CSCE555 Bioinformatics CSCE555 Bioinformatics Lecture 11 Promoter Predication 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 . HAPPY CHINESE NEW YEAR
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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
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
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Promoter prediction is easier in microbial genomes
• 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
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Examples of promoter Examples of promoter prediction/characterization prediction/characterization softwaresoftware
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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
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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
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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/
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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