CSCE 555 Bioinformatics Lecture 11 Promoter Predication HAPPY
CSCE 555 Bioinformatics Lecture 11 Promoter Predication HAPPY CHINESE NEW YEAR Meeting: MW 4: 00 PM-5: 15 PM SWGN 2 A 21 Instructor: Dr. Jianjun Hu Course page: http: //www. scigen. org/csce 555 University of South Carolina Department of Computer Science and Engineering 2008 www. cse. sc. edu.
Outline Introduction to DNA Motif Representations (Recap) Motif database search Algorithms for motif discovery 12/25/2021 2
Search Space Motif width = W N Length = L Size of search space = (L – W + 1)N L=100, W=15, N=10 size 1019
Worked Example cki = 1 2 3 4 a 0 2 0 3 c 4 0 2 1 g 0 1 2 0 t 0 1 0 0 Score = 1. 99 - 0. 50 + 0. 20 + 0. 60 = 2. 29 N = 4 pi = ¼
Gibbs Sampling Search 1 Suppose the search space is a 2 D rectangle. (Typically, more than 2 dimensions!) Start at a random point X. Randomly pick a dimension. 2 X 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 (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. ht ml 1. Fill in your emailadres, description of the sequences 2. Open the fasta formatted file you just saved with Genome 2 d (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 Prediction 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 13
What signals are there? Complex ones in eukaryotes 14
Eukaryotic genes are transcribed by 3 different RNA polymerases Recognize different types of promoters & enhancers: 15
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. tabl e. 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 • Common in eukaryotic proteins factors • Estimated 1% of mammalian encode zinc-finger proteins genes • 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 v But: "regulatory" regions are much less well-conserved than coding regions Locate ORFs • Identify TSS (if possible!)First. EF • Use promoter prediction programs • • 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 22
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 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 • Cp. G islands before promoter around TSS • TATA Box, CCAAT box 24
Why we cannot rely on consensus sequence? Inr (Initiator) consensus sequence will appear once every 512 bp in random sequences For TATA box, one for every 120 bp 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 r. Vista, Con. Site, Prom. H, Foot. Printer • • 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! 26
3: Promoter Prediction: Co-expression based algorithms Problems: • • • Need sets of co-regulated genes Genes experimentally determined to be coregulated (using microarrays? ? ) Careful: How determine co-regulation? Alignments of co-regulated genes should highlight elements involved in regulation Algorithms: MEME Align. ACE, Phylo. Con 27
Examples of promoter prediction/characterization software MATCH, Mat. Inspector 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 Cp. G islands, etc) JASPAR 28
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/v 5/n 4/full/nrg 1315_fs. ht ml Check this out: http: //www. phylofoot. org/NRG_testcases/ D Dobbs ISU - BCB 444/544 X: 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/v 5/n 4/full/nrg 1315_fs. htm URLs for Baxevanis & Ouellette, Chp 5: http: //www. wiley. com/legacy/products/subject/life/bioinformatics/ch 05. 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: //www 3. 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. )
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