Biological MotivationGene Finding in Eukaryotic Genomes

Anne R. HaakeRhys Price Jones

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 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

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

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

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

Eukaryotic Gene Structure

www.bio.purdue.edu/courses/biol516/eukgenestructure.gif

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?

In prokaryotic genomes we usually start by looking for ORFs.

Is this a good approach for the eukaryotic genome? Why or why not?

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!)

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

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

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

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

http://www.lclark.edu/~lycan/Bio490/pptpresentations/mutation/sld016.htm

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

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”

Translation Signals

Kozak sequence The signal for initiation of translation

in vertebrates Consensus is GCCACCatgG

And of course.. Translation stop codons

Codon Biasin 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)

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)

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

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

List of Gene Finding Programs

http://www.hku.hk/bruhk/sggene.html

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

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

http://hto-13.usc.edu/software/procrustes/#salign

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)

http://genes.mit.edu/GENSCAN.html

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

GenScan

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

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.

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

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

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-exp

http://compbio.ornl.gov/grailexp/gxpfaq1.html