10/24/05 promoter prediction rna structure & function prediction

10/24/05 D Dobbs ISU - BCB 444/544X: Promoter Prediction 1

10/24/05

Promoter Prediction

RNA Structure & FunctionPrediction


AnnouncementsSeminar (Mon Oct 24) (several additional seminars listed in email sent to class)

12:10 PM IG Faculty Seminar in 101 Ind Ed II"Laser capture microdissection-facilitated

transcriptional profiling of abscission zones in Arabidopsis" Coralie Lashbrook, EEOB

http://www.bb.iastate.edu/%7Emarit/GEN691.html

Mark your calendars:1:10 PM Nov 14 Baker Seminar in Howe Hall Auditorium

"Discovering transcription factor binding sites"

Douglas Brutlag,Dept of Biochemistry & Medicine, Stanford University School of Medicine


Announcements

544 Semester ProjectsThanks to all who sent already!

Others: Information needed [email protected]

Briefly describe: • Your background & current grad research• Is there a problem related to your research you would like to learn more about & develop as project for this course? or • What would your ‘dream’ project be?


Announcements

Exam 2 - this Friday

Posted Online: Exam 2 Study Guide 544 Reading Assignment (2

papers)

Office Hours: David Mon 1-2 PM in 209 Atanasoff

Drena Tues 10-11AM in 106 MBB Michael - none this week

Thurs No Lab - Extra Office Hrs instead: David 1-3 PM in 209 Atanasoff Drena 1-3 PM in 106 MBB


Announcements

• Updated PPTs & PDFs for Gene Prediction lectures (covered on Exam 2) will be posted today (changes are minor)

• Is everyone on BCB 444/544 mailing list? Auditors?


Promoter Prediction & RNA Structure/Function Prediction

Mon Quite a few more words re: Gene prediction

Promoter prediction Wed RNA structure & function

RNA structure prediction2' & 3' structure prediction

miRNA & target prediction Thurs No Lab

Fri Exam 2


Reading Assignment - previousMount Bioinformatics

• Chp 9 Gene Prediction & Regulation• pp 361-401• Ck Errata:

http://www.bioinformaticsonline.org/help/errata2.html

* Brown Genomes 2 (NCBI textbooks online)• Sect 9 Overview: Assembly of Transcription Initiation

Complex • http://www.ncbi.nlm.nih.gov/books/bv.fcgi?

rid=genomes.chapter.7002

• Sect 9.1-9.3 DNA binding proteins, Transcription initiation• http://www.ncbi.nlm.nih.gov/books/bv.fcgi?

rid=genomes.section.7016* NOTEs: Don’t worry about the details!!

• See Study Guide for Exam 2 re:Sections covered


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/


Reading Assignment (for Wed)

Mount Bioinformatics• Chp 8 Prediction of RNA Secondary Structure • pp. 327-355• Ck Errata:


Cates (Online) RNA Secondary Structure Prediction Module• http://cnx.rice.edu/content/m11065/latest/


Review last lecture: Gene Prediction

(formerly Gene Prediction - 3)

• Overview of steps & strategies• Algorithms• Gene prediction software


Predicting Genes - Basic steps:• Obtain genomic DNA sequence• Translate in all 6 reading frames

• Compare with protein sequence database• Also perform database similarity search with EST & cDNA databases, if available

• Use gene prediction programs to locate genes• Analyze gene regulatory sequences

Note: Several important details missing above:1. Mask to "remove" repetitive elements (ALUs, etc.) ･2. Perform database search on translated DNA

(BlastX,TFasta)3. Use several programs to predict genes

(GenScan,GeneMark.hmm)4. Translate putative ORFs and search for functional

motifs (Blocks, Motifs, etc.) & regulatory sequences


Gene prediction flowchart

Fig 5.15Baxevanis & Ouellette 2005


Overview of gene prediction strategies

What sequence signals can be used?• Transcription: TF binding sites, promoter,

initiation site, terminator• Processing signals: splice donor/acceptors, polyA signal• Translation: start (AUG = Met) & stop (UGA,UUA, UAG)

ORFs, codon usageWhat other types of information can be used?• cDNAs & ESTs (pairwise alignment)• homology (sequence comparison, BLAST)


Examples of gene prediction software

1)Similarity-based or Comparative • BLAST • SGP2 (extension of GeneID)

2)Ab initio = “from the beginning”• GeneID - (used in lab last week)• GENSCAN - (used in lab last week)• GeneMark.hmm - (should try this!)

3)Combined "evidence-based”• GeneSeqer (Brendel et al., ISU)

BEST? GENSCAN, GeneMark.hmm, GeneSeqer

but depends on organism & specific task


Annotated lists of gene prediction software

• URLs from Mount Chp 9, available onlineTable 9.1 http://www.bioinformaticsonline.org/links/ch_09_t_1.html

• from Pevsner Chps 14 & 16http://www.bioinfbook.org/chapt14.htm - prokaryotichttp://www.bioinfbook.org/chapt16.htm - eukaryotic

• Table in Zhang Nat Rev Genet article: hptt://proxy.lib.iastate.edu:2103/nrg/journal/v3/n9/full/nrg890_fs.html

• Another list: Kozar, Stanfordhttp://cmgm.stanford.edu/classes/genefind/

Performance Evaluation? Guig�ó, Barcelona (&

sites above)http://www1.imim.es/courses/SeqAnalysis/GeneIdentification/Evaluation.html


Gene prediction: Eukaryotes vs prokaryotes

Gene prediction is easier in microbial genomes

Methods? Previously, mostly HMM-based

Now: similarity-based methodsbecause so many genomes

availableMany microbial genomes have been fully sequenced & whole-genome "gene structure" and "gene function" annotations are available.e.g., GeneMark.hmm TIGR Comprehensive Microbial Resource (CMR)

NCBI Microbial Genomes

see Mount Fig 9.7 (E.coli gene)


UCSC Browser view of 1000 kb region (Human URO-D gene)



• Perform pairwise alignment with large gaps in one sequence (due to introns)• Align genomic DNA with cDNA, ESTs, protein sequences

• Score semi-conserved sequences at splice junctions• Using a Bayesian model

• Score coding constraints in translated exons• Using a Bayesian model

Spliced Alignment Algorithm

Brendel 2005

GeneSeqer - Brendel et al.http://deepc2.psi.iastate.edu/cgi-bin/gs.cgi

Intron

GT AG

Splice sites

Donor

Acceptor

Brendel et al (2004) Bioinformatics 20: 1157


Brendel - Spliced Alignment I:Compare with cDNA or EST probes

Genomic DNA

Start codon Stop codon

mRNA -Poly(A)Cap-

5’-UTR 3’-UTR


Brendel 2005


Brendel - Spliced Alignment II:Compare with protein probes

Genomic DNA


Protein

Brendel 2005


• Information Content Information Content IIii ::

I f fi iBB U C A G

iB= +∈∑2 2, , ,

log ( )

• Extent of Splice Signal Window:

I Ii I≤ +196. σ

i: ith position in sequenceĪ: avg information content over all positions >20 nt from splice siteσĪ: avg sample standard deviation of Ī

Splice Site Detection

Brendel 2005

Do DNA sequences surrounding splice "consensus" sequences contribute to splicing signal?

YES


0.0

0.1

0.2

0.3

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0.8

-50 -40 -30 -20 -10 0 10 20 30 40 50

0.0

0.1

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0.5

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0.8

-50 -40 -30 -20 -10 0 10 20 30 40 50

HumanT2_GT

HumanT2_AG

Information content vs position

Brendel 2005

Which sequences are exons & which are introns?How can you tell?



Bayesian Splice Site Prediction

where H indexes the hypotheses of GT or AG at - True site in reading phase 1, 2, or 0 - False within-exon site in reading phase 1, 2, or 0 - False within-intron site

Let S = s-l s-l+1 s-l+2…s-1GT s1 s2 s3 …sr

∑=H

HPHSPHPHSPSHP }){}|{/(}{}|{}|{

11,/}{}|{}{}{

11

1−−∏∏

+−=−−

+−=− ==

iii s

r

lislii

r

lil ffspsspspSP

Brendel 2005



Bayes Factor as Decision Criterion

H0: H=T }){1(

}{

})|{1(

}|{

Tp

Tp

STp

STpBF

−−=

2-class model: }|{}|{ FSpTSpBF =

7-class model:

€

BF =p{S |Tx}p{Tx}x=1,2,0

∑p{Tx}x=1,2,0

∑p{S |Fx}p{Fx}x=1,2,0,i

∑p{Fx}x=1,2,0,i

∑

Brendel 2005



en en+1

in in+1

PG

PA(n)PG

(1-PG)PD(n+1)

(1-PG)PD(n+1)

(1-PG)(1-PD(n+1))

1-PA(n)

PG

Markov Model for Spliced Alignment

Brendel 2005


Evaluation of Splice Site Prediction

• Normalized specificity: σ 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


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

σ σ

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

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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

σ σ

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


Evaluation of Splice Site Prediction


What do measures really mean?

Sp =


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


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!


Performance of GeneSeqer vs other methods?

• Comparison with ab initio gene prediction

(e.g., GENESCAN)

• Depends on:• Availability of ESTs• Availability of protein homologs

Brendel 2005

Other Performance Evaluations? Guig�óhttp://www1.imim.es/courses/SeqAnalysis/GeneIdentification/Evaluation.html


Target protein alignment score

0.000.100.200.300.400.500.600.700.800.901.00

0 10 20 30 40 50 60 70 80 90 100

Exo

n (S

n +

Sp)

/ 2

GeneSeqer

NAP

GENSCAN

Brendel 2005

GENSCAN - Burge, MIT

GeneSeqer vs GENSCAN (Exon prediction)


0.000.100.200.300.400.500.600.700.800.901.00

0 10 20 30 40 50 60 70 80 90 100Target protein alignment score

Intr

on (

Sn

+ S

p) /

2

GeneSeqer

NAP

GENSCAN

Brendel 2005

GENSCAN - Burge, MIT

GeneSeqer vs GENSCAN(Intron prediction)


Other Resources

Current Protocols in Bioinformaticshttp://www.4ulr.com/products/currentprotocols/bioinformatics.html

Finding Genes 4.1 An Overview of Gene Identification: Approaches, Strategies, and

Considerations 4.2 Using MZEF To Find Internal Coding Exons 4.3 Using GENEID to Identify Genes 4.4 Using GlimmerM to Find Genes in Eukaryotic Genomes 4.5 Prokaryotic Gene Prediction Using GeneMark and GeneMark.hmm 4.6 Eukaryotic Gene Prediction Using GeneMark.hmm 4.7 Application of FirstEF to Find Promoters and First Exons in the Human Genome

4.8 Using TWINSCAN to Predict Gene Structures in Genomic DNA Sequences 4.9 GrailEXP and Genome Analysis Pipeline for Genome Annotation 4.10 Using RepeatMasker to Identify Repetitive Elements in Genomic Sequences


New Today: Promoter Prediction

• A few more words about Gene prediction

• Predicting regulatory regions (focus on promoters)

Brief review promoters & enhancers Predicting in eukaryotes vs prokaryotes

Introduction to RNAStructure & function


Predicting Promoters

What signals are there?

Algorithms

Promoter prediction software


What signals are there? Simple ones in prokaryotes

BIOS Scientific Publishers Ltd, 1999

Brown Fig 9.17


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


QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

What signals are there? Complex ones in eukaryotes!

Fig 9.13Mount 2004


Simpler view of complex promoters in eukaryotes:



Eukaryotic genes are transcribed by 3 different RNA polymerases


Brown Fig 9.18

Recognize different types of promoters & enhancers:


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


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


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


Brown Fig 9.12

• Potentially valuable tools for directed genome modification (esp. in plants) & human gene therapy


Global alignment of human & mouse obese gene promoters (200 bp

upstream from TSS)



Reading Assignment (for Wed)

Mount Bioinformatics• Chp 8 Prediction of RNA Secondary Structure • pp. pp. 327-355• Ck Errata:


Cates (Online) RNA Secondary Structure Prediction Module• http://cnx.rice.edu/content/m11065/latest/

10/24/05 promoter prediction rna structure & function prediction

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