1. Applied Bioinformatics
Week 6

2. Theoretical Part I
Gene Prediction

3. 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?
Sanja Rogic CS Department UBC
Computational Gene Finding

4. 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
Sanja Rogic CS Department UBC
Computational Gene Finding

5. Computational Gene Finding
Gene Structure
Sanja Rogic CS Department UBC
Computational Gene Finding

6. 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
Sanja Rogic CS Department UBC
Computational Gene Finding

7. 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 -
Sanja Rogic CS Department UBC
Computational Gene Finding

8. 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)
Sanja Rogic CS Department UBC
Computational Gene Finding

9. Computational Gene Finding
Sanja Rogic CS Department UBC
Computational Gene Finding

10. 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
Sanja Rogic CS Department UBC
Computational Gene Finding

11. 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
Sanja Rogic CS Department UBC
Computational Gene Finding

12. 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
Sanja Rogic CS Department UBC
Computational Gene Finding

13. 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
Sanja Rogic CS Department UBC
Computational Gene Finding

14. 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
Sanja Rogic CS Department UBC
Computational Gene Finding

15. 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)
Sanja Rogic CS Department UBC
Computational Gene Finding

16. 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
Sanja Rogic CS Department UBC
Computational Gene Finding

17. Coding Profile of ß-globin gene
Window size 120
Distance between overlapping windows 10
LP computed for all three reading frame
Sanja Rogic CS Department UBC
Computational Gene Finding

18. Computational Gene Finding
Signal Sensors, Part 1
Signal – a string of DNA recognized by the cellular machinery
Sanja Rogic CS Department UBC
Computational Gene Finding

19. 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 …
Sanja Rogic CS Department UBC
Computational Gene Finding

20. 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
Sanja Rogic CS Department UBC
Computational Gene Finding

21. 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
Sanja Rogic CS Department UBC
Computational Gene Finding

22. 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
Sanja Rogic CS Department UBC
Computational Gene Finding

23. 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
Sanja Rogic CS Department UBC
Computational Gene Finding

24. Problems with Ab initio Gene Finding
No biological evidence
In long genomic sequences many false positive predictions
Prediction accuracy high, but not sufficient
Sanja Rogic CS Department UBC
Computational Gene Finding

25. End Theory I
Mind mapping
10 min break

26. Practice I

27. Prokaryotic DNA Finding protein coding regions Finding ORFs
Goto NCBI and find the entry for
M68521, gi|147118
Get the FASTA sequence
Keep the gene bank entry visible

28. GeneMark http://exon.gatech.edu/GeneMark
Paste the sequence and run the prediction
Compare the gene bank entry and the predicted sequence
Which one would you trust, and why?

29. Eukaryotes? Prokaryotes are easy .. or are they
Look at AE000141
Eukaryotes are significantly more difficult
Due to exon-intron structure we need much more sequence to predict a gene
Sometimes more than a complete bacterial genome for just one human gene

30. Theoretical Part II
Evaluation of Gene Prediction

31. 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)
Sanja Rogic CS Department UBC
Computational Gene Finding

32. 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
Sanja Rogic CS Department UBC
Computational Gene Finding

33. Measures of Prediction Accuracy, Part 2
Exon level accuracy
WRONG EXON
CORRECT EXON
MISSING
EXON
REALITY
PREDICTION
Sanja Rogic CS Department UBC
Computational Gene Finding

34. Computational Gene Finding
Evaluation Results
Sanja Rogic CS Department UBC
Computational Gene Finding

35. Eukaryotes Prokaryotes are easy .. or are they
Look at AE000141
Eukaryotes are significantly more difficult
Due to exon-intron structure we need much more sequence to predict a gene
Sometimes more than a complete bacterial genome for just one human gene

36. Gene Finding Content Based Site Based Comparative Codon usage Start
Splice sites
Regulatory elements
Binding sites
Polyadenylation signals …
Comparative
Homology

37. Eukaryotic Gene Structure
Whereas control elements for bacterial promoters tend to be located nearby, eukaryotic control elements can be located up to 50 kb upstream or downstream of the gene. Can also be inside the gene.
While most Pol II genes have a TATAA box, some don't 37

38. Raw Nucleotide Sequences
Most sequences are raw nucleotide sequences
How do we know whether it is a gene?
There are certain measures which indicate that there may be a gene

39. Finding Genes http://rulai.cshl.org/tools/genefinder/human.htm
Get AF from gene bank
Enter the FASTA sequence and predict the gene
Double check with

40. More Gene Finding Tools
Large Collection
GeneScan
HMMgene
GeneBuilder

41. Gene Finding Fails
When similar genes have not been encountered before (e.g.: NTT, IPW)
When part of the signals are missing
When the “wrong” gene finding tool is used
When the gene is small and/or has many introns

42. Practical Part II

43. Finding Genes http://rulai.cshl.org/tools/genefinder/human.htm
Get AF from gene bank
Enter the FASTA sequence and predict the gene
Double check with

44. More Gene Finding Tools
GeneScan
HMMgene
Gene Prediction Software List

45. Comparison Use 4 tools to perform gene prediction
Store start and end positions for all exons in Excel for the 4 different results
Add the annotation from Genbank to the results
Scetch a plot showing the predictions on the genome
Compare the results

46. Result Evaluation Are number of exons different?
Are start and end positions shifted?
How much are they shifted?
Are there exons missing in some prediction?
How many?
Which result gives you the correct gene structure?