Transcript Genomics 101 • DNA sequencing • Alignment • Gene identification • Gene expression • Genome evolution •…

Genomics 101
• DNA sequencing
• Alignment
• Gene identification
• Gene expression
• Genome evolution
•…
Next Few Topics
• Gene Recognition
Finding genes in DNA with computational methods
• Large-scale alignment & multiple alignment
Comparing whole genomes, or large families of genes
• Gene Expression and Regulation
Measuring the expression of many genes at a time
Finding elements in DNA that control the expression of genes
Gene Recognition
Credits for slides:
Marina Alexandersson
Lior Pachter
Serge Saxonov
Reading
• GENSCAN
• EasyGene
• SLAM
• Twinscan
Optional:
Chris Burge’s Thesis
Gene expression
DNA
CCTGAGCCAACTATTGATGAA
transcription
RNA
CCUGAGCCAACUAUUGAUGAA
translation
Protein
PEPTIDE
Gene structure
exon1
intron1
exon2
intron2
exon3
transcription
splicing
translation
exon = protein-coding
intron = non-coding
Codon:
A triplet of nucleotides
that is converted to one
amino acid
Where are the genes?
In humans:
~22,000 genes
~1.5% of human DNA
Finding Genes
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
Approaches to gene finding
• Homology
 BLAST, Procrustes.
• Ab initio
 Genscan, Genie, GeneID.
• Hybrids
 GenomeScan, GenieEST, Twinscan, SGP, ROSETTA,
CEM, TBLASTX, SLAM.
1.
Exploit the regular gene structure
Exon 1
5’
Start codon
ATG
Intron 1
Exon 2
Intron 2
Splice sites
Exon 3
3’
Stop codon
TAG/TGA/TAA
Next Exon:
Frame 0
Next Exon:
Frame 1
2.
Recognize “coding bias”
• Each exon can be in one of three frames
ag—gattacagattacagattaca—gtaag Frame 0
ag—gattacagattacagattaca—gtaag Frame 1
ag—gattacagattacagattaca—gtaag Frame 2
Frame of next exon depends on how many nucleotides are left over
from previous exon
• Codons “tag”, “tga”, and “taa” are STOP
 No STOP codon appears in-frame, until end of gene
 Absence of STOP is called open reading frame (ORF)
• Different codons appear with different frequencies—
coding bias
2.
Recognize “coding bias”
Amino Acid
Isoleucine
Leucine
Valine
Phenylalanine
Methionine
Cysteine
Alanine
Glycine
Proline
Threonine
Serine
Tyrosine
Tryptophan
Glutamine
Asparagine
Histidine
Glutamic acid
Aspartic acid
Lysine
Arginine
Stop codons Stop
SLC
I
L
V
F
M
C
A
G
P
T
S
Y
W
Q
N
H
E
D
K
R
DNA codons
ATT, ATC, ATA
CTT, CTC, CTA, CTG, TTA, TTG
GTT, GTC, GTA, GTG
TTT, TTC
ATG
TGT, TGC
GCT, GCC, GCA, GCG
GGT, GGC, GGA, GGG
CCT, CCC, CCA, CCG
ACT, ACC, ACA, ACG
TCT, TCC, TCA, TCG, AGT, AGC
TAT, TAC
TGG
CAA, CAG
AAT, AAC
CAT, CAC
GAA, GAG
GAT, GAC
AAA, AAG
CGT, CGC, CGA, CGG, AGA, AGG
TAA, TAG, TGA
Can map 61 non-stop codons to frequencies & take log-odds ratios
atg
caggtg
ggtgag
cagatg
ggtgag
cagttg
ggtgag
caggcc
ggtgag
tga
Biology of Splicing
(http://genes.mit.edu/chris/)
3.
Recognize splice sites
Donor: 7.9 bits
Acceptor: 9.4 bits
(Stephens & Schneider, 1996)
(http://www-lmmb.ncifcrf.gov/~toms/sequencelogo.html)
3.
Recognize splice sites
Donor site
5’
3’
Position
%
A
C
G
T
-8 … -2 -1
26
26
25
23
…
…
…
…
60
15
12
13
1
2
… 17
9 0 1
5 0 1
78 99 0
8 1 98
54
2
41
3
…
…
…
…
0
21
27
27
25
3.
Recognize splice sites
• WMM: weight matrix model = PSSM (Staden 1984)
• WAM: weight array model = 1st order Markov (Zhang & Marr 1993)
• MDD: maximal dependence decomposition (Burge & Karlin 1997)
 Decision-tree algorithm to take pairwise dependencies into account
• For each position I, calculate Si = ji2(Ci, Xj)
• Choose i* such that Si* is maximal and partition into two subsets, until
• No significant dependencies left, or
• Not enough sequences in subset
 Train separate WMM models for each subset
G5
G5G-1
G5G-1
A2
G5G-1
A2U6
not G5
G5
not G-1
G5G-1
not A2
G5G-1A2
not U6
All donor
splice sites
4.
Model the duration of regions
Hidden Markov Models for Gene Finding
Intergene
State
intergene
exon
First Exon
State
intron
Intron
State
exon
intron
exon
intergene
GTCAGATGAGCAAAGTAGACACTCCAGTAACGCGGTGAGTACATTAA
Hidden Markov Models for Gene Finding
Intergene
State
intergene
exon
First Exon
State
intron
Intron
State
exon
intron
exon
intergene
GTCAGATGAGCAAAGTAGACACTCCAGTAACGCGGTGAGTACATTAA
Duration HMM for Gene Finding
Duration Modeling
Introns: regular HMM states—geometric duration
Exons: special duration model
GENSCAN:
Chris BurgeVand
Sam Karlin, 1997
E0,0(i) = maxd=1…D { Prob[duration(E0,0)=d]aIntron0,E0,0
j=i-d+1…ieE0,0(xj) }
Best performing de novo gene finder
HMM with duration
for Exon states
where modeling
i is an admissible
exon-ending state,
D is restricted by the longest ORF
duration
T A A T A T G T C C A C GGG T A T T G AG C A T T G T A C A C GGGG T A T T G A G C A T G T A A T G A A
Exon1
Exon2
Exon3
HMM-based Gene Finders
• GENSCAN (Burge 1997)
 Big jump in accuracy of de novo gene finding
 Currently, one of the best
 HMM with duration modeling for Exon states
• FGENESH (Solovyev 1997)
 Currently one of the best
• HMMgene (Krogh 1997)
• GENIE (Kulp 1996)
• GENMARK (Borodovsky & McIninch 1993)
• VEIL (Henderson, Salzberg, & Fasman 1997)
Better way to do it: negative binomial
• EasyGene:
Prokaryotic
gene-finder
Larsen TS, Krogh A
• Negative binomial with n = 3
GENSCAN’s hidden weapon
• C+G content is correlated with:
 Gene content
 Mean exon length
 Mean intron length
(+)
(+)
(–)
• These quantities affect
parameters of model
• Solution
 Train parameters of model in four
different C+G content ranges!
Evaluation of Accuracy
TP
FP
TN
FN
TP
FN
TN
Actual
Predicted
Actual
No Coding / Coding
Predicted
Coding / No Coding
TP
FP
FN
TN
Sensitivity (SN)
Fraction of exons (coding nucleotides) whose boundaries are
predicted exactly (that are predicted as coding)
•Specificity (Sp)
Fraction of the predicted exons (coding nucleotides) that are
exactly correct (that are coding)
•Correlation
Coefficient (CC)
Combined measure of Sensitivity & Specificity
Range: -1 (always wrong)  +1 (always right)
(Slide by NF Samatova)
Results of GENSCAN
• On the initial test dataset (Burset & Guigo)
 80% exact exon detection
• 10% partial exons
• 10% wrong exons
• In general
 HMMs have been best in de novo prediction
 In practice they overpredict human genes by ~2x