1. Biological Motivation Gene Finding in Eukaryotic Genomes
Anne R. Haake
Rhys Price Jones

2. Recall from our previous discussion of gene finding in prokaryotes:
The major strategies in gene finding programs are to look for:
Signals/Features
Content/Composition
Similarity to known genes (BLAST!)

3. 3 Major Categories of Information used in Gene Finding Programs
Signals/features = a sequence pattern with functional significance e.g. splice donor & acceptor sites, start and stop codons, promoter features such as TATA boxes, TF binding sites
Content/composition -statistical properties of coding vs. non-coding regions.
e.g. codon-bias; length of ORFs in prokaryotes; CpG islands GC content
Similarity-compare DNA sequence to known sequences in database
Not only known proteins but also ESTs, cDNAs
Similarity: -involves translation in all 6 possible reading frames. Need to discuss repetitive sequences & how to handle them. Can also use database information to aid in the prediction –not only known proteins but also ESTs, cDNAs (explain these).

4. In Prokaryotic Genomes
We usually start by looking for an ORF
A start codon, followed by (usually) at least 60 amino acid codons before a stop codon occurs
Or by searching for similarity to a known ORF
Look for basal signals
Transcription (the promoter consensus and the termination consensus)
Translation (ribosome binding site: the Shine-Dalgarno sequence)
Look for differences in sequence content between coding and non-coding DNA
GC content and codon bias
Yeast, ~1% of genes have ORFs<100 aa

5. The Complicating factors in Eukaryotes
Interrupted genes (split genes)
introns and exons
Large genomes
Most DNA is non-coding
introns, regulatory regions, “junk” DNA (unknown function)
About 3% coding
Complex regulation of gene expression
Regulatory sequences may be far away from start codon

6. Some numbers to consider:
Vertebrate genes average about 30Kb long
varies a lot
Coding region is only about 1-2 Kb
Exon sizes and numbers vary a lot
Average is 6 exons, each about 150 bp long
An average 5’ UTR is about 750 bp
An average 3’UTR is about 450 bp
(both can be much longer)
There are huge deviations from all of these numbers
e.g. dystrophin is 2.4 Mb long ; factor VIII gene has 26 exons, introns are up to 32 Kb (one intron produces 2 transcripts unrelated to the gene!)
There are genes without introns: called single-exon or intronless genes

7. Eukaryotic Gene Structure

8. Given a long eukaryotic DNA sequence:
How would you determine if it had a gene?
How would you determine which substrings of the sequence contained protein-coding regions?

9. In prokaryotic genomes we usually start by looking for ORFs.
Is this a good approach for the eukaryotic genome?
Why or why not?

10. So, what’s the problem with looking for ORFs?
“split” genes make it difficult to define ORFs
Where are the stops and stops?
What problems do introns introduce?
What would you predict for the size of ORFs?
(you can’t with any certainty!)

11. Most Programs Concentrate on Finding Exons
Exon: the region of DNA within a gene that codes for a polypeptide chain or domain
Intron: non-coding sequences found in the structural genes
Typically a mature protein is composed of several domains coded by different exons within a single gene.

12. Splice Sites used to Define Exons
Splice donor (exon-intron boundary) and splice acceptor (intron-exon boundary)
are consensus sequences
A statistical determination of the pattern;approximates the pattern
C(orA)AG/GTA(orG)AGT "donor" splice site
T(orC)nNC(orT)AG/G "acceptor" splice site
These sequences in eucaryotic organisms, ranging from yeasts to mammals, have common sequence motifs with introns beginnig with 5'-GU and ending with 3'-AG. The consensus 5' splice site of vertebrate introns is AGGUAAGU, while the consensus at the 3' splice site is a stretch of pyrimidines (U or C), followed by any base, then by C, and ending with the invariant AG
To read more about splicing check out: Or

13. Gene finding programs look for different types of exon
single exon genes: begin with start codon & end with stop codon
initial exons: begin with start codon & end with donor site
internal exons: begin with acceptor & end with donor
terminal exons: begin with acceptor & end with stop codon

14. How are correct splice sites identified?
There are many occurrences of GT or AG within introns that are not splice sites
Statistical profiles of splice sites are used

15. Other Biologically Important Signals Used in Gene Finding Programs
Transcriptional Signals
Transcription Start: characterized by cap signal
A single purine (A/G)
TATA box (promoter) at –25 relative to start
Polyadenylation signal: AATAAA (3’ end)
Major Caveat: not all genes have these signals
Makes it difficult to define the beginning and end of a gene

16. Upstream Promoter Sites
Transcription Factor (TF) sites
Transcription factors are sequence-specific DNA-binding proteins
Bind to consensus DNA sequences
e.g. CAAT transcription factor and CAAT box
Many of these
Vary in sequence, location, interaction with other sites
Further complicates the problem of delineating a “gene”

17. Translation Signals Kozak sequence And of course..
The signal for initiation of translation in vertebrates
Consensus is GCCACCatgG
And of course..
Translation stop codons

18. Codon Bias in Eukaryotic Genomes
Yeast Genome: arg specified by AGA 48% of time (other five equivalent codons ~10% each)
Fruitfly Genome: arg specified by CGC 33% of time (other five ~13% each)

19. GC Content in Eukaryotes
Overall GC content does not vary between species as it does in prokaryotes
GC content is still important in gene finding algorithms
CpG Islands
CG dinucleotides occur at low frequency overall in the genome
Exception: CpG islands near promoters
CG dinucleotides occur at level predicted by chance
-1,500 to +500 (relative to transcription start site)

20. CpG Islands Occurrence related to methylation
Methylation of C in CG dinucleotides
Methylation of C makes CpG prone to mutation (e.g. to TpG or CpA)
Level of methylation is low in actively transcribed genes
Transcription requires a methyl-free promoter

21. Gene Finding Strategies
Homology-based approach
Find sequences that are similar to known gene sequences
ab initio-based approach is to identify genes by:
Signal sequences
Composition

22. List of Gene Finding Programs

23. Homology-Based Approaches in Eukaryotic Genomes
More complicated than prokaryotes due to split genes
Genome sequence -> first identify all candidate exons
Use a spliced alignment algorithm to explore all possible exon assemblies & compare to known
e.g. Procrustes
Limitations:
must have similar sequence in the database with known exon structure
Sensitive to frame shift errors

24. Procrustes Gene Recognition via spliced alignment
Given a genomic sequence and a set of candidate exons, the spliced alignment algorithm explores all possible exon assemblies and finds a chain of exons with the best fit to a related target protein

25. GenScan Allows integration of multiple types of information
Earlier programs considered features of gene structure in isolation
Uses a generalized HMM (one state might use a weight matrix model, another an HMM)
GenScan efficient only with human DNA because uses human genes as training set for the HMM
The states correspond to the different functional units on a gene
The transitions between the states ensures that the order is biologically consistent. E.g. promoter must move to the state corresponding to the 5’UTR.

26. GenScan Probabilistic Model of Genes
Accounts for many of the known structural & compositional properties of genes including:
typical gene density
typical number of exons per gene
distribution of exon sizes for different types of exon
compositional properties of coding vs. non-coding
translation initiation (Kozak)
termination signals
TATA box, cap site and poly-adenylation signals
donor and acceptor splice sites

27. GenScan
Uses as a training set 238 multi-exon genes and 142 single-exon genes from GenBank to compute parameters
Initial state probabilities
Transition probabilities
State length distributions

28. GenScan Probabilistic models for the states
The states correspond to different functional units on a gene e.g promoter regions, exon
Transitions ensure that the order that the model marches through the states is biologically consistent
Length distributions take into account that different functional units have different lengths.

29. GenScan Signal models used by GenScan - WMM= weight matrix model
for transcriptional and translational signals (translation initiation, polyadenylation signals, TATA box etc.) e.g. polyadenylation signal is modeled as a 6 bp WMM with AATAAA as the consensus sequence
-WAM= weight array model; assumes some dependencies between adjacent positions in the sequence
e.g. used for the pyrimidine-rich region and the splice acceptor site
-Maximal dependency decomposition
e.g. used for donor splice sites

30. GenScan does not use similarity search
uses double stranded genomic sequence model
potential genes on both strands are analysed simultaneously
Limitations:
cannot handle overlapping transcription unit
does not address alternative splicing

31. GRAIL GRAIL (Gene Recognition and Assembly Internet Link)
uses a number of sensor algorithms to evaluate coding potential of a DNA sequence
features include 6-mer composition, GC composition and splice junction recognition
the output of the sensor algorithms is input to a neural network, which uses empirical data for training.
GRAIL provides analysis of protein coding potential of a DNA sequence. GRAIL uses variable-length windows tailored to each potential exon candidate, defined as an open reading frame bounded by a pair of start/donor, acceptor/donor or acceptor/stop sites. This scheme facilitates the use of more genomic context information (splice junctions, translation starts, non-coding scores of 60-base regions on either side of a putative exon) in the exon recognition process. GRAIL finds about 91% of all coding regions with an apparent false positive rate of 8.6%.

32. GRAIL-exp