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An introduction to gene prediction

An introduction to gene prediction. Introduction Prokaryotes Start/stop, operons Eukaryotes Start/stop promoter/polyA Intron/exons/UTR Problems Pseudogenes Alternative splicing RNA genes Repeats/CpG island. Methods HMMs Neural networks Compositional bias Syntheny Programs.

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An introduction to gene prediction

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  1. An introduction to gene prediction

  2. Introduction Prokaryotes Start/stop, operons Eukaryotes Start/stop promoter/polyA Intron/exons/UTR Problems Pseudogenes Alternative splicing RNA genes Repeats/CpG island Methods HMMs Neural networks Compositional bias Syntheny Programs Content

  3. Transcription start/stop -35 Region TATA box Translation start/stop ORFs Shine-Delgarno motif Start ATG/GTG Stop TAA/TAG/TGA Stem-loops Operons Few special cases Introns, inteins, slipering Signals in Prokaryotes

  4. Transcription Promoter/enhancer/silencer TATA box Introns/exons Donor/acceptor/branch polyA Repeats Alu, Satellites, Expansions CpG islands Cap/CCAAT&GC boxes Translation 5’ and 3’ UTR Kozak consensus Start ATG Stop TAA/TAG/TGA Signals in Eukaryotes

  5. Eukaryotes central dogma

  6. Promoters/enhancers/silencers

  7. Intron/exons splicing • Consensus • Donor • (A,C)AG/GT(A,G)AGT • Acceptor • TTTTTNCAG/GCCCCC • Branch • CT(G,A)A(C,T)

  8. Alternative splicing

  9. Pseudogenes • Promoters loss, stop codons, frameshifts • Translocation, duplication

  10. RNA genes and other problems • rRNA (ribosome) • tRNA (transfert) • snRNA (splicing) • tmRNA (telomerase) • Repeats (Alu, satellites, expansion etc…) • CpG islands

  11. Methods • Signals • Statistics (compositional bias) • HMMs • Neural networks • Homology/Syntheny

  12. Programs • See Lorenzo Cerutti presentation

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