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

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

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?

So, what’s the problem with looking for ORFs? “split” genes make it difficult to define ORFs Where are the translation starts 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 Example: _326/lectures/lect9_10/SpliceSite.htm _326/lectures/lect9_10/SpliceSite.htm

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

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 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) Complete set of codon usage biases can be found at:

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

Gene Finding Known gene(s)? Known gene High score, above the threshold? Low score Probability of being a gene. High cumulative scoreLow cumulative score Input DNA sequence (string of G’s, A’s, C’s, T’s) BLAST CpG Islands? Promoter ? ORF Signals ? Splice Sites ? No High score, above the threshold? Low scoreHigh score, above the threshold?

List of Gene Finding Programs tokyo.ac.jp/~katsu/genefinding/programs.htmlhttp:// tokyo.ac.jp/~katsu/genefinding/programs.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 13.usc.edu/software/procrustes/#salignhttp://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)

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

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 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 (uses annotated data from GenBank) -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 –identifies exon candidates (different types of exon) –other features such as 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

Glimmer for predicting genes in microbial genomes