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RNA Structure & Function 10/31/05
D Dobbs ISU - BCB 444/544X 1
10/31/05 D Dobbs ISU - BCB 444/544X: RNA Structure & Function 1
10/31/05
RNA Structure & Function
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Announcements
Seminar (Mon Oct 31)12:10 PM IG Faculty Seminar in 101 Ind Ed II
Plant Steroid Hormone Signal TransductionYanhai Yin, GDCB
• BCB Link for Seminar Schedules (updated)http://www.bcb.iastate.edu/seminars/index.html
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Announcements
BCB 544 Projects - Important Dates:
Nov 2 Wed noon - Project proposals due to David/Drena
Nov 4 Fri 10A - Approvals/responses to students
Dec 2 Fri noon - Written project reports due
Dec 5,7,8,9 class/lab - Oral Presentations (20')
(Dec 15 Thurs = Final Exam)
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Methods? Previously, again mostly HMM-based Now: similarity-based. comparative methods
because so many genomes available
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Promoter Prediction: Steps & Strategies
Closely related to gene prediction!• Obtain genomic sequence• Use sequence-similarity based comparison
(BLAST, MSA) to find related genesBut: "regulatory" regions are much less well-conserved than coding regions
• Locate ORFs• Identify TSS (Transcription Start Site)• Use promoter prediction programs• Analyze motifs, etc. in sequence (TRANSFAC)
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Promoter Prediction: Steps & Strategies
Identify TSS --if possible?• One of biggest problems is determining exact TSS!
Not very many full-length cDNAs!• Good starting point? (human & vertebrate genes)
Use FirstEFfound within UCSC Genome Browseror submit to FirstEF web server
Fig 5.10Baxevanis &Ouellette 2005
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Promoter prediction strategies
1) Pattern-driven algorithms
2) Sequence-driven algorithms
3) Combined "evidence-based"
BEST RESULTS? Combined, sequential
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Promoter Prediction: Pattern-driven algorithms
• Success depends on availability of collections ofannotated 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
RNA Structure & Function 10/31/05
D Dobbs ISU - BCB 444/544X 3
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Promoter Prediction: Pattern-driven algorithms
Solutions to problem of too many FP predictions?• Take sequence context/biology into account
• Eukaryotes: clusters of TFBSs are common• Prokaryotes: knowledge of σ factors helps
• Probability of "real" binding site increases ifannotated transcription start site (TSS) nearby• But: What about enhancers? (no TSS nearby!)
& Only a small fraction of TSSs have beenexperimentally mapped
• Do the wet lab experiments!• But: Promoter-bashing is tedious
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Promoter Prediction: Sequence-driven algorithms
• Assumption: common functionality can be deducedfrom sequence conservation• Alignments of co-regulated genes should highlight
elements involved in regulationCareful: How determine co-regulation?• Orthologous genes from difference species• Genes experimentally determined to be
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Problems:• Need sets of co-regulated genes• 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 elementsare not conserved across species!
Promoter Prediction: Sequence-driven algorithms
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Examples of promoterprediction/characterization software
Lab: used MATCH, MatInspectorTRANSFACMEME & MASTBLAST, etc.
Others?FIRST EFDragon Promoter Finder (these are links in PPTs)
also see Dragon Genome Explorer (has specializedpromoter software for GC-rich DNA, finding CpGislands, etc)
JASPAR
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Global alignment of human & mouse obesegene promoters (200 bp upstream from TSS)
Fig 5.14Baxevanis &Ouellette 2005 10/31/05 D Dobbs ISU - BCB 444/544X: RNA Structure & Function 18
Check out optional review &try associated tutorial:
Wasserman WW & Sandelin A (2004) Applied bioinformatics foridentification of regulatory elements. Nat Rev Genet 5:276-287http://proxy.lib.iastate.edu:2103/nrg/journal/v5/n4/full/nrg1315_fs.html
Check this out: http://www.phylofoot.org/NRG_testcases/
RNA Structure & Function 10/31/05
D Dobbs ISU - BCB 444/544X 4
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Annotated lists of promoter databases &promoter 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 articlehttp://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=promo