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Gene Finding

Gene Finding. Transcription. Translation. Protein. RNA. “The Central Dogma”. Gene Finding in Prokaryotes. Reminder: The Genetic Code. 1 start, 3 stop Codons. Finding Genes in Prokaryotes. Gene structure. High gene density ~85% coding in E.coli => is every ORF a gene?. Finding ORFs.

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Gene Finding

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  1. Gene Finding

  2. Transcription Translation Protein RNA “The Central Dogma”

  3. Gene Finding in Prokaryotes

  4. Reminder: The Genetic Code 1 start, 3 stop Codons

  5. Finding Genes in Prokaryotes • Gene structure • High gene density • ~85% coding in E.coli • => is every ORF a gene?

  6. Finding ORFs • Many more ORFs than genes • In E.Coli one finds 6500 ORFs while there are 4290 genes. • In random DNA, one stop codon every 64/3=21 codons on average. • Average protein is ~300 codons long. • => search long ORFs. • Problems: • Short genes • Overlapping long ORFs on opposite strands

  7. Codon Frequencies • Coding DNA is not random: • In random DNA, expect Leu : Ala : Trp ratio of 6 : 4 : 1 • In real proteins, 6.9 : 6.5 : 1 • Different frequencies for different species.

  8. Human and Yeast codon usage

  9. Using Codon Frequencies/Usage • Assume each codon is independent. • For codon abc calculate frequency f(abc) in coding region. • Given coding sequence a1b1c1,…, an+1bn+1cn+1 • Calculate • The probability that the ith reading frame is the coding region:

  10. CodonPreference ORF The real genes

  11. C+G Content • C+G content (“isochore”) has strong effect on gene density, gene length etc. • < 43% C+G : 62% of genome, 34% of genes • >57% C+G : 3-5% of genome, 28% of genes • Gene density in C+G rich regions is 5 times higher than moderate C+G regions and 10 times higher than rich A+T regions • Amount of intronic DNA is 3 times higher for A+T rich regions. (Both intron length and number). • Etc…

  12. CodonPreference: 3rd position GC bias

  13. RNA Transcription • Not all ORFs are expressed. • Transcription depends on regulatory regions. • Common regulatory region – the promoter • RNA polymerase binds tightly to a specific DNA sequence in the promoter called the binding site.

  14. Prokaryotic Promoter • One type of RNA polymerase.

  15. Positional Weight Matrix • For TATA box:

  16. Gene Finding in Eukaryotes

  17. Coding density

  18. Eukaryote gene structure • Gene length: 30kb, coding region: 1-2kb • Binding site: ~6bp; ~30bp upstream of TSS • Average of 6 exons, 150bp long • Huge variance: - dystrophin: 2.4Mb long • Blood coagulation factor: 26 exons, 69bp to 3106bp; intron 22 contains another unrelated gene

  19. Splicing • Splicing: the removal of the introns. • Performed by complexes called spliceosomes, containing both proteins and snRNA. • The snRNA recognizes the splice sites through RNA-RNA base-pairing • Recognition must be precise: a 1nt error can shift the reading frame making nonsense of its message. • Many genes have alternative splicing which changes the protein created.

  20. Splice Sites

  21. Frame +1 Frame+2 Frame +3 Gene prediction programs Scan the sequence in all 6 reading frames: • Start and stop codons • Long ORF • Codon usage • GC content • Gene features: promotor, terminator, poly A sites, exons and introns, …

  22. Gene prediction programs Genscan: • Vertebrates/Maize/Arbidopsis. • Predict location and gene features. • Can handle few genes in one sequence

  23. Gene prediction programs • Results:

  24. GenScan Output

  25. GenScan Performance • Predicts correctly 80% of exons • With multiple exons probability declines… • Prediction per bp > 90%

  26. Many prediction Tools • Dynamic programming to make the high scoring model from available features. • e.g. Genefinder • Markov model based on a typical gene model. • e.g. GENSCAN or GLIMMER • Neural net trained with confirmed gene models. • e.g. GRAIL

  27. An end to ab initio prediction • ab initio gene prediction is inaccurate. • High false positive rates for most predictors. • Rarely used as a final product • Human annotation runs multiple algorithms and scores exon predicted by multiple predictors. • Used as a starting point for refinement/verification

  28. Comparative Genomics Use homologue sequences: • Annotated genes. • mRNA sequences. • Proteins sequences • ESTs

  29. ESTs EST – Expressed Sequence Tags. Short sequences which are obtained from cDNA (mRNA).

  30. Gene Model: EST cDNA Transcript-based prediction Align transcript data to genomic sequence using a pair-wise sequence comparison.

  31. Transcript-based prediction Example: BlastN against a ESTs/human database.

  32. Annotation of eukaryotic genomes Genomic DNA ab initio gene prediction(w/o prior knowledge) transcription Unprocessed RNA RNA processing Comparative gene prediction (use other biological data) AAAAAAA Gm3 Mature mRNA translation Nascent polypeptide folding Active enzyme Functional identification Function Reactant A Product B

  33. FIN

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