BCB 444/544
Lecture 28
Gene Prediction - finish it
Promoter Prediction
#28_Oct29
BCB 444/544 F07 ISU Dobbs #28- Promoter Prediction
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Required Reading
(before lecture)
Mon Oct 29 - Lecture 28
Promoter & Regulatory Element Prediction
• Chp 9 - pp 113 - 126
Wed Oct 30 - Lecture 29
Phylogenetics Basics
• Chp 10 - pp 127 - 141
Thurs Oct 31 - Lab 9
Gene & Regulatory Element Prediction
Fri Oct 30 - Lecture 29
Phylogenetic Tree Construction Methods & Programs
• Chp 11 - pp 142 - 169
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Assignments & Announcements
Mon Oct 29 - HW#5 - will be posted today
HW#5 = Hands-on exercises with phylogenetics
and tree-building software
Due: Mon Nov 5
(not Fri Nov 1 as previously posted)
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BCB 544 "Team" Projects
Last week of classes will be devoted to Projects
• Written reports due:
• Mon Dec 3 (no class that day)
• Oral presentations (20-30') will be:
• Wed-Fri Dec 5,6,7
• 1 or 2 teams will present during each class period
 See Guidelines for Projects posted online
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BCB 544 Only:
New Homework Assignment
544 Extra#2
Due:
√PART 1 - ASAP
PART 2 - meeting prior to 5 PM Fri Nov 2
Part 1 - Brief outline of Project, email to Drena & Michael
after response/approval, then:
Part 2 - More detailed outline of project
Read a few papers and summarize status of problem
Schedule meeting with Drena & Michael to discuss ideas
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Seminars this Week
BCB List of URLs for Seminars related to Bioinformatics:
http://www.bcb.iastate.edu/seminars/index.html
• Nov 1 Thurs - BBMB Seminar 4:10 in 1414 MBB
• Todd Yeates UCLA TBA -something cool about
structure and evolution?
• Nov 2 Fri - BCB Faculty Seminar 2:10 in 102 ScI
• Bob Jernigan BBMB, ISU
• Control of Protein Motions by Structure
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Chp 8 - Gene Prediction
SECTION III GENE AND PROMOTER PREDICTION
Xiong: Chp 8 Gene Prediction
• Categories of Gene Prediction Programs
• Gene Prediction in Prokaryotes
• Gene Prediction in Eukaryotes
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Computational Gene Prediction: Approaches
• Ab initio methods
• Search by signal: find DNA sequences involved in gene
expression
• Search by content: Test statistical properties distinguishing
coding from non-coding DNA
• Similarity-based methods
• Database search: exploit similarity to proteins, ESTs, cDNAs
• Comparative genomics: exploit aligned genomes
• Do other organisms have similar sequence?
• Hybrid methods - best
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This is a new slide
Computational Gene Prediction: Algorithms
1. Neural Networks (NNs)
(more on these later…)
e.g., GRAIL
2. Linear discriminant analysis (LDA) (see text)
e.g., FGENES, MZEF
3. Markov Models (MMs) & Hidden Markov Models (HMMs)
e.g., GeneSeqer - uses MMs
GENSCAN - uses 5th order HMMs - (see text)
HMMgene - uses conditional maximum likelihood (see text)
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Signals Search
This is a new slide
Approach: Build models (PSSMs, profiles, HMMs, …) and search
against DNA. Detected instances provide evidence for genes
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Content Search
This is a new slide
Observation: Encoding a protein affects statistical properties
of DNA sequence:
• Nucleotide.amino acid distribution
• GC content (CpG islands, exon/intron)
• Uneven usage of synonymous codons (codon bias)
• Hexamer frequency - most discriminative of these
for identifying coding potential
Method: Evaluate these differences (coding statistics) to
differentiate between coding and non-coding regions
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Human Codon Usage
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Predicting Genes based on
Codon Usage Differences
This is a new slide
Algorithm:
Process sliding window
•
•
Use codon frequencies to
compute probability of
coding versus non-coding
Plot log-likelihood ratio:


PS | coding 

log 
 P( S | non  coding ) 
Exons
Coding Profile of ß-globin gene
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Similarity-Based Methods:
Database Search
This is a new slide
In different genomes: Translate DNA into all 6 reading
frames and search against proteins (TBLASTX,BLASTX, etc.)
ATTGCGTAGGGCGCT
TAACGCATCCCGCGA
Within same genome: Search with EST/cDNA database
(EST2genome, BLAT, etc.).
Problems:
• Will not find “new” or RNA genes (non-coding genes).
• Limits of similarity are hard to define
• Small exons might be overlooked
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Similarity-Based Methods:
Comparative Genomics
This is a new slide
Idea: Functional regions are more conserved than non-functional
ones; high similarity in alignment indicates gene
human
mouse
GGTTTT--ATGAGTAAAGTAGACACTCCAGTAACGCGGTGAGTAC----ATTAA
|
||||| ||||| |||
||||| |||||||||||||
| |
C-TCAGGAATGAGCAAAGTCGAC---CCAGTAACGCGGTAAGTACATTAACGA-
Advantages:
•
May find uncharacterized or RNA genes
Problems:
•
•
Finding suitable evolutionary distance
Finding limits of high similarity (functional regions)
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Human-Mouse Homology
Human
This is a new slide
Mouse
Comparison of 1196 orthologous genes
• Sequence identity between genes in human vs mouse
Exons:
84.6%
Protein:
85.4%
Introns: 35%
5’ UTRs: 67%
3’ UTRs: 69%
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Thanks to Volker Brendel, ISU
for the following Figs & Slides
Slightly modified from:
BSSI Genome Informatics Module
http://www.bioinformatics.iastate.edu/BBSI/course_desc_20
05.html#moduleB
V Brendel vbrendel@iastate.edu
Brendel et al (2004) Bioinformatics 20: 1157
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GeneSeqer - Brendel et al.- ISU
http://deepc2.psi.iastate.edu/cgi-bin/gs.cgi
Spliced Alignment Algorithm
Brendel et al (2004) Bioinformatics 20: 1157
http://bioinformatics.oxfordjournals.org/cgi/con
tent/abstract/20/7/1157
• 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 Bayesian probability model & 1st order MM
• Score coding constraints in translated exons
Intron
• Using Bayesian model
GT
Donor
Brendel 2005
AG
Splice sites
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Acceptor
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Splice Site Detection
Do DNA sequences surrounding splice "consensus"
sequences contribute to splicing signal? YES
• Information Content Ii :
Ii  2 
f
iB
BU ,C , A,G
log 2 ( f iB )
• Extent of Splice Signal Window:
I i  I  196
. I
i: ith position in sequence
Ī: avg information content over all positions >20 nt from splice site
Ī: avg sample standard deviation of Ī
Brendel 2005
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Information Content vs Position
0.8
0.8
0.7
0.7
Human
T2_GT
0.6
0.5
0.5
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0.0
-50
-40
-30
-20
-10
Human
T2_AG
0.6
0.0
0
10
20
30
40
50 -50
-40
-30
-20
-10
0
10
20
30
40
50
Which sequences are exons & which are introns?
How can you tell?
Brendel 2005
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Markov Model for Spliced Alignment
PG
PG
(1-PG)(1-PD(n+1))
en
en+1
(1-PG)PD(n+1)
PA(n)PG
(1-PG)PD(n+1)
in
in+1
1-PA(n)
Brendel 2005
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This is a new slide
Evaluation of Splice Site Prediction
Right!
Fig 5.11
Baxevanis & Ouellette
2005
TP
FP
TN
FN
=
=
=
=
positive instance correctly predicted as positive
negative instance incorrectly predicted as positive
negative instance correctly predicted as negative
positive instance incorrectly predicted as negative
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Evaluation of Predictions
Predicted
Positives
Actual
True False
Predicted
True
TP
FP
PP=TP+FP
False
FN
TN
PN=FN+TN
True
Positives
False
Positives
AP=TP+FN AN=FP+TN
FN
AP
 TP
/ AP
• Sensitivity: S n SnTP
/ AP
 11 
• Misclassification rates:
Coverage
Recall


FP
AN
ANAN AN 1 11
 TP
S/ pPP
TP
  1   
• Specificity: S p SpTP
/ PP
1/1PP
PPPP PP1 11
 r
r
• Normalized
1 
specificity:  
1   
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r
AN
AP
Do not memorize this!
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Evaluation of Predictions - in English
Actual
True False
Predicted
True
TP
FP
PP=TP+FP
False
FN
TN
PN=FN+TN
AP=TP+FN AN=FP+TN
• Sensitivity: S n  TP / AP  =
1 Coverage

In English? Sensitivity is the
fraction of all positive instances
having a true positive prediction.
IMPORTANT: Sensitivity alone does
not tell us much about performance
because a 100% sensitivity can be
achieved trivially by labeling all test
cases positive!
AN
1 
• Specificity: S p  TP / PP  1= Recall

PP
1IMPORTANT:
   r
in medical jargon,
Specificity is sometimes defined
In English? Specificity is the
differently (what we define here as
fraction of all predicted positives
"Specificity" is sometimes referred
that are, in fact, true positives.
to as "Positive predictive value")
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This slide has been changed
Best Measures for Comparison?
• ROC curves
(Receiver Operating Characteristic (?!!)
http://en.wikipedia.org/wiki/Roc_curve
In signal detection theory, a receiver operating characteristic (ROC), or
ROC curve is a plot of sensitivity
vs
(1 - specificity)
for a binary classifier system as its discrimination threshold is varied.
The ROC can also be represented equivalently by plotting fraction
of true positives (TPR = true positive rate) vs
fraction of false positives (FPR = false positive rate)
• Correlation Coefficient
Matthews correlation coefficient (MCC)
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
MCC = 1 for a perfect prediction
0 for a completely random assignment
-1 for a "perfectly incorrect" prediction
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GeneSeqer: Input
http://deepc2.psi.iastate.edu/cgi-bin/gs.cgi
Brendel 2005
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GeneSeqer: Output
Brendel 2005
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GeneSeqer: Gene Evidence Summary
Brendel 2005
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Gene Prediction - Problems & Status?
Common errors?
• False positive intergenic regions:
• 2 annotated genes actually correspond to a single gene
• False negative intergenic region:
• One annotated gene structure actually contains 2 genes
• False negative gene prediction:
• Missing gene (no annotation)
• Other:
• Partially incorrect gene annotation
• Missing annotation of alternative transcripts
Current status?
• For ab initio prediction in eukaryotes: HMMs have better overall
performance for detecting intron/exon boundaries
• Limitation? Training data: predictions are organism specific
• Combined ab initio/homology based predictions: Improved accurracy
• Limitation? Availability of identifiable sequence homologs in databases
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Recommended Gene Prediction Software
•
Ab initio
•
•
•
•
Similarity-based
•
•
GENSCAN: http://genes.mit.edu/GENSCAN.html
GeneMark.hmm: http://exon.gatech.edu/GeneMark/
others: GRAIL, FGENES, MZEF, HMMgene
BLAST, GenomeScan, EST2Genome, Twinscan
Combined:
•
•
GeneSeqer, http://deepc2.psi.iastate.edu/cgi-bin/gs.cgi
ROSETTA
 Consensus: because results depend on organisms & specific
task, Always use more than one program!
• Two servers hat report consensus predictions
• GeneComber
• DIGIT
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Other Gene Prediction Resources: at ISU
http://www.bioinformatics.iastate.edu/bioinformatics2go/
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Other Gene Prediction Resources:
GaTech, MIT, Stanford, etc.
Lists of Gene Prediction Software
http://www.bioinformaticsonline.org/links/ch_09_t_1.html
http://cmgm.stanford.edu/classes/genefind/
Current Protocols in Bioinformatics (BCB/ISU owns a copy - currently in my lab!)
Chapter 4 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
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Chp 9 - Promoter & Regulatory Element
Prediction
SECTION III GENE AND PROMOTER PREDICTION
Xiong: Chp 9 Promoter & Regulatory Element Prediction
• Promoter & Regulatory Elements in Prokaryotes
• Promoter & Regulatory Elements in Eukaryotes
• Prediction Algorithms
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Eukaryotes vs Prokaryotes:
Genomes
Eukaryotic genomes
• Are packaged in chromatin & sequestered in a nucleus
• Are larger and have multiple linear chromosomes
• Contain mostly non-protein coding DNA (98-99%)
Prokarytic genomes
• DNA is associated with a nucleoid, but no nucleus
• Much larger, usually single, circular chromosome
• Contain mostly protein encoding DNA
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Eukaryotes vs Prokryotes:
Gene Structure
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Eukaryotes vs Prokaryotes:
Genes
Eukaryotic genes
• Are larger and more complex than in prokaryotes
• Contain introns that are “spliced” out to generate mature mRNAs*
• Often undergo alternative splicing, giving rise to multiple RNAs*
• Are transcribed by 3 different RNA polymerases
(instead of 1, as in prokaryotes)
* In biology, statements such as this include an implicit “usually” or “often”
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Eukaryotes vs Prokaryotes:
Levels of Gene Regulation
Primary level of control?
• Prokaryotes: Transcription initiation
• Eukaryotes: Transcription is also very important, but
• Expression is regulated at multiple levels
many of which are post-transcriptional:
•
•
•
•
•
RNA processing, transport, stability
Translation initiation
Protein processing, transport, stability
Post-translational modification (PTM)
Subcellular localization
Recent important discoveries: small regulatory RNAs (miRNA, siRNA)
are abundant and play very important roles in controlling gene
expression in eukaryotes, often at post-transcriptional levels
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Eukaryotes vs Prokaryotes:
Regulatory Elements
• Prokaryotes:
• Promoters & operators (for operons) - cis-acting DNA signals
• Activators & repressors - trans-acting proteins
(we won't discuss these…)
• Eukaryotes:
• Promoters & enhancers (for single genes) - cis-acting
•Transcription factors - trans-acting
• Important difference?
•What the RNA polymerase actually binds
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Prokaryotic Promoters
• RNA polymerase complex recognizes promoter sequences located
very close to and on 5’ side (“upstream”) of tansription initiation site
• Prokaryotic RNA polymerase complex binds directly to promoter,
by virtue of its sigma subunit - no requirement for “transcription
factors” binding first
• Prokaryotic promoter sequences are highly conserved:
• -10 region
• -35 region
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Eukaryotic Promoters
• Eukaryotic RNA polymerase complexes do not bind directly to
promoter sequences
• Transcription factors must bind first and serve as landmarks
recognized by RNA polymerase complexes
• Eukaryotic promoter sequences are less highly conserved, but many
promoters (for RNA polymerase II) contain :
• -30 region "TATA" box
• -100 region "CCAAT" box
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Eukaryotic Promoters vs Enhancers
Both promoters & enhancers are binding sites for transcription
factors (TFs)
•
Promoters
• essential for initiation of transcription
• located “relatively” close to start site (usually <200 bp upstream,
but can be located within gene, rather than upstream!)
•
Enhancers
• needed for regulated transcription (differential expression in
specific cell types, developmental stages, in response to environment,
etc.)
• can be very far from start site (sometimes > 100 kb)
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Eukaryotic genes are transcribed by
3 different RNA polymerases
(Location of promoter regions, TFBSs & TFs differ, too)
rRNA
mRNA
tRNA, 5S RNA
Brown Fig 9.18
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Prokaryotic Genes & Operons
•
Genes with related functions are often clustered within operons
(e.g., lac operon)
•
Operons = genes with related functions that are transcribed and
regulated as a single unit; one promoter controls expression of
several proteins
•
mRNAs produced from operons are “polycistronic” - a single mRNA
encodes several proteins; i.e., there are multiple ORFs, each with
its own AUG (START) & STOP codons, linked within one mRNA
molecule
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Promoter of lac operon in E. coli
(Transcribed by prokaryotic RNA polymerase)
Brown Fig 9.17
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Eukaryotic genes
• Genes with related functions are occasionally, but not usually
clustered; instead, they share common regulatory regions
(promoters, enhancers, etc.)
• Chromatin structure must also be “active” for transcription to
occur
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Eukaryotic genes have large & complex
regulatory regions
•Cis-acting regulatory elements include:
Promoters, enhancers, silencers
•Trans-acting regulatory factors include:
Transcription factors (TFs), chromatin
remodeling complexes, small RNAs
Brown Fig 9.17
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Eukaryotic Promoters: DNA sequences required
for initiation, usually <200 bp from start site
Eukaryotic RNA polymerases bind by recognizing a complex of
TFs bound at promotor
First, TFs must bind
short motifs (TFBSs)
within promoters;
then RNA polymerase
can bind and initiate
transcription of RNA
~250 bp
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Pre-mRNA
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Eukaryotic promoters & enhancer regions
often contain many different TFBS motifs
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
Fig 9.13
Mount 2004
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Simplified View of Promoters in Eukaryotes
Fig 5.12
Baxevanis &
Ouellette 2005
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Eukaryotic Activators
vs
Repressors
Regions far from the promoter can act as "enhancers" or "repressors"
of transcription by serving as binding sites for activator or repressor
proteins (TFs)
RNAP
promoter
enhancer
repressor
100 - 50,000 bp
Activator proteins (TFs)
bind to enhancers &
interact with RNAP to
stimulate transcription
Gene
enhancer proteins
interact with RNAP
transcription
repressor prevents
binding of activator
Repressors block the
action of activators
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Eukaryotic Transcription Factors (TFs)
• Transcription factors = proteins that interact with
the RNA polymerase complex to activate or repress
transcription
• TFs often contain both:
• a trans-activating domain
• a DNA binding domain or motif
Here motif = amino acid
sequence in protein
• TFs recognize and bind specific short DNA sequence motifs
called “transcription factor binding sites” (TFBSs)
• Databases for TFs &TFBSs include:
• TRANSFAC,
• JASPAR
Here motif = nucleotide
sequence in DNA
http://www.generegulation.com/cgibin/pub/databases/transfac
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Zinc Finger Proteins - Transcription Factors
• Common in eukaryotic proteins
• ~ 1% of mammalian genes encode
zinc-finger proteins (ZFPs)
• 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 - one clinical trial
will begin soon!
Brown Fig 9.12
• Did you go to Dave Segal's seminar?
• Your TAs Pete & Jeff work on
designing better ZFPs!
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Promoter Prediction Algorithms & Software
Xiong -
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Eukaryotes vs Prokaryotes:
Promoter Prediction
Promoter prediction is much easier in prokaryotes
Why?
Highly conserved
Simpler gene structures
More sequenced genomes!
(for comparative approaches)
Methods? Previously: mostly HMM-based
Now: similarity-based comparative methods
because so many genomes available
Xiong textbook:
1) "Manual method"= rules of Wang et al (see text)
2) BPROM - uses linear discriminant function
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Eukaryotes vs Prokaryotes:
Promoter Prediction
Promoter prediction is much easier in prokaryotes
Why?
Highly conserved
Simpler gene structures
More sequenced genomes!
(for comparative approaches)
Methods? Previously: mostly HMM-based
Now: similarity-based comparative methods
because so many genomes available
Xiong textbook:
1) "Manual method"= rules of Wang et al (see text)
2) BPROM - uses linear discriminant function
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Predicting Promoters in Eukaryotes
 Closely
related to gene prediction!
• Obtain genomic sequence
• Use sequence-similarity based comparison
(BLAST, MSA) to find related genes
But: "regulatory" regions are much less wellconserved than coding regions
• Locate ORFs
• Identify Transcription Start Site (TSS)
(if possible!)
• Use Promoter Prediction Programs
• Analyze motifs, etc. in DNA sequence (TRANSFAC, JASPAR)
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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 FirstEF
found within UCSC Genome Browser
or submit to FirstEF web server
Fig 5.10
Baxevanis &
Ouellette 2005
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Automated Promoter Prediction Strategies
1) Pattern-driven algorithms (ab initio)
2) Sequence-driven algorithms (homology based)
3) Combined "evidence-based"
BEST RESULTS? Combined, sequential
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1) Pattern-driven Algorithms
•
Success depends on availability of collections of annotated
transcription factor binding sites (TFBSs)
Tend to produce very large numbers of false positives (FPs)
•
Why?
•
•
•
•
•
•
Binding sites for specific TFs are often variable
Binding sites are short (typically 6-10 bp)
Interactions between TFs (& other proteins) influence both
affinity & specificity of TF binding
One binding site often recognized by multiple TFs
Biology is complex: gene activation is often specific to
organism/cell/stage/environmental condition; promoter and
enhancer elements must mediate this
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Ways to Reduce FPs in ab initio Prediction
•
Take sequence context/biology into account
Eukaryotes: clusters of TFBSs are common
Prokaryotes: knowledge of  (sigma) factors helps
•
Probability of "real" binding site higher if annotated transcription
start site (TSS) is nearby
But: What about enhancers? (no TSS nearby!)
& only a small fraction of TSSs have been experimentally
determinined
•
Do the wet lab experiments!
But: Promoter-bashing can be tedious…
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2) Sequence-driven Algorithms
•
Assumption: Common functionality can be deduced from
sequence conservation (Homology)
•
Alignments of co-regulated genes should highlight elements
involved in regulation
Careful: How determine co-regulation?
1. Orthologous genes from difference species
2. Genes experimentally shown to be co-regulated
(using microarrays??)
Comparative promoter prediction:
1. Phylogenetic footprinting
2. Expression Profiling
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Phylogenetic Footprinting
•
•
Based on increasing availability of whole genome DNA sequences
from many different species
Selection of organisms for comparison is important
•
•
To reduce FPs, must extract non-coding sequences and then
align them; prediction depends on good alignment
•
•
•
not too close, not too far: good = human vs mouse
use MSA algorithms (e.g., CLUSTAL)
more sensitive methods
•
Gibbs sampling
•
Expectation Maximization (EM) methods
Examples of programs:
•
Consite, rVISTA, PromH(W), Bayes aligner, Footprinter
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Expression Profiling
•
•
Based on increasing availability of whole genome mRNA expression
data, esp., microarray data
High-throughput simultaneous monitoring of expression levels of
thousands of genes
•
Assumptions: (sometimes valid, sometimes NOT)
1.
2.
•
Drawbacks:
1.
2.
•
Co-expression implies co-regulation
Co-regulated genes share common regulatory elements
Signals are short & weak!
Requires Gibbs sampling or EM: e.g., MEME, AlignACE, Melina
Prediction depends on determining which genes are co-expressed usually by clustering - which an be error prone
Examples of programs:
• INCLUSive - combined microarray analysis & motif detection
• PhyloCon - combined phylo footprinting & expression profiling)
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Problems with Sequence-driven Algorithms
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 or inversions than change order of
functional elements
• If background conservation of entire region is high,
comparison is useless
• Not enough data (but Prokaryotes >>> Eukaryotes)
Complexity: many regulatory elements are not conserved
across species!
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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
• Other info
Fig 5.13
Baxevanis & Ouellette
2005
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Global Alignment of Human & Mouse Obese Gene
Promoters (200 bp upstream from TSS)
Fig 5.14
Baxevanis & Ouellette
2005
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Annotated Lists of Promoter Databases &
Promoter Prediction Software
•
URLs from Mount textbook:
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/v5/n4/full/nrg1315_fs.htm
•
URLs from Baxevanis & Ouellette textbook:
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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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/
Bottom line: this is a very "hot" area - new
software for computational prediction of gene
regulatory elements published every day!
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