1. Genomics 101 DNA sequencing Alignment Gene identification
Gene expression
Genome evolution …
In this course a lot of our focus will be in biological sequences, and especially DNA, which is the topic of genomics and really the key to understanding life at the molecular level.
An average human is composed of trillions of cells, with some small variations across humans.
Same for our close relatives.
Each cell contains a nucleus, which has a copy of our entire DNA….

2. 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

3. Gene Recognition
Credits for slides: Marina Alexandersson Lior Pachter Serge Saxonov

4. Reading
GENSCAN
EasyGene
SLAM
Twinscan
Optional:
Chris Burge’s Thesis

5. Gene expression DNA RNA Protein PEPTIDE transcription translation
CCTGAGCCAACTATTGATGAA
CCUGAGCCAACUAUUGAUGAA
But first some quick genetics to make sure that we all are on the same page. The genes are expressed by the DNA in our chromosomes being transcribed into RNA. Basically an enzyme attaches to the DNA molecule and copies it, creating a RNA molecule. Then the RNA is translated in triplets into amin acids, creating a peptide, which in its finished form becomes a protein. The protein is the end product of the gene, and thus the expression of the genes.
PEPTIDE

6. Gene structure transcription splicing translation
intron1
intron2
exon1
exon2
exon3
transcription
splicing
The genes themselves are structured in coding bits, that is the stuff that becomes amino acids, called exons, and non-coding stretches of sequence in between, called introns. When the gene is transcribed the whole thing becomes an RNA molecule, including the garbage in between the exons, and then these introns are cut out in a process called splicing. The resulting bits are glued together and translated into a protein.
translation
Codon:
A triplet of nucleotides that is converted to one amino acid
exon = protein-coding
intron = non-coding

7. Where are the genes?

8. In humans:
~22,000 genes
~1.5% of human DNA

9. Finding Genes Exploit the regular gene structure
ATG—Exon1—Intron1—Exon2—…—ExonN—STOP
Recognize “coding bias”
CAG-CGA-GAC-TAT-TTA-GAT-AAC-ACA-CAT-GAA-…
Recognize splice sites
Intron—cAGt—Exon—gGTgag—Intron
Model the duration of regions
Introns tend to be much longer than exons, in mammals
Exons are biased to have a given minimum length
Use cross-species comparison
Gene structure is conserved in mammals
Exons are more similar (~85%) than introns

10. Approaches to gene finding
Homology
BLAST, Procrustes.
Ab initio
Genscan, Genie, GeneID.
Hybrids
GenomeScan, GenieEST, Twinscan, SGP, ROSETTA, CEM, TBLASTX, SLAM.
Annotating features of biological importance is relatively straightforward for organisms with compact genomes such as bacteria and yeast, because exons tend to be large and the introns small or non-existent. In large genomes as for mammals the coding signal is scattered in a vast sea of non-coding noise.
The crudest, yet often most reliable approach has been to manually look for exons by integrating and filtering various sources of homology information. This is based on the fact that exons and regulatory regions tend to be more strongly conserved by evolution than random genomic sequences. The tool that is frequently used is called BLAST.
Procrustes is a program that attempts to integrate BLAST based gene finding with ‘ab initio methods'.
Ab inito approaches, such as the following directly use only information about the input sequence itself to identify likely splice sites and to detect differences in
sequence composition between coding and non-coding DNA. One of the best of this sort is GENSCAN, which uses a Hidden Markov Model to scan large genomic sequences.
Then there are various forms of hybrids. Here we mean software that takes extra information from a second sequence, or database, in some sense.
GenomeScan and GenieEST, uses protein and EST databases respectively to confirm the potential exons. Twinscan and SGP both have a single species algorithm as base, take two sequences as input and use the second to boost the exon probability if the alignment is good. ROSETTA is the first cross-species gene finder, using a anchor based approach to determine homologous exon, and then a heuristic scoring scheme for the final prediction. CEM is similar to ROSETTA. TBLASTX is another variant of BLAST taking a nucleotide query and a nucleotide database as input, but translating both the query and the database into peptides in all reading frames before searching for matches. SLAM uses a generalized pair HMM, that is a merging of the Genscan HMM and a pair HMM, usually used for alignments.

11. 1. Exploit the regular gene structure5’ 3’
Exon 1
Exon 2
Exon 3
Intron 1
Intron 2
Start codon
ATG
Stop codon
TAG/TGA/TAA
The problem of predicting genes means to give coordinates for the exon boundaries.
The first kind of information that prediction algorithms use, is the regular structure of a gene. Every gene starts with an ATG codon, and then exons alternate with introns; at the exon-intron boundaries, the splice sites, there are short words that are approximately preserved.
Splice sites

12. Next Exon:
Frame 0
Next Exon:
Frame 1

13. 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

14. 2. Recognize “coding bias”
Amino Acid SLC DNA codons
Isoleucine I ATT, ATC, ATA
Leucine L CTT, CTC, CTA, CTG, TTA, TTG
Valine V GTT, GTC, GTA, GTG
Phenylalanine F TTT, TTC
Methionine M ATG
Cysteine C TGT, TGC
Alanine A GCT, GCC, GCA, GCG
Glycine G GGT, GGC, GGA, GGG
Proline P CCT, CCC, CCA, CCG
Threonine T ACT, ACC, ACA, ACG
Serine S TCT, TCC, TCA, TCG, AGT, AGC
Tyrosine Y TAT, TAC
Tryptophan W TGG
Glutamine Q CAA, CAG
Asparagine N AAT, AAC
Histidine H CAT, CAC
Glutamic acid E GAA, GAG
Aspartic acid D GAT, GAC
Lysine K AAA, AAG
Arginine R CGT, CGC, CGA, CGG, AGA, AGG
Stop codons Stop TAA, TAG, TGA
Can map 61 non-stop codons to frequencies & take log-odds ratios

15. atg
caggtg
ggtgag
cagatg
ggtgag
cagttg
ggtgag
caggcc
ggtgag
tga

16. Biology of Splicing (

17. 3. Recognize splice sites
Donor: 7.9 bits
Acceptor: 9.4 bits
(Stephens & Schneider, 1996)
How much info?
Find branch sites (

18. 3. Recognize splice sites
Donor site 5’ 3’
Position %

19. 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
All donor
splice sites
not G5 G5
not G-1
G5G-1
not A2
G5G-1A2
not U6

20. 4. Model the duration of regions
Why the algorithm has to be generalized is because in a standard HMM the output in each step would be one base, leading to state durations of geometric length. If we look at empirical data, such as these plots, we see that the intron lengths seem to follow the geometric distribution fairly well, but for the exon that would be a pretty bad model. So in our state space the exons are generalized states, choosing a length from a general distribution and outputting the entire exon in one step, while the intron and intergene states still output one base at a time and follow the geometric distribution.

21. Hidden Markov Models for Gene Finding
First Exon State
Intergene State
Intron
State
exon
intron
intergene
And that’s what we want to do when predicting genes. A Markov chain is a random process where the next step only depends on where you’re at at the moment. For the dice, which die to role next only depended on which you just roled and not on the history.
A hidden Markov model simply means that you don’t observe the state sequence, the sequence of roled dice, directly, but something depending on it, and so the state sequence is hidden from you.
In the gene finding context you observe the DNA sequence and the state sequence you want to determine consists of exons, introns and intergene states. That is you want to determine for each DNA base which state it belongs to, and thus predict the exon boundaries.
GTCAGATGAGCAAAGTAGACACTCCAGTAACGCGGTGAGTACATTAA

22. Hidden Markov Models for Gene Finding
First Exon State
Intergene State
Intron
State
exon
intron
intergene
And that’s what we want to do when predicting genes. A Markov chain is a random process where the next step only depends on where you’re at at the moment. For the dice, which die to role next only depended on which you just roled and not on the history.
A hidden Markov model simply means that you don’t observe the state sequence, the sequence of roled dice, directly, but something depending on it, and so the state sequence is hidden from you.
In the gene finding context you observe the DNA sequence and the state sequence you want to determine consists of exons, introns and intergene states. That is you want to determine for each DNA base which state it belongs to, and thus predict the exon boundaries.
GTCAGATGAGCAAAGTAGACACTCCAGTAACGCGGTGAGTACATTAA

23. Duration HMM for Gene Finding
Duration Modeling
Introns: regular HMM states—geometric duration
Exons: special duration model
VE0,0(i) = maxd=1…D { Prob[duration(E0,0)=d]aIntron0,E0,0
j=i-d+1…ieE0,0(xj) }
where i is an admissible exon-ending state,
D is restricted by the longest ORF
GENSCAN:
Chris Burge and Sam Karlin, 1997
Best performing de novo gene finder
HMM with duration modeling for Exon states
This is the state space of the Generalized HMM used by for instance Genscan and Genie, performing single species gene finding. The hidden states are the exons in red and the introns and intergene in green. The reason to why we have so many states is because the sequence is translated into protein in triplets, so there are three different ways to translate the same sequence, or three different reading frames. The end product has to be a sequence divisible by three, and if one exon ends in the middle of a codon, that codon has to be finished in the beginning of the next codon. Thus in each exon we would have to remember the number of extra bases in the previous exon, that is two states ago, which violates the Markov model assumption that the transition to the next state only depends on the current. If we instead have one intron for each reading frame, we so to speak bring the information with us, preserving the Markov property.
duration
Exon1
Exon2
Exon3 T A G C

24. 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)

25. Better way to do it: negative binomial
EasyGene:
Prokaryotic
gene-finder
Larsen TS, Krogh A
Negative binomial with n = 3
Non-parametric duration density is too expensive
Better to use some alternative density, such as negative binomial

26. 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!

27. Evaluation of Accuracy
TP FP TN FN TP FN TN
Actual
Predicted TN FN FP TP
Predicted
Actual
No Coding / Coding
Coding / No Coding
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)

28. 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