesStrategies 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!)Identify TSS (if possible!)• Use promoter prediction programs • Analyze motifs, etc. in sequence
(TRANSFAC)
21
FirstEF
Automated promoter Automated promoter prediction strategiesprediction strategies
22
1) Pattern-driven algorithms
2) Sequence-similarity based algorithms
3) Combined "evidence-based"
BEST RESULTS? Combined, sequential
1: Promoter Prediction: Pattern-driven 1: Promoter Prediction: Pattern-driven algorithmsalgorithms
23
• 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
Solutions to problem of too many Solutions to problem of too many FP predictions?FP predictions?
24
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
Why we cannot rely on consensus Why we cannot rely on consensus sequence?sequence?Inr (Initiator) consensus sequence will
appear once every 512bp in random sequences
For TATA box, one for every 120bpShort-sequence patterns can appear
by chance with high likelihood (false postives)
2: Promoter Prediction: Phylogenetic 2: Promoter Prediction: Phylogenetic FootprintingFootprinting
26
• Assumption: common functionality can be deduced from sequence conservation• Comparative promoter prediction:
"Phylogenetic footprintingrVista, 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!
3: Promoter Prediction: Co-3: Promoter Prediction: Co-expression based algorithmsexpression 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
27
Examples of promoter Examples of promoter prediction/characterization prediction/characterization softwaresoftware
28
MATCH, MatInspectorTRANSFACMEME & MASTBLAST, etc.
Others?FIRST EFDragon 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
29
TRANSFAC matrix entry: for TRANSFAC matrix entry: for TATA boxTATA 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 Global alignment of human & mouse obese gene promoters (200 bp upstream from gene promoters (200 bp upstream from TSS)TSS)
Check out optional review & Check out optional review & try associated tutorial: try associated tutorial:
Wasserman WW & Sandelin A (2004) Applied bioinformatics for identification of regulatory elements. Nat Rev Genet 5:276-287http://proxy.lib.iastate.edu:2103/nrg/journal/v5/n4/full/nrg1315_fs.html
D Dobbs ISU - BCB 444/544X: Promoter Prediction (really!) 31
Check this out: http://www.phylofoot.org/NRG_testcases/
32
Annotated lists of promoter databases & Annotated lists of promoter databases & promoter prediction softwarepromoter prediction software
• URLs from Mount Chp 9, available onlineTable 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=promoter• http://bioinformatics.ubc.ca/resources/links_directory/?
subcategory_id=104• http://www3.oup.co.uk/nar/database/subcat/1/4/
SummarySummaryPromoter & gene regulation3 types of methods for promoter predictionMany programs have sensitivity and
specificity less than 0.5 Integrative algorithms are more promising
AcknowledgementAcknowledgementZhiping Weng (Boston Uni.)