1. Computational Gene Finding
Sanja Rogic
CS Department UBC
Jan 23, 2003
Computational Gene Finding

2. What is Computational Gene Finding?
Given an uncharacterized DNA sequence, find out:
Which region codes for a protein?
Which DNA strand is used to encode the gene?
Which reading frame is used in that strand?
Where does the gene starts and ends?
Where are the exon-intron boundaries in eukaryotes?
(optionally) Where are the regulatory sequences for that gene?
Jan 23, 2003
Computational Gene Finding

3. Prokaryotic Vs. Eukaryotic Gene Finding
Prokaryotes:
small genomes 0.5 – 10·106 bp
high coding density (>90%)
no introns
Gene identification relatively easy, with success rate ~ 99%
Problems:
overlapping ORFs
short genes
finding TSS and promoters
Eukaryotes:
large genomes 107 – 1010 bp
low coding density (<50%)
intron/exon structure
Gene identification a complex problem, gene level accuracy ~50%
Problems:
many
Jan 23, 2003
Computational Gene Finding

4. Computational Gene Finding
Gene Structure
Jan 23, 2003
Computational Gene Finding

5. Gene Finding: Different Approaches
Similarity-based methods (extrinsic) - use similarity to annotated sequences:
proteins
cDNAs
ESTs
Comparative genomics - Aligning genomic sequences from different species
Ab initio gene-finding (intrinsic)
Integrated approaches
Jan 23, 2003
Computational Gene Finding

6. Similarity-based methods
Based on sequence conservation due to functional constraints
Use local alignment tools (Smith-Waterman algo, BLAST, FASTA) to search protein, cDNA, and EST databases
Will not identify genes that code for proteins not already in databases (can identify ~50% new genes)
Limits of the regions of similarity not well defined
-protein: databases SwissProt or PIR///cannot delimit UTRs////not all domains present
cDNA///most relevant for determining gene structure/// best if derived from the same organism////
-EST:poor sequence quality///chimeric///contamination with primers///wrong orientation -
Jan 23, 2003
Computational Gene Finding

7. Computational Gene Finding
Comparative Genomics
Based on the assumption that coding sequences are more conserved than non-coding
Two approaches:
intra-genomic (gene families)
inter-genomic (cross-species)
Alignment of homologous regions
Difficult to define limits of higher similarity
Difficult to find optimal evolutionary distance (pattern of conservation differ between loci)
Jan 23, 2003
Computational Gene Finding

8. Computational Gene Finding
Jan 23, 2003
Computational Gene Finding

9. Summary for Extrinsic Approaches
Strengths:
Rely on accumulated pre-existing biological data, thus should produce biologically relevant predictions
Weaknesses:
Limited to pre-existing biological data
Errors in databases
Difficult to find limits of similarity
Jan 23, 2003
Computational Gene Finding

10. Ab initio Gene Finding, Part 1
Input: A DNA string over the alphabet {A,C,G,T}
Output: An annotation of the string showing for every nucleotide whether it is coding or non-coding
AAAGCATGCATTTAACGAGTGCATCAGGACTCCATACGTAATGCCG
Gene finder
AAAGC ATG CAT TTA ACG A GT GCATC AG GA CTC CAT ACG TAA TGCCG
Jan 23, 2003
Computational Gene Finding

11. Ab initio Gene Finding, Part 2
Using only sequence information
Identifying only coding exons of protein-coding genes (transcription start site, 5’ and 3’ UTRs are ignored)
Integrates coding statistics with signal detection
Jan 23, 2003
Computational Gene Finding

12. Coding Statistics, Part 1
Unequal usage of codons in the coding regions is a universal feature of the genomes
uneven usage of amino acids in existing proteins
uneven usage of synonymous codons (correlates with the abundance of corresponding tRNAs)
We can use this feature to differentiate between coding and non-coding regions of the genome
Coding statistics - a function that for a given DNA sequence computes a likelihood that the sequence is coding for a protein
Jan 23, 2003
Computational Gene Finding

13. Coding Statistics, Part 2
Many different ones
codon usage
hexamer usage
GC content
compositional bias between codon positions
nucleotide periodicity …
Hexamer usage shown to be most discriminative and majority of current algos are using it
Jan 23, 2003
Computational Gene Finding

14. An Example of Coding Statistics, Part 1
For each codon the table displays the frequency of usage of each codon (per thousand) in human (first column)
Relative frequency of each codon among synonymous codons (second column)
Jan 23, 2003
Computational Gene Finding

15. An Example of Coding Statistics, Part 2
Let F(c) be the frequency (probability) of codon c in the genes of the species under consideration
Given the sequence of codons C=c1c2…cm and assuming independence between adjacent codons:
P(C)=F(c1)F(c2)…F(cm)
is probability of finding C, knowing that C codes for protein
Example: S=AGGACC c1=AGG c2= ACC
P(S) = F(AGG)·F(ACC) = · 0.038=
Jan 23, 2003
Computational Gene Finding

16. An Example of Coding Statistics, Part 3
Let F0(c) be the frequency of codon c in a non-coding sequence.
P0 (C)=F0(c1)F0(c2)…F0(cm)
is the probability of finding C, knowing that C is non-coding
Assuming the random model of non-coding DNA, F0(c) = 1/64= for all codons
P0 (S) = · =
The log-likelihood (LP) ratio for S is:
LP(S) = log( / ) = log(3.43) = 0.53
LP(S) > S is coding
Jan 23, 2003
Computational Gene Finding

17. Computing Coding Statistics in Practice
Usually, the value of coding statistics is computed using sliding windows coding profile of the sequence
Larger windows are required to detect a clear signal (50 – 200 bp)
Sliding window = successive overlapping windows
Small exons might be missed
Jan 23, 2003
Computational Gene Finding

18. Coding Profile of ß-globin gene
Window size 120
Distance between overlapping windows 10
LP computed for all three reading frame
Jan 23, 2003
Computational Gene Finding

19. Computational Gene Finding
Signal Sensors, Part 1
Signal – a string of DNA recognized by the cellular machinery
Jan 23, 2003
Computational Gene Finding

20. Computational Gene Finding
Signal Sensors, Part 2
Various pattern recognition method are used for identification of these signals:
consensus sequences
weight matrices
weight arrays
decision trees
Hidden Markov Models (HMMs)
neural networks …
Jan 23, 2003
Computational Gene Finding

21. Example of Consensus Sequence
obtained by choosing the most frequent base at each position of the multiple alignment of subsequences of interest
TACGAT
TATAAT
GATACT
TATGAT
TATGTT
consensus sequence
consensus (IUPAC)
Leads to loss of information and can produce
many false positive or false negative predictions
TATAAT
MELON
MANGO
HONEY
SWEET
COOKY
IUPAC –set of symbols encoding each subset of four nucleotides
R – purine
N- any
TATRNT
MONEY
Jan 23, 2003
Computational Gene Finding

22. Example of (Positional) Weight Matrix
Computed by measuring the frequency of every element of every position of the site (weight)
Score for any putative site is the sum of the matrix values (converted in probabilities) for that sequence (log-likelihood score)
Disadvantages:
cut-off value required
assumes independence between adjacent bases
TACGAT
TATAAT
GATACT
TATGAT
TATGTT 1 2 3 4 5 6 A C G T
Jan 23, 2003
Computational Gene Finding

23. Example of Decision Tree
- model that captures most significant dependencies between the nucleotides
H is A, C or U
Jan 23, 2003
Computational Gene Finding

24. Computational Gene Finding
Markov Models
Collection of states that have transitional probabilities between them
The future state depends only on the present state and not on the past ones
Jan 23, 2003
Computational Gene Finding

25. Ingredients of a Markov Model
Collection of states
{S1, S2, …,SN}
State transition probabilities (transition matrix)
Aij = P(qt+1 = Si | qt = Sj)
Initial state distribution
i = P(q1 = Si)
Jan 23, 2003
Computational Gene Finding

26. Ingredients of Our Markov Model
Collection of states
{Ssunny, Srainy, Ssnowy}
State transition probabilities (transition matrix)
A =
Initial state distribution
i = ( )
.08
.15
.05
.38 .6
.02
.75 .2
Jan 23, 2003
Computational Gene Finding

27. Probability of a Sequence of Events
P(Ssunny) x P(Srainy | Ssunny) x P(Srainy | Srainy) x
P(Srainy | Srainy) x P(Ssnowy | Srainy) x P(Ssnowy | Ssnowy)
= 0.7 x 0.15 x 0.6 x 0.6 x 0.02 x 0.2 =
This is a classification problem
Jan 23, 2003
Computational Gene Finding

28. Computational Gene Finding
Hidden Markov Models
Jan 23, 2003
Computational Gene Finding

29. Computational Gene Finding
Ingredients of a HMM
Collection of states: {S1, S2,…,SN}
State transition probabilities (transition matrix)
Aij = P(qt+1 = Si | qt = Sj)
Initial state distribution
i = P(q1 = Si)
Observations: {O1, O2,…,OM}
Observation probabilities:
Bj(k) = P(vt = Ok | qt = Sj)
Jan 23, 2003
Computational Gene Finding

30. Computational Gene Finding
Ingredients of Our HMM
States: {Ssunny, Srainy, Ssnowy}
State transition probabilities (transition matrix)
A =
Initial state distribution
i = ( )
Observations: {O1, O2,…,OM}
Observation probabilities (emission matrix): B =
.08
.15
.05
.38 .6
.02
.75 .2
.08
.15
.05
.38 .6
.02
.75 .2
Jan 23, 2003
Computational Gene Finding

31. Probability of a Sequence of Events
P(O) = P(Ogloves, Ogloves, Oumbrella,…, Oumbrella)
=  P(O | Q)P(Q) =  P(O | q1,…,q7)
= 0.7 x 0.86 x 0.32 x 0.14 x …
all Q
q1,…q7
Jan 23, 2003
Computational Gene Finding

32. Computational Gene Finding
Typical HMM Problems
Annotation Given a model M and an observed string S, what is the most probable path through M generating S
Classification Given a model M and an observed string S, what is the total probability of S under M
Consensus Given a model M, what is the string having the highest probability under M
Training Given a set of strings and a model structure, find transition and emission probabilities assigning high probabilities to the strings
Annotation – gene finding
Classification – signal sensor, content sensor
Annotation and classification problems can be solved efficiently using dynamic programming (Viterbi)
Jan 23, 2003
Computational Gene Finding

33. HMMs and Gene Structure
Nucleotides {A,C,G,T} are the observables
Different states generates generate nucleotides at different frequencies
A simple HMM for unspliced genes:
AAAGC ATG CAT TTA ACG AGA GCA CAA GGG CTC TAA TGCCG
The sequence of states is an annotation of the generated string – each nucleotide is generated in intergenic, start/stop, coding state
This HMM has 4 states: x- non-coding, c- coding, start and stop
Jan 23, 2003
Computational Gene Finding

34. State of the Art in Ab initio Gene Finding
Coding statistics and signal sensors are integrated in overall gene model using
machine learning techniques (HMMs, decision trees, neural networks)
discriminant functions (linear, quadratic)
Use dynamic programming (Viterbi) to find the highest scoring path through the model
Capable of predicting:
genes on both strands simultaneously
partial and multiple genes in a sequence
suboptimal exons
Number of potential gene models grows exponentially with the number of predicted exons
Jan 23, 2003
Computational Gene Finding

35. Examples of Gene Finders
FGENES – linear DF for content and signal sensors and DP for finding optimal combination of exons
GeneMark – HMMs enhanced with ribosomal binding site recognition
Genie – neural networks for splicing, HMMs for coding sensors, overall structure modeled by HMM
Genscan – WM, WA and decision trees as signal sensors, HMMs for content sensors, overall HMM
HMMgene – HMM trained using conditional maximum likelihood
Morgan – decision trees for exon classification, also Markov Models
MZEF – quadratic DF, predict only internal exons
Jan 23, 2003
Computational Gene Finding

36. Computational Gene Finding
Genscan Example
Developed by Chris Burge 1997
One of the most accurate ab initio programs
Uses explicit state duration HMM to model gene structure (different length distributions for exons)
Different model parameters for regions with different GC content
Also generalized
Jan 23, 2003
Computational Gene Finding

37. Computational Gene Finding
Einit
Eterm
3’ UTR
5’ UTR
Esngl
polyA P
forward strand N
backward strand
polyA P
5’ UTR
Esngl
3’ UTR
Einit
Eterm I0 I1 I2 E0 E1 E2
Jan 23, 2003
Computational Gene Finding

38. Genscan’s Architecture
HMM’s states for exons and introns in three different phases, single exon, 5’ and 3’ UTRs, promoter region and polyA site and intergenic region
Explicit length modeling
HMMs for exons, introns and intergenic regions
WM and WA for acceptor site, branch point, polyA site and promoter region
Decision tree (maximal dependence decomposition) for donor sites
Jan 23, 2003
Computational Gene Finding

39. Ab initio Gene Finding is Difficult
Genes are separated by large intergenic regions
Genes are not continuous, but split in a number of (small) coding exons, separated by (larger) non-coding introns
in humans coding sequence comprise only a few percents of the genome and an average of 5% of each gene
Sequence signals that are essential for elucidation of a gene structure are degenerate and highly unspecific
Alternative splicing
Repeat elements (>50% in humans) – some contain coding regions
It is almost impossible to distinguish between signals that are truly processed by the cell from those that are apparently non-functional
Jan 23, 2003
Computational Gene Finding

40. Problems with Ab initio Gene Finding
No biological evidence
In long genomic sequences many false positive predictions
Prediction accuracy high, but not sufficient
Jan 23, 2003
Computational Gene Finding

41. Evaluation of Gene Finding Programs
Calculating accuracy of programs’ predictions
Several evaluation studies:
Burset and Guigó, 1996 (vertebrate sequences)
Pavy et al., 1999 (Arabidopsis thaliana)
Rogic et al., 2001 (mammalian sequences)
Jan 23, 2003
Computational Gene Finding

42. Measures of Prediction Accuracy, Part 1
Nucleotide level accuracy
Sensitivity =
Specificity = TN FP FN TP
REALITY
PREDICTION
number of correct exons
number of actual exons
number of correct exons
number of predicted exons
Jan 23, 2003
Computational Gene Finding

43. Measures of Prediction Accuracy, Part 2
Exon level accuracy
WRONG EXON
CORRECT EXON
MISSING
EXON
REALITY
PREDICTION
Jan 23, 2003
Computational Gene Finding

44. Evaluation in Rogic et al., 2001
We developed a new dataset for this purposes – HMR195
Characteristics of the sequences:
human – mouse – rat origin
Relatively short DNA sequences from GenBank
one gene per sequence
sequences used for the training of the programs were excluded
Jan 23, 2003
Computational Gene Finding

45. Computational Gene Finding
More About HMR195
Filtering:
Canonical start and stop codon
No in-frame stop codons
Canonical splice site dinucleotides AG – GT
Redundancy filtering: similar sequences excluded
Confirming exon locations using mRNA alignment
Jan 23, 2003
Computational Gene Finding

46. Computational Gene Finding
Evaluation Results
Jan 23, 2003
Computational Gene Finding

47. Computational Gene Finding
Additional Testing
Accuracy as a function of sequence and prediction characteristics:
GC content
exon length
exon type
exon type and signal
exon probabilities and scores
phylogenetic specificity
Jan 23, 2003
Computational Gene Finding

48. Integrated Approaches for Gene Finding
Programs that integrate results of similarity searches with ab initio techniques (GenomeScan, FGENESH+, Procrustes)
Programs that use synteny between organisms (ROSETTA, SLAM)
Integration of programs predicting different elements of a gene (EuGène)
Combining predictions from several gene finding programs (combination of experts)
Jan 23, 2003
Computational Gene Finding

49. Combining Programs’ Predictions
Set of methods used and they way they are integrated differs between individual programs
Different programs often predict different elements of an actual gene
they could complement each other yielding better prediction
Jan 23, 2003
Computational Gene Finding

50. Computational Gene Finding
Related Work
This approach was suggested by several authors
Burset and Guigó (1996)
Investigated correlation between 9 gene-finding programs
99% of exons predicted by all programs were correct
1% of exons completely missed by all programs
Murakami and Tagaki (1998)
Five methods for combining the prediction by 4 gene-finding programs
Nucleotide level accuracy measures improved by 3-5% in comparison with the best single
Jan 23, 2003
Computational Gene Finding

51. Computational Gene Finding
AND and OR Methods
exon 1
exon 2
union
intersection
Jan 23, 2003
Computational Gene Finding

52. Combining Genscan and HMMgene
High prediction accuracy as well as reliability of their exon probability made them the best candidates for our study
Genscan predicted 77% of exons correctly, HMMgene 75%, both 87%
Genscan
111
624 91
HMMgene
Jan 23, 2003
Computational Gene Finding

53. Computational Gene Finding
Control Datasets
Burset/Guigó dataset – 570 vertebrate genomic sequences containing exactly one multi-exon gene
Multi-gene dataset – 22 human/murine sequences containing more than one gene
Jan 23, 2003
Computational Gene Finding

54. EUI Method (exon union – intersection)
Union of exons with p  0.75
Intersection of exons with p < 0.75
Rule for initial exon
Jan 23, 2003
Computational Gene Finding

55. GI Method (gene intersection)
Intersection of genes
Apply EUI method to exons completely belonging to GI genes
Jan 23, 2003
Computational Gene Finding

56. EUI_frame Method (EUI with reading frame consistency)
Assign probabilities to GI genes. Determine position of acceptor and donor site in a reading frame.
GI gene with higher probability imposes the reading frame. Choose only EUI exons contained in GI genes that are in a chosen reading frame.
Jan 23, 2003
Computational Gene Finding

57. Computational Gene Finding
Results – HMR dataset
Sp increased 3.2%, ESn increased 2.6%, ESp increased 11.7%
Number of wrong exons significantly decreased
Jan 23, 2003
Computational Gene Finding

58. Results – Burset/Guigó dataset
Similar to HMR195
Jan 23, 2003
Computational Gene Finding

59. Results – Drosophila Adh region
Sp increased 21%, ESn increased 6.8%, ESp increased 32.5%
Number of wrong exons decreased several-fold
Jan 23, 2003
Computational Gene Finding

60. Computational Gene Finding
Why Does It Work?
Jan 23, 2003
Computational Gene Finding

61. Computational Gene Finding
Thank you
Jan 23, 2003
Computational Gene Finding