Eukaryotic Gene Finding Adapted in part from http://online.itp.ucsb.edu/online/infobio01/burge/

Prokaryotic vs. Eukaryotic Genes Prokaryotes small genomes high gene density no introns (or splicing) no RNA processing similar promoters overlapping genes Eukaryotes large genomes low gene density introns (splicing) RNA processing heterogeneous promoters polyadenylation

Pre-mRNA Splicing ... ... U 1 s n R N P 2 intronic repressor 5 ’ splice signal U 2 A F 6 5 3 1 s n R N P SR proteins intron definition exon definition exonic enhancers 5 ’ splice signal 3 polyY branch signal intronic enhancers exonic repressor ... (assembly of spliceosome, catalysis) ...

Some Statistics On average, a vertebrate gene is about 30KB long Coding region takes about 1KB Exon sizes can vary from double digit numbers to kilobases An average 5’ UTR is about 750 bp An average 3’UTR is about 450 bp but both can be much longer.

Human Splice Signal Motifs

Semi-Markov HMM Model

GHMM A finite Set Q of states Initial state distribution Π Transition probabilities Ti,j for Length distribution f of the states (fq is the length distribution of state q) Probability model for each state

GHMM – contin. A parse Ф of a sequence S of length L is an ordered sequence of states (q1, . . . , qt) with an associated duration di to each state The most probable pass Фopt can be computed as in Veterbi algorithm

Genscan HSMM

GenScan States N - intergenic region P - promoter F - 5’ untranslated region Esngl – single exon (intronless) (translation start -> stop codon) Einit – initial exon (translation start -> donor splice site) Ek – phase k internal exon (acceptor splice site -> donor splice site) Eterm – terminal exon (acceptor splice site -> stop codon) Ik – phase k intron: 0 – between codons; 1 – after the first base of a codon; 2 – after the second base of a codon

GenScan features Model both strands at once Each state may output a string of symbols (according to some probability distribution). Explicit intron/exon length modeling Advanced splice site modeling Complete intron/exon annotation for sequence Able to predict multiple genes and partial/whole genes Parameters learned from annotated genes Separate parameter training for different CpG content groups (< 43%, 43-51%, 51-57%,>57% CG content)

Various parameters in GENSCAN

GenScan Signal Modeling PSSM: P(S) = P1(S1)•P2(S2) •…•Pn(Sn) PolyA signal Translation initiation/termination signal Promoters WAM: P(S) = P1(S1) •P2(S2|S1)•…•Pn(Sn|Sn-1) 5’ and 3’ splice sites

GENSCAN Performance > 80% correct exon predictions, and > 90% correct coding/non coding predictions by bp. BUT - the ability to predict the whole gene correctly is much lower

HMM-based Gene Finding GENSCAN (Burge 1997) FGENESH (Solovyev 1997) HMMgene (Krogh 1997) GENIE (Kulp 1996) GENMARK (Borodovsky & McIninch 1993) VEIL (Henderson, Salzberg, & Fasman 1997)

Using Sequence Similarity for Gene Finding Compare genomic sequence with expressed sequence tags (ESTs) (e.g. by BLASTN), to identify regions corresponding to processed mRNA Compare genomic sequence to Protein DB (e.g. by BLASTX), to identify probably coding regions “Spliced Alignment” of genomic sequence of a complete gene with a homologous protein sequence (e.g. by PROCRUSTES) may enable exon/intron reconstruction Compare predicted peptides (e.g. by GENSCAN) with protein DB to assign confidence to predictions and functional annotations Compare Genomic sequence with homologous from close organisms/species (e.g. by BLAST, CLASTW), to identify conserved regions which might correspond to coding regions and DNA signals “Each of these methods can provide useful information about gene locations, as well as clues to gene function, although similarity based methods are currently (1998) able to identify only about hald of all human genes, and this proportion is increasing rather slowly. It should be kept in mind that similarity bsed mehtods are only as reliable as the DB that are searched, and apparent homology can be misleading at times…” (from Burge review, 98)

GenomeScan proteins are available. Idea: We can enhance our gene prediction by using external information: DNA regions with homology to known proteins are more likely to be coding exons. Combine probabilistic ‘extrinsic’ information (BLAST hits) with a probabilistic model of gene structure/composition (GenScan) Focus on ‘typical case’ when homologous but not identical proteins are available.

GeneWise [Birney, Amitai] Motivation: Use good DB of protein world (PFAM) to help us annotate genomic DNA GeneWise algorithm aligns a profile HMM directly to the DNA

Sample GeneWise Output

Developing GeneWise Model Start with a PFAM domain HMM Replace AA emissions with codon emissions Allow for sequencing errors (deletions/insertions) Add a 3-state intron model

GeneWise Model

GeneWise Intron Model PY tract central spacer 5’ site 3’ site

GeneWise Model Viterbi algorithm -> “best” alignment of DNA to protein domain Alignment gives exact exon-intron boundaries Parameters learned from species-specific statistics

GeneWise problems Only provides partial prediction, and only where the homology lies Does not find “more” genes Pseudogenes, Retrotransposons picked up CPU intensive Solution: Pre-filter with BLAST Retrotransposons are explained in p. 484-5 in Genetic Analysis, 5th edition: Basically parts of the genome which are copied multiple times into the DNA genome by means of reverese transcription (from mRNA back into the DNA). Good example is the ~200bp long human Alu sequnence, which we have hunderds of thousends of copies of, making ~5% of our genome. This is an example of SINES ( short interspersed elements). There are also LINES ( long interspersed elements), 1-5kb long, 20k-40k copies of them in the human genome. Elements of this class have ORFs that potentially code for enyzmes used in transposition. Many are the result of RNA virus that replicate through a DNA stage which can integrate into host chromosomes. Pseudogenes are explained in p.480 of the same book: basically these were once copies of a gene that stopped being functional (transcribed), and therefore is now under no selective pressure  it is still very similar to “real” genes, but with many more mutations.

Other Sequence Usage Search translated genomic sequences for the occurrences of the shot peptide motifs that are characteristic of common protein families (e.g. zinc finger, ATP/GTP binding modifs etc.) Identify sequences which are probably NON coding: identify known classes of interspersed repeates (e.g LINE SINE) in none coding regions. Can be essential to remove these before simple BLAST is done against EST’s.

Summary Genes are complex structures which are difficult to predict with the required level of accuracy/confidence Different approaches to gene finding: Ab Initio : GenScan Ab Initio modified by BLAST homologies: GenomeScan Homology guided: GeneWise

Future Directions Find genes not for proteins (tRNA, rRNA, smRNA) – hard ! Deal better with overlapping genes, multiple genes in a single sequence Alternative splicing/transcription/translation – a whole separate issue The mechanisms governing it, the signals predicting the various genes Very important !