10/26/05 D Dobbs ISU - BCB 444/544X: Promoter Prediction (really!) 1
10/26/05
Promoter Prediction(really!)
10/26/05 D Dobbs ISU - BCB 444/544X: Promoter Prediction (really!) 2
Announcements• BCB Link for Seminar Schedules (updated)
http://www.bcb.iastate.edu/seminars/index.html
Seminar (Fri Oct 28)
12:10 PM BCB Faculty Seminar in E164 Lagomarcino
Assembly and Alignment of Genomic DNA Sequence Xiaoqiu Huang, ComS
http://www.bcb.iastate.edu/courses/BCB691-F2005.html#Oct%2028
Mark your calendars:1:10 PM Nov 14 Baker Seminar in Howe Hall Auditorium
"Discovering transcription factor binding sites"Douglas Brutlag,Dept of Biochemistry & MedicineStanford University School of Medicine
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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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Announcements
Lab 9 - due Wed noon (today)Exam 2 - this Friday
Posted Online: Exam 2 Study Guide 544 Reading Assignment (2
papers) Lab Keys (today)
Thurs No Lab - Extra Office Hrs instead: David 1-3 PM in 209 Atanasoff Drena 1-3 PM in 106 MBB
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Promoter Prediction RNA Structure/Function Prediction
Mon Quite a few more words re: Gene prediction
Wed Promoter prediction
next Mon: RNA structure & function
RNA structure prediction2' & 3' structure prediction
miRNA & target prediction
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Optional - but very helpful reading:
1) Zhang MQ (2002) Computational prediction of eukaryotic protein-coding genes. Nat Rev Genet 3:698-709
http://proxy.lib.iastate.edu:2103/nrg/journal/v3/n9/full/nrg890_fs.html
2) 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
03489059922
(that's a hint!)
Check this out: http://www.phylofoot.org/NRG_testcases/
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Reading Assignment (for Mon)
Mount Bioinformatics• Chp 8 Prediction of RNA Secondary Structure • pp. 327-355• Ck Errata:
http://www.bioinformaticsonline.org/help/errata2.html
Cates (Online) RNA Secondary Structure Prediction Module• http://cnx.rice.edu/content/m11065/latest/
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Review last lecture:
Flowchart for Gene Prediction
Performance Assessment Measures
Correction re: slide 10/24 # 27
Promoters
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Gene prediction flowchart
Fig 5.15Baxevanis & Ouellette 2005
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Evaluation of Splice Site Prediction
Fig 5.11Baxevanis & Ouellette 2005
What do measures really mean?
Sp =
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Correction re: last lecture:
GeneSeqer Performance Graphs
Brendel et al (2004) Bioinformatics 20: 1157
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0.00
0.20
0.40
0.60
0.80
1.00
-10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 200.00
0.20
0.40
0.60
0.80
1.00
-10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20
SnSn
HumanGT site
HumanAG site
0.00
0.20
0.40
0.60
0.80
1.00
-10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20
0.00
0.20
0.40
0.60
0.80
1.00
-10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20
SnSn
A. thalianaAG site
A. thalianaGT site
Brendel 2005
Performance?
Note: these are not ROC curves (plots of (1-Sn) vs Sp)
• But plots such as these (& ROCs) much better than using "single number" to compare different methods• Both types of plots illustrate trade-off: Sn vs Sp
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QuickTime™ and aTIFF (LZW) decompressorare needed to see this picture.
Fig 2 - Brendel et al (2004) Bioinformatics 20: 1157
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Bayes Factor as Decision Criterion
H0: H=T:}){1(
}{
})|{1(
}|{
Tp
Tp
STp
STpBF
−−=
2-class model: }|{}|{ FSpTSpBF =
7 class model: ∑∑
∑∑
=
=
=
==ix x
ix xx
x x
x xx
Fp
FpFSp
Tp
TpTSpBF
,0,2,1
,0,2,1
0,2,1
0,2,1
}{
}{}|{
}{
}{}|{
Brendel 2005
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Evaluation of Splice Site Prediction
• Normalized specificity: αα β
=−
− +1
1
ActualTrue False
PP=TP+FP
PN=FN+TN
AP=TP+FNAN=FP+TN
PredictedTrue
False TNFN
FPTP
Brendel 2005
• Specificity: rAN
AP=
• Misclassification rates: α =FN
APβ =
FP
AN
• Sensitivity: = Coverage
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Careful: different definitions for "Specificity"
ActualTrue False
PP=TP+FP
PN=FN+TN
AP=TP+FNAN=FP+TN
PredictedTrue
False TNFN
FPTP
• Specificity:
• Sensitivity:
cf. Guig�ó definitions Sn: Sensitivity = TP/(TP+FN)
Sp: Specificity = TN/(TN+FP) = Sp-
AC: Approximate Coefficient = 0.5 x ((TP/(TP+FN)) + (TP/(TP+FP)) + (TN/(TN+FP)) + (TN/(TN+FN))) - 1
Other measures? Predictive Values, Correlation Coefficient
Brendel definitions
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Best measures for comparing different methods?
• ROC curves (Receiver Operating Characteristic?!!)
http://www.anaesthetist.com/mnm/stats/roc/
"The Magnificent ROC" - has fun applets & quotes:
"There is no statistical test, however intuitive and simple, which will not be abused by medical researchers"
• Correlation Coefficient(Matthews correlation coefficient (MCC)
MCC = 1 for a perfect prediction 0 for a completely random assignment
-1 for a "perfectly incorrect" prediction
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
Do not memorize this!
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PromotersWhat signals are there?
Simple ones in prokaryotes
BIOS Scientific Publishers Ltd, 1999
Brown Fig 9.17
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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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QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
What signals are there? Complex ones in eukaryotes!
Fig 9.13Mount 2004
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Simpler view of complex promoters in eukaryotes:
Fig 5.12Baxevanis & Ouellette 2005
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Eukaryotic genes are transcribed by 3 different RNA polymerases
BIOS Scientific Publishers Ltd, 1999
Brown Fig 9.18
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
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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
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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
BIOS Scientific Publishers Ltd, 1999
Brown Fig 9.12
• Potentially valuable tools for directed genome modification (esp. in plants) & human gene therapy
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New Today: Promoter Prediction
Predicting regulatory regions (focus on promoters) Brief review promoters & enhancers
Predicting promoters: eukaryotes vs prokaryotes
Next week: RNA structure & function
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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!
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Promoter prediction: Eukaryotes vs prokaryotes
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
methodsbecause so many genomes
available
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Predicting promoters: 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 (if possible!)• Use promoter prediction programs • Analyze motifs, etc. in sequence (TRANSFAC)
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Predicting promoters: 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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Automated 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 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
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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 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
• Do the wet lab experiments! • But: Promoter-bashing is tedious
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Promoter Prediction: Sequence-driven algorithms
• Assumption: common functionality can be deduced from 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
co-regulated (using microarrays??)• Comparative promoter prediction:
"Phylogenetic footprinting" - more later….
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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 elements are not conserved across species!
Promoter Prediction: Sequence-driven algorithms
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Examples of promoter prediction/characterization software
Lab: used 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 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
Fig 5.13Baxevanis & Ouellette 2005
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Global alignment of human & mouse obese gene promoters (200 bp
upstream from TSS)
Fig 5.14Baxevanis & Ouellette 2005
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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/
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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 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/
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Reading Assignment (for Mon)
Mount Bioinformatics• Chp 8 Prediction of RNA Secondary Structure • pp. 327-355• Ck Errata:
http://www.bioinformaticsonline.org/help/errata2.html
Cates (Online) RNA Secondary Structure Prediction Module• http://cnx.rice.edu/content/m11065/latest/