BCB 444/544
Lecture 27
Gene Prediction II
#27_Oct24
BCB 444/544 F07 ISU Dobbs #27 - Gene Prediction II
10/24/07
1
Required Reading
(before lecture)
Mon Oct 22 - Lecture 26
Gene Prediction
• Chp 8 - pp 97 - 112
Wed Oct 24 - Lecture 27
(will not be covered on Exam 2)
Promoter & Regulatory Element Prediction
• Chp 9 - pp 113 - 126
Thurs Oct 25 - Review Session & Project Planning
Fri Oct 26 - EXAM 2
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Assignments & Announcements
Mon Oct 22 - Study Guide for Exam 2 was posted, finally…
Mon Oct 22 - HW#4 Due
(no "correct" answer to post)
Thu Oct 25 - no Lab => Optional Review Session for Exam
544 Project Planning/Consult with DD & MT
Fri Oct 26 - Exam 2 - Will cover:
•
•
•
•
Lectures 13-26 (thru Mon Sept 17)
Labs 5-8
HW# 3 & 4
All assigned reading:
Chps 6 (beginning with HMMs), 7-8, 12-16
Eddy: What is an HMM
Ginalski: Practical Lessons…
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BCB 544 "Team" Projects
• 544 Extra HW#2 is next step in Team Projects
•
•
•
•
Write ~ 1 page outline
Schedule meeting with Michael & Drena to discuss topic
Read a few papers
Write a more detailed plan
• You may work alone if you prefer
• Last week of classes will be devoted to Projects
• Written reports due: Mon Dec 3 (no class that day)
• Oral presentations (15-20') 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 (posted online Thurs?)
No - sorry! sent by email on Sat…
Due:
PART 1 - ASAP
PART 2 - Fri Nov 2 by 5 PM
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
• Oct 25 Thur - BBMB Seminar 4:10 in 1414 MBB
•
Dave Segal
UC Davis
Zinc Finger Protein Design
• Oct 19 Fri - BCB Faculty Seminar 2:10 in 102 ScI
• Guang Song ComS, ISU Probing functional mechanisms
by structure-based modeling and simulations
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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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What is a Gene?
What is a gene? segment of DNA, some of which is
"structural," i.e., transcribed to give a functional RNA
product, & some of which is "regulatory"
• Genes can encode:
• mRNA (for protein)
• other types of RNA (tRNA, rRNA, miRNA, etc.)
• Genes differ in eukaryotes vs prokaryotes (& archaea),
both structure & regulation
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Synthesis & Processing of Eukaryotic mRNA
DN
Gene in DNA
5’ exon 1
3’
intron
1' transcript (RNA)
exon 2
3’
exon 3 5’
intron
Transcription
5’
3’
Splicing (remove introns)
3’
5’
Mature mRNA
5’ 7MeG
Capping & polyadenylation
AAAAA 3’
m
Export to cytoplasm
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What are cDNAs & ESTs?
cDNA libraries are important for determining gene
structure & studying regulation of gene expression
•
Isolate RNA (always from a specific
organism, region, and time point)
• Convert RNA to complementary DNA
• (with reverse transcriptase)
• Clone into cDNA vector
• Sequence the cDNA inserts
• Short cDNAs are called ESTs or
Expressed Sequence Tags
ESTs are strong evidence for genes
insert
vector
• Full-length cDNAs can be difficult to obtain
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UniGene: Unique genes via ESTs
• Find UniGene at NCBI:
www.ncbi.nlm.nih.gov/UniGene
• UniGene clusters contain many ESTs
• UniGene data come from many cDNA libraries.
When you look up a gene in UniGene, you can
obtain information re: level & tissue
distribution of expression
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Gene Prediction in Prokaryotes vs Eukaryotes
Eukaryotes
• Large genomes 107 – 1010 bp
• Often less than 2% coding
• Complicated gene structure
(splicing, long exons)
• Prediction success 50-95%
• Small genomes 0.5 - 10·106 bp
• About 90% of genome is
coding
• Simple gene structure
• Prediction success ~99%
Splice sites
ATG
Prokaryotes
TAA
5’ UTR
3’ UTR
Promotor
Exons
Start codon
Stop codon
ATG
TAA
Promotor Open reading frame (ORF)
Introns
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Prediction is Easier in Microbial Genomes
Why?
Smaller genomes
Simpler gene structures
Many more sequenced genomes!
(for comparative approaches)
Many microbial genomes have been fully sequenced &
whole-genome "gene structure" and "gene function"
annotations are available
e.g., GeneMark.hmm, Glimmer
TIGR Comprehensive Microbial Resource (CMR)
NCBI Microbial Genomes
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Gene Prediction - The Problem
Problem:
Given a new genomic DNA sequence, identify coding regions
and their predicted RNA and protein sequences
ATTACCATGGGGCAGGGTCAGATATAATGCCCTCATTTT
ATTACCATGGGGCAGGGTCAGATATAATGCCCTCATTTT
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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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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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Gene Prediction Strategies
What sequence signals can be used?
• Transcription: TF binding sites, promoter, initiation site, terminator,
GC islands, etc.
• Processing signals: Splice donor/acceptors, polyA signal
• Translation: Start (AUG = Met) & stop (UGA,UUA, UAG)
ORFs, codon usage
What other types of information can be used?
• Homology (sequence comparison, BLAST)
• cDNAs & ESTs (experimental data, pairwise alignment)
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Signals Search
Approach: Build models (PSSMs, profiles, HMMs, …) and search
against DNA. Detected instances provide evidence for genes
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DNA Signals Used in Gene Prediction
1.
Exploit the regular gene structure
ATG—Exon1—Intron1—Exon2—…—ExonN—STOP
2.
Recognize “coding bias”
CAG-CGA-GAC-TAT-TTA-GAT-AAC-ACA-CAT-GAA-…
3.
Recognize splice sites
Intron—cAGt—Exon—gGTgag—Intron
4.
Model the duration of regions
Introns tend to be much longer than exons, in mammals
Exons are biased to have a given minimum length
5.
Use cross-species comparison
Gene structure is conserved in mammals
Exons are more similar (~85%) than introns
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Content Search
Observation: Encoding a protein affects statistical properties
of DNA sequence:
• Nucleotide composition
• Hexamer frequency
• GC content (CpG islands, exon/intron)
• Uneven usage of synonymous codons (codon bias)
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
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
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
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
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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Gene Prediction Flowchart
Fig 5.15
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Baxevanis & Ouellette 2005
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Predicting Genes - Basic steps:
• Obtain genomic sequence
• BLAST it!
• Perform database similarity search
(with EST & cDNA databases, if available)
• Translate in all 6 reading frames
(i.e., "6-frame translation")
• Compare with protein sequence databases
•
•
•
•
Use Gene Prediction software to locate genes
Compare results obtained using different programs
Analyze regulatory sequences, too
Refine gene prediction
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Predicting Genes - a few Details:
1. 1st, mask to "remove" repetitive elements (ALUs, etc.)
2. Perform database search on translated DNA
(BlastX,TFasta)
3. Use several programs to predict genes & find ORFs
(GENSCAN, GeneSeqer, GeneMark.hmm, GRAIL)
4. Search for functional motifs in translated ORFs & in
neighboring DNA sequences (InterPro, Transfac)
5. Repeat
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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
Genomic Sequence
Fast Search
Spliced Alignment
EST or protein database
(Suffix Array/Suffix Tree)
Output
Brendel 2005
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Assembly
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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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Signals: Pre-mRNA Splicing
Start codon
Stop codon
Genomic DNA
Transcription
pre-mRNA
Cap-
-Poly(A)
Splicing
mRNA
-Poly(A)
Cap-
Translation
Protein
EXON
INTRON
GT
AG
Acceptor
site
Donor site
Splice sites
Brendel 2005
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Brendel - Spliced Alignment I:
Compare with cDNA or EST probes
Start codon
Stop codon
Genomic DNA
Start codon
mRNA
-Poly(A)
Cap5’-UTR
Brendel 2005
Stop codon
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3’-UTR
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Brendel - Spliced Alignment II:
Compare with protein probes
Start codon
Stop codon
Genomic DNA
Protein
Brendel 2005
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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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Donor (GT) & Acceptor (AG) Sites
Used for Model Training
Species
Brendel 2005
Type
Number of True Splice Sites / Phase
1
2
3
Home sapiens
GT
AG
6586
6555
5277
5194
3037
2979
Mus musculus
GT
AG
1212
1194
1185
1139
521
504
Rattus norvegicus
GT
AG
450
442
408
386
147
140
Gallus gallus
GT
AG
288
284
238
228
107
103
Drosophila
GT
AG
989
1001
670
671
524
536
C. elegans
GT
AG
37029
36864
20500
20325
20789
20626
S. pombe
GT
AG
170
179
118
122
119
118
Aspergillus
GT
AG
221
217
176
172
157
163
Arabidopsis thaliana
GT
AG
23019
22929
9297
9247
8653
8611
Zea mays
GT
AG
316
311
107
104
88
83
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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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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
/ AP
 11 
• Sensitivity: S n SnTP
• 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
trivially achieved 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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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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GenSeqer Performance?

1.00
Human
GT site
0.80
Sn
0.60
-10 -8
-6 -4
0.20
0.20
4
6
Sn
0.60
0.40
2
8
10 12 14 16 18 20
-10 -8
-6 -4
0.00
-2 0
2
4
6
8

1.00
0.80
Sn
0.60
-10 -8
•
•
•
-6 -4
A. thaliana
GT site
0.80
0.20
0.20
4
6
8
10 12 14 16 18 20
Sn
0.60
0.40
2
10 12 14 16 18 20

1.00
0.40
0.00
-2 0
Human
AG site
0.80
0.40
0.00
-2 0

1.00
-10 -8
-6 -4
0.00
-2 0
2
4
6
8
A. thaliana
AG site
10 12 14 16 18 20
Plots such as these (& ROCs) are much better than using a "single
number" to compare different methods
Such plots illustrate trade-off: Sn vs Sp
Note: the above are not ROC curves (plots of Sn vs 1-Sp)
Brendel 2005
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GeneSeqer Results on Different Genomes
Species
Homo sapiens
Drosophila
C. elegans
A. thaliana
Brendel 2005
Model
2C
2C
7C
7C
Site
Test Site Set
True
False
GT
921
44411
AG
920
65103
GT
329
11501
AG
329
14920
GT
400
7460
AG
400
10132
GT
613
9027
AG
614
10196
Bayes
Factor
Sn

Sp
(%)
(%)
(%)
0
3
6
0
3
6
98.5
91.7
66.3
96.3
90.3
76.1
90.5
96.3
98.5
88.4
92.9
96.1
16.4
34.8
57.6
9.7
15.7
25.6
0
3
6
0
3
6
95.4
90.0
83.9
95.7
92.1
85.1
94.8
97.6
99.1
94.8
97.0
98.5
34.1
53.6
75.0
28.7
41.4
59.4
0
3
6
0
3
6
97.8
94.2
84.8
98.8
96.2
90.2
92.7
97.1
99.1
97.2
98.8
99.5
40.4
64.3
85.4
58.2
76.9
88.5
0
3
6
0
3
6
99.5
95.6
87.1
99.2
96.4
87.1
93.2
97.6
99.3
92.3
96.4
98.6
48.1
73.2
91.0
41.9
62.0
81.2
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Performance of GeneSeqer vs Others?
• Comparison with ab initio gene prediction:
vs GENSCAN an HMM-based ab initio method
• "Winner" depends on:
• Availability of ESTs
• Level of similarity to protein homologs
Brendel 2005
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GeneSeqer
vs
GENSCAN
Exon (Sn + Sp) / 2
(Exon prediction)
1.00
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
GeneSeqer
NAP
GENSCAN
0
10 20 30 40 50 60 70 80 90 100
Target protein alignment score
GENSCAN - Burge, MIT
Brendel 2005
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GeneSeqer vs
GENSCAN
Intron (Sn + Sp) / 2
(Intron prediction)
1.00
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
GeneSeqer
NAP
GENSCAN
0 10 20 30 40 50 60 70 80 90 100
Target protein alignment score
GENSCAN - Burge, MIT
Brendel 2005
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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 untron/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
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•
•
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Similarity-based
•
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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, 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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