3 Genome Annotation Gene Prediction II Gene Prediction

3. Genome Annotation: Gene Prediction (II)

Gene Prediction: Computational Challenge • Gene: A sequence of nucleotides coding for protein • Gene Prediction Problem: Determine the beginning and end positions of genes in a genome

Eukaryotic gene finding • On average, vertebrate gene is about 30 KB long • Coding region takes about 1 KB • Exon sizes 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.

Exons and Introns • In eukaryotes, the gene is a combination of coding segments (exons) that are interrupted by non-coding segments (introns) • This makes computational gene prediction in eukaryotes even more difficult • Prokaryotes don’t have introns - Genes in prokaryotes are continuous

Central Dogma and Splicing exon 1 intron 1 exon 2 intron 2 exon 3 transcription splicing exon = coding intron = non-coding translation

Gene Structure

Splicing Signals Exons are interspersed with introns and typically flanked by GT and AG

Splice site detection 5’ Donor site Position % 3’

Consensus splice sites Donor: 7. 9 bits Acceptor: 9. 4 bits

Promoters • Promoters are DNA segments upstream of transcripts that initiate transcription Promoter 5’ 3’ • Promoter attracts RNA Polymerase to the transcription start site

Splicing mechanism (http: //genes. mit. edu/chris/)

Splicing mechanism • Adenine recognition site marks intron • sn. RNPs bind around adenine recognition site • The spliceosome thus forms • Spliceosome excises introns in the m. RNA

Two Approaches to Eukaryotic Gene Prediction • Statistical: coding segments (exons) have typical sequences on either end and use different subwords than non-coding segments (introns). • Similarity-based: many human genes are similar to genes in mice, chicken, or even bacteria. Therefore, already known mouse, chicken, and bacterial genes may help to find human genes.

Similarity-Based Approach: Metaphor in Different Languages If you could compare the day’s news in English, side-by-side to the same news in a foreign language, some similarities may become apparent

Distinguishing genes from non-coding regions Splice Dmel Dsec Dsim Dyak Dere Dana Dpse Dper Dwil Dmoj Dvir Dgri TGTTCATAAATAAA-----TTTACAACAGTTAGCTG-GTTAGCCAGGCGGAGTGTCTGCGCCCATTACCGTGCGGACGAGCATGT---GGCTCCAGCATCTTC TGTCCATAAA-----TTTACAACAGTTAGCTG-GTTAGCCAGGCGGAGTGTCTGCGCCCATTACCGTGCGGACGAGCATGT---GGCTCCAGCATCTTC TGTCCATAAA-----TTTACAACAGTTAGCTG-GTTAGCCAGGCGGAGTGCCTTCTACCATTACCGTGCGGACGAGCATGT---GGCTCCAGCATCTTC TGTCCATAAA-----TTTACAACAGTTAGCTG-CTTAGCCATGCGGAGTGCCTCCTGCCATTGCCGTGCGGGCGAGCATGT---GGCTCCAGCATCTTT TGTCCATAAA-----TCTACAACATTTAGCTG-GTTAGCCAGGCGGAGTGTCTGCGACCGTTCATG------CGGCCGTGA---GGCTCCATCATCTTA TGTCCATAAATGAA-----TTTACAACATTTAGCTG-CTTAGCCAGGCGGAATGGCGCCGTTCCCGTGCATACGCCCGTGG---GGCTCCATCATTTTC TGTCCATAAATGAA-----TTTACAACATTTAGCTG-CTTAGCCAGGCGGAATGCCGCCGTTCCCGTGCATACGCCCGTGG---GGCTCCATTATTTTC TGTTCATAAATGAA-----TTTACAACACTTAACTGAGTTAGCCAAGCCGAGTGCCGCCGGCCATTAGTATGCAAACGACCATGG---GGTTCCATTATCTTC TGATTATAAACGTAATGCTTTTATAACAATTAGCTG-GTTAGCCAAGCCGAGTGGCGCC------TGCCGTGCGTACGCCCCTGTCCCGGCTCCATCAGCTTT TGTTTATAAAATTCTTTTAAAACAATTAGCTG-GTTAGCCAGGCGGAATGGCGCC------GTCCGTGCGGCTCTGGCCCGGCTCCATCAGCTTC TGTCTATAAAAATAATTCTTTTATGACACTTAACTG-ATTAGCCAGGCAGAGTGTCGCC------TGCCATGGGCACGACCCTGGCCGGGTTCCATCAGCTTT ***** * * ** *** ******* ** ** **** * ** • Protein-coding genes have specific evolutionary constraints – – Gaps are multiples of three (preserve amino acid translation) Mutations are largely 3 -periodic (silent codon substitutions) Specific triplets exchanged more frequently (conservative substs. ) Conservation boundaries are sharp (pinpoint individual splicing signals) • Encode as ‘evolutionary signatures’ – Computational test for each of them – Combine and score systematically

Signature 1: Reading frame conservation RFC 100% 60% 100% 55% 100% 90% 100% 40% 100% 60% 100% 20% 100% 30% 100% 40% 100% 60% Mutations Gaps Frameshifts Genes Intergenic 30% 1. 3% 0. 14% 58% 14% 10. 2% Separation 2 -fold 10 -fold 75 -fold

Results in yeast ~4000 named genes ~300 intergenic regions Accept Reject 99. 9% 0. 1% 1% 99%

Signature 2: Distinct patterns of codon substitution Genes Codon observed in species 2 Codon observed in species 1 Codon observed in species 2 • Codon substitution patterns specific to genes – Genetic code dictates substitution patterns – Amino acid properties dictate substitution patterns Intergenic

human Codon Substitution Matrix (CSM) mouse aliphatic aromatic polar negative positive

Gene structure in eukaryotes exons Final exon Initial exon Transcribed region start codon stop codon 3’ 5’ GT Promoter AG Untranslated regions Transcription stop side Transcription start side donor and acceptor sides

Gene Prediction and Motifs • Upstream regions of genes often contain motifs that can be used for gene prediction ATG -35 -10 0 TTCCAA TATACT Pribnow Box 10 GGAGG Ribosomal binding site Transcription start site STOP

Ribosomal Binding Site

Splicing Signals • Try to recognize location of splicing signals at exon-intron junctions – This has yielded a weakly conserved donor splice site and acceptor splice site • Profiles for sites are still weak, and lends the problem to the Hidden Markov Model (HMM) approaches, which capture the statistical dependencies between sites

Gen. Scan Model • States- correspond to different functional units of a genome (promoter region, intron, exon, …. ) • The states for introns and exons are subdivided according to “phase” three frames. • There are two symmetric sub modules forward and backward strands. Performance: 80% exon detecting (but if a gene has more than one exon probability of detection decrease rapidly.

Donor and Acceptor Sites: GT and AG dinucleotides • The beginning and end of exons are signaled by donor and acceptor sites that usually have GT and AC dinucleotides • Detecting these sites is difficult, because GT and AC appear very often Donor Acceptor Site GT exon 1 Site AC exon 2

Donor and Acceptor Sites: Motif Logos Donor: 7. 9 bits Acceptor: 9. 4 bits (Stephens & Schneider, 1996) (http: //www-lmmb. ncifcrf. gov/~toms/sequencelogo. html)

Popular Gene Prediction Algorithms • GENSCAN: uses Hidden Markov Models (HMMs) • TWINSCAN – Uses both HMM and similarity (e. g. , between human and mouse genomes)

Similarity-based gene finding • Alignment of – Genomic sequence and (assembled) EST sequences – Genomic sequence and known (similar) protein sequences – Two or more similar genomic sequences

Expressed Sequence Tags Cell or tissue Isolate m. RNA and Reverse transcribe into c. DNA db. EST Clone c. DNA into a vector to Make a c. DNA library Vectors Submit To db. EST 5’ EST 3’ Pick a clone And sequence the 5’ and 3’ Ends of c. DNA insert

Central Dogma and Splicing exon 1 intron 1 exon 2 intron 2 exon 3 transcription splicing exon = coding intron = non-coding translation

Splicing Sequence Alignment Potential splicing sites

Comparing Genomic DNA Against intron 1 exon 2 intron 2 Portion of genome { { { EST (codon sequence) exon 1 exon 3

Using Similarities to Find the Exon Structure • Human EST (m. RNA) sequence is aligned to different locations in the human genome • Find the “best” path to reveal the exon structure of human gene EST sequence Human Genome

Spliced Alignment Problem: Formulation • Goal: Find a chain of blocks in a genomic sequence that best fits a target sequence • Input: Genomic sequences G, target sequence T, and a set of candidate exons B. • Output: A chain of exons Γ such that the global alignment score between Γ* and T is maximum among all chains of blocks from B. Γ* - concatenation of all exons from chain Γ

Lewis Carroll Example

Spliced Alignment: Speedup

Spliced Alignment: Speedup

Spliced Alignment: Speedup P(i, j)=maxall blocks B preceding position i S(end(B), j, B)

EST_genome • http: //www. well. ox. ac. uk/~rmott/ESTGEN OME/est_genome. shtml

Gene finding based on multiple genomes • Twinscan • Phylo. HMM